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2023-029
Commit 39909344 authored by Gamage B.G.J's avatar Gamage B.G.J
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Merge branch 'dev' into 'master' 

Dev

See merge request !5
parents b67a974b 57a9efd8
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Project/Backend/ML_Models/Learning_Model/.gitignore 0 → 100644
View file @ 39909344
models/*
!models/
DataSet/Sn_sign_language_dataset/
Project/Backend/ML_Models/Learning_Model/.ipynb_checkpoints/API_Calculate_Similarity_Score-1-checkpoint.ipynb 0 → 100644
View file @ 39909344
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "f47e929b",
"metadata": {},
"outputs": [],
"source": [
"import nest_asyncio\n",
"import asyncio\n",
"import torch.nn.functional as F\n",
"import torch.nn as nn\n",
"import torchvision.transforms as transforms\n",
"import numpy as np\n",
"import io\n",
"import uvicorn\n",
"from fastapi import FastAPI, UploadFile\n",
"from PIL import Image\n",
"import torch"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "13b1d58b",
"metadata": {},
"outputs": [],
"source": [
"nest_asyncio.apply()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "35f4adc0",
"metadata": {},
"outputs": [],
"source": [
"app = FastAPI()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "a5aba4be",
"metadata": {},
"outputs": [],
"source": [
"transform = transforms.Compose([\n",
" transforms.Resize((300, 300)),\n",
" transforms.Grayscale(num_output_channels=1),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize(mean=(0.5), std=(0.5))\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "11ec2fae",
"metadata": {},
"outputs": [],
"source": [
"class theCNN(nn.Module):\n",
" def __init__(self):\n",
" super(theCNN, self).__init__()\n",
" \n",
" self.conv01 = nn.Conv2d(\n",
" in_channels=1,\n",
" out_channels=10,\n",
" kernel_size=5,\n",
" stride=1,\n",
" padding=1\n",
" )\n",
" \n",
" self.conv02 = nn.Conv2d(\n",
" in_channels=10,\n",
" out_channels=20,\n",
" kernel_size=5,\n",
" stride=1,\n",
" padding=1\n",
" )\n",
" \n",
" expectedSize = int(np.floor((73 + 2 * 0 - 1) / 1) + 1)\n",
" expectedSize = 20 * int(expectedSize ** 2)\n",
" \n",
" self.fc01 = nn.Linear(expectedSize, 50)\n",
" self.output = nn.Linear(50, 16)\n",
"\n",
" def forward(self, x):\n",
" x = F.relu(F.max_pool2d(self.conv01(x), 2))\n",
" x = F.relu(F.max_pool2d(self.conv02(x), 2))\n",
" nUnits = x.shape.numel() / x.shape[0]\n",
" x = x.view(-1, int(nUnits))\n",
" x = F.relu(self.fc01(x))\n",
" return torch.softmax(self.output(x), axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "353a4725",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"theCNN(\n",
" (conv01): Conv2d(1, 10, kernel_size=(5, 5), stride=(1, 1), padding=(1, 1))\n",
" (conv02): Conv2d(10, 20, kernel_size=(5, 5), stride=(1, 1), padding=(1, 1))\n",
" (fc01): Linear(in_features=106580, out_features=50, bias=True)\n",
" (output): Linear(in_features=50, out_features=16, bias=True)\n",
")"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = theCNN()\n",
"model.load_state_dict(torch.load(\"model.pth\"))\n",
"model.eval()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "bb87b7f0",
"metadata": {},
"outputs": [],
"source": [
"@app.post(\"/score\")\n",
"async def calculate_score(image_file: UploadFile):\n",
" image = Image.open(io.BytesIO(await image_file.read())).convert(\"L\")\n",
" image = transform(image).unsqueeze(0)\n",
"\n",
" with torch.no_grad():\n",
" output = model(image)\n",
"\n",
" probabilities = torch.softmax(output, dim=1)[0]\n",
" predicted_class = torch.argmax(probabilities).item()\n",
"\n",
" # Get the actual number corresponding to the hand sign\n",
" actual_number = get_actual_number_from_image(image)\n",
" \n",
" print(actual_number)\n",
"\n",
" # Compare predicted class with actual number and calculate correctness percentage\n",
" correct = int(predicted_class + 1 == actual_number)\n",
" print(correct)\n",
" correctness_percentage = correct / 1.0 * 100.0\n",
"\n",
" return {\"predicted_class\": predicted_class, \"correctness_percentage\": correctness_percentage}"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "05646e93",
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"import numpy as np\n",
"\n",
"def get_actual_number_from_image(image):\n",
" # Convert the image to numpy array\n",
" image_array = np.array(image)\n",
"\n",
" # Apply image processing techniques to detect and recognize digits\n",
" # Example steps: thresholding, contour detection, character segmentation, digit recognition\n",
"\n",
" # Apply thresholding\n",
" _, binary_image = cv2.threshold(image_array, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\n",
"\n",
" # Find contours\n",
" contours, _ = cv2.findContours(binary_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
"\n",
" # Sort contours based on their x-coordinate\n",
" contours = sorted(contours, key=lambda cnt: cv2.boundingRect(cnt)[0])\n",
"\n",
" # Initialize the recognized digit sequence\n",
" digit_sequence = \"\"\n",
"\n",
" # Iterate over the contours and recognize digits\n",
" for contour in contours:\n",
" # Get the bounding box of the contour\n",
" x, y, w, h = cv2.boundingRect(contour)\n",
"\n",
" # Crop the digit region from the image\n",
" digit_image = binary_image[y:y + h, x:x + w]\n",
"\n",
" # Resize the digit image to a fixed size (e.g., 28x28)\n",
" resized_digit_image = cv2.resize(digit_image, (28, 28))\n",
"\n",
" # Preprocess the resized digit image (e.g., normalize pixel values)\n",
" preprocessed_digit_image = resized_digit_image / 255.0\n",
"\n",
" # Flatten the preprocessed digit image\n",
" flattened_digit_image = preprocessed_digit_image.flatten()\n",
"\n",
" # Pass the flattened digit image to your digit recognition model\n",
" # to get the predicted digit (e.g., using a separate model or the same model you used for training)\n",
"\n",
" # Here, let's assume you have a function `predict_digit` that takes the flattened digit image\n",
" # and returns the predicted digit as an integer\n",
" predicted_digit = predict_digit(flattened_digit_image)\n",
"\n",
" # Add the predicted digit to the digit sequence\n",
" digit_sequence += str(predicted_digit)\n",
"\n",
" # Convert the digit sequence to an integer\n",
" actual_number = int(digit_sequence)\n",
"\n",
" return actual_number\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ee993fc1",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO: Started server process [36312]\n",
"INFO: Waiting for application startup.\n",
"INFO: Application startup complete.\n",
"INFO: Uvicorn running on http://127.0.0.1:8001 (Press CTRL+C to quit)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"5\n",
"0\n",
"INFO: 127.0.0.1:60625 - \"POST /score HTTP/1.1\" 200 OK\n"
]
}
],
"source": [
"if __name__ == \"__main__\":\n",
" loop = asyncio.get_event_loop()\n",
" loop.create_task(uvicorn.run(app, host=\"127.0.0.1\", port=8001))\n",
" loop.run_forever()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "34c5efea",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Project/Backend/ML_Models/Learning_Model/.ipynb_checkpoints/API_Calculate_Similarity_Score-2-checkpoint.ipynb 0 → 100644
View file @ 39909344
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "c898b57c",
"metadata": {},
"outputs": [],
"source": [
"pip install python-multipart"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "a99b4156",
"metadata": {},
"outputs": [],
"source": [
"import nest_asyncio\n",
"import asyncio\n",
"import torch.nn.functional as F\n",
"\n",
"# Apply the patch\n",
"nest_asyncio.apply()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "01b6e57c",
"metadata": {},
"outputs": [],
"source": [
"from fastapi import FastAPI, UploadFile\n",
"from PIL import Image\n",
"import torch\n",
"import torchvision.transforms as transforms\n",
"import numpy as np\n",
"import io\n",
"import torch.nn as nn\n",
"import asyncio\n",
"import uvicorn "
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "2e561f13",
"metadata": {},
"outputs": [],
"source": [
"app = FastAPI()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "1e9e4208",
"metadata": {},
"outputs": [],
"source": [
"transform = transforms.Compose([\n",
" transforms.Resize((300, 300)),\n",
" transforms.Grayscale(num_output_channels=1),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize(mean=(0.5), std=(0.5))\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "4d86d2b8",
"metadata": {},
"outputs": [],
"source": [
"class theCNN(nn.Module):\n",
" def __init__(self):\n",
" super(theCNN, self).__init__()\n",
" \n",
" self.conv01 = nn.Conv2d(\n",
" in_channels=1,\n",
" out_channels=10,\n",
" kernel_size=5,\n",
" stride=1,\n",
" padding=1\n",
" )\n",
" \n",
" self.conv02 = nn.Conv2d(\n",
" in_channels=10,\n",
" out_channels=20,\n",
" kernel_size=5,\n",
" stride=1,\n",
" padding=1\n",
" )\n",
" \n",
" expectedSize = int(np.floor((73 + 2 * 0 - 1) / 1) + 1)\n",
" expectedSize = 20 * int(expectedSize ** 2)\n",
" \n",
" self.fc01 = nn.Linear(expectedSize, 50)\n",
" self.output = nn.Linear(50, 16)\n",
"\n",
" def forward(self, x):\n",
" x = F.relu(F.max_pool2d(self.conv01(x), 2))\n",
" x = F.relu(F.max_pool2d(self.conv02(x), 2))\n",
" nUnits = x.shape.numel() / x.shape[0]\n",
" x = x.view(-1, int(nUnits))\n",
" x = F.relu(self.fc01(x))\n",
" return torch.softmax(self.output(x), axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "9379bf73",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "Expected state_dict to be dict-like, got <class '__main__.theCNN'>.",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[6], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m model \u001b[38;5;241m=\u001b[39m theCNN()\n\u001b[1;32m----> 2\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_state_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmodel1.pth\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3\u001b[0m model\u001b[38;5;241m.\u001b[39meval()\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\torch\\nn\\modules\\module.py:1994\u001b[0m, in \u001b[0;36mModule.load_state_dict\u001b[1;34m(self, state_dict, strict)\u001b[0m\n\u001b[0;32m 1971\u001b[0m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Copies parameters and buffers from :attr:`state_dict` into\u001b[39;00m\n\u001b[0;32m 1972\u001b[0m \u001b[38;5;124;03mthis module and its descendants. If :attr:`strict` is ``True``, then\u001b[39;00m\n\u001b[0;32m 1973\u001b[0m \u001b[38;5;124;03mthe keys of :attr:`state_dict` must exactly match the keys returned\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1991\u001b[0m \u001b[38;5;124;03m ``RuntimeError``.\u001b[39;00m\n\u001b[0;32m 1992\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 1993\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(state_dict, Mapping):\n\u001b[1;32m-> 1994\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected state_dict to be dict-like, got \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mtype\u001b[39m(state_dict)))\n\u001b[0;32m 1996\u001b[0m missing_keys: List[\u001b[38;5;28mstr\u001b[39m] \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m 1997\u001b[0m unexpected_keys: List[\u001b[38;5;28mstr\u001b[39m] \u001b[38;5;241m=\u001b[39m []\n",
"\u001b[1;31mTypeError\u001b[0m: Expected state_dict to be dict-like, got <class '__main__.theCNN'>."
]
}
],
"source": [
"model = theCNN()\n",
"model.load_state_dict(torch.load(\"model.pth\"))\n",
"model.eval()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "18b869d3",
"metadata": {},
"outputs": [],
"source": [
"@app.post(\"/score\")\n",
"async def calculate_score(image_file: UploadFile):\n",
" image = Image.open(io.BytesIO(await image_file.read())).convert(\"L\")\n",
" image = transform(image).unsqueeze(0)\n",
"\n",
" with torch.no_grad():\n",
" output = model(image)\n",
"\n",
" probabilities = torch.softmax(output, dim=1)[0]\n",
" similarity_scores = probabilities.numpy()\n",
"\n",
" return {\"similarity_scores\": similarity_scores.tolist()}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4a99a8b6",
"metadata": {},
"outputs": [],
"source": [
"@app.get(\"/\")\n",
"async def hello_world(): \n",
"\n",
" return {\"Hello World\"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7b30f5c6",
"metadata": {},
"outputs": [],
"source": [
"if __name__ == \"__main__\":\n",
" loop = asyncio.get_event_loop()\n",
" loop.create_task(uvicorn.run(app, host=\"127.0.0.1\", port=8001))\n",
" loop.run_forever()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3e2e07d8",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Project/Backend/ML_Models/Learning_Model/.ipynb_checkpoints/API_Calculate_Similarity_Score-3-checkpoint.ipynb 0 → 100644
View file @ 39909344
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "60c58fd2",
"metadata": {},
"outputs": [],
"source": [
"import nest_asyncio\n",
"import asyncio\n",
"import torch.nn.functional as F\n",
"\n",
"# Apply the patch\n",
"nest_asyncio.apply()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "c1b7af91",
"metadata": {},
"outputs": [],
"source": [
"from fastapi import FastAPI, UploadFile\n",
"from PIL import Image\n",
"import torch\n",
"import torchvision.transforms as transforms\n",
"import numpy as np\n",
"import io\n",
"import torch.nn as nn\n",
"import asyncio\n",
"import uvicorn \n",
"from io import BytesIO"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "7f1cd8a4",
"metadata": {},
"outputs": [],
"source": [
"app = FastAPI()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "2035acf5",
"metadata": {},
"outputs": [],
"source": [
"transform = transforms.Compose([\n",
" transforms.Resize((300, 300)),\n",
" transforms.Grayscale(num_output_channels=1),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize(mean=(0.5), std=(0.5))\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "2cfca47d",
"metadata": {},
"outputs": [],
"source": [
"class theCNN(nn.Module):\n",
" def __init__(self):\n",
" super(theCNN, self).__init__()\n",
" \n",
" self.conv01 = nn.Conv2d(\n",
" in_channels=1,\n",
" out_channels=10,\n",
" kernel_size=5,\n",
" stride=1,\n",
" padding=1\n",
" )\n",
" \n",
" self.conv02 = nn.Conv2d(\n",
" in_channels=10,\n",
" out_channels=20,\n",
" kernel_size=5,\n",
" stride=1,\n",
" padding=1\n",
" )\n",
" \n",
" expectedSize = int(np.floor((73 + 2 * 0 - 1) / 1) + 1)\n",
" expectedSize = 20 * int(expectedSize ** 2)\n",
" \n",
" self.fc01 = nn.Linear(expectedSize, 50)\n",
" self.output = nn.Linear(50, 16)\n",
"\n",
" def forward(self, x):\n",
" x = F.relu(F.max_pool2d(self.conv01(x), 2))\n",
" x = F.relu(F.max_pool2d(self.conv02(x), 2))\n",
" nUnits = x.shape.numel() / x.shape[0]\n",
" x = x.view(-1, int(nUnits))\n",
" x = F.relu(self.fc01(x))\n",
" return torch.softmax(self.output(x), axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "2786c4df",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"theCNN(\n",
" (conv01): Conv2d(1, 10, kernel_size=(5, 5), stride=(1, 1), padding=(1, 1))\n",
" (conv02): Conv2d(10, 20, kernel_size=(5, 5), stride=(1, 1), padding=(1, 1))\n",
" (fc01): Linear(in_features=106580, out_features=50, bias=True)\n",
" (output): Linear(in_features=50, out_features=16, bias=True)\n",
")"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = theCNN()\n",
"model.load_state_dict(torch.load(\"model.pth\"))\n",
"model.eval()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "30569105",
"metadata": {},
"outputs": [],
"source": [
"# @app.post(\"/predict-similarity\")\n",
"# async def predict_similarity(image: UploadFile):\n",
"# image_bytes = await image.read()\n",
"# img = Image.open(BytesIO(image_bytes))\n",
"# img = transform(img).unsqueeze(0)\n",
"# output = model(img)\n",
"# similarity_score = torch.max(output).item() * 100 # Get the maximum predicted probability as the similarity score\n",
"# return {\"similarity_score\": similarity_score}\n",
"\n",
"\n",
"@app.post(\"/predict-similarity\")\n",
"async def predict_similarity(sign: str, image: UploadFile):\n",
" image_bytes = await image.read()\n",
" img = Image.open(BytesIO(image_bytes))\n",
" img = transform(img).unsqueeze(0)\n",
" output = model(img)\n",
" similarity_score = torch.max(output).item() * 100 # Get the maximum predicted probability as the similarity score\n",
" return {\"sign\": sign, \"similarity_score\": similarity_score}"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "5c19e31c",
"metadata": {},
"outputs": [],
"source": [
"@app.get(\"/\")\n",
"async def hello_world(): \n",
"\n",
" return {\"Hello World\"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bea39dc9",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO: Started server process [36440]\n",
"INFO: Waiting for application startup.\n",
"INFO: Application startup complete.\n",
"INFO: Uvicorn running on http://127.0.0.1:8001 (Press CTRL+C to quit)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO: 127.0.0.1:63408 - \"POST /predict-similarity HTTP/1.1\" 422 Unprocessable Entity\n",
"INFO: 127.0.0.1:63440 - \"POST /predict-similarity HTTP/1.1\" 422 Unprocessable Entity\n",
"INFO: 127.0.0.1:63461 - \"POST /predict-similarity HTTP/1.1\" 422 Unprocessable Entity\n",
"INFO: 127.0.0.1:63484 - \"POST /predict-similarity HTTP/1.1\" 422 Unprocessable Entity\n"
]
}
],
"source": [
"if __name__ == \"__main__\":\n",
" loop = asyncio.get_event_loop()\n",
" loop.create_task(uvicorn.run(app, host=\"127.0.0.1\", port=8001))\n",
" loop.run_forever()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "52ede8b7",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "3d7bb1db",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Project/Backend/ML_Models/Learning_Model/.ipynb_checkpoints/ML_Model_Calculate_Similarity_Score-checkpoint.ipynb 0 → 100644
View file @ 39909344
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Defaulting to user installation because normal site-packages is not writeable\n",
"Requirement already satisfied: torchvision in c:\\users\\janithgamage\\appdata\\roaming\\python\\python310\\site-packages (0.15.2)\n",
"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from torchvision) (9.4.0)\n",
"Requirement already satisfied: torch==2.0.1 in c:\\users\\janithgamage\\appdata\\roaming\\python\\python310\\site-packages (from torchvision) (2.0.1)\n",
"Requirement already satisfied: requests in c:\\programdata\\anaconda3\\lib\\site-packages (from torchvision) (2.28.1)\n",
"Requirement already satisfied: numpy in c:\\programdata\\anaconda3\\lib\\site-packages (from torchvision) (1.23.5)\n",
"Requirement already satisfied: jinja2 in c:\\programdata\\anaconda3\\lib\\site-packages (from torch==2.0.1->torchvision) (3.1.2)\n",
"Requirement already satisfied: networkx in c:\\programdata\\anaconda3\\lib\\site-packages (from torch==2.0.1->torchvision) (2.8.4)\n",
"Requirement already satisfied: sympy in c:\\programdata\\anaconda3\\lib\\site-packages (from torch==2.0.1->torchvision) (1.11.1)\n",
"Requirement already satisfied: filelock in c:\\programdata\\anaconda3\\lib\\site-packages (from torch==2.0.1->torchvision) (3.9.0)\n",
"Requirement already satisfied: typing-extensions in c:\\programdata\\anaconda3\\lib\\site-packages (from torch==2.0.1->torchvision) (4.4.0)\n",
"Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests->torchvision) (1.26.14)\n",
"Requirement already satisfied: certifi>=2017.4.17 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests->torchvision) (2022.12.7)\n",
"Requirement already satisfied: idna<4,>=2.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests->torchvision) (3.4)\n",
"Requirement already satisfied: charset-normalizer<3,>=2 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests->torchvision) (2.0.4)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from jinja2->torch==2.0.1->torchvision) (2.1.1)\n",
"Requirement already satisfied: mpmath>=0.19 in c:\\programdata\\anaconda3\\lib\\site-packages (from sympy->torch==2.0.1->torchvision) (1.2.1)\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"pip install torchvision"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.578710Z",
"iopub.status.idle": "2023-05-21T06:28:52.579058Z",
"shell.execute_reply": "2023-05-21T06:28:52.578896Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.578879Z"
}
},
"outputs": [],
"source": [
"import torch\n",
"import torchvision\n",
"import torchvision.transforms as transforms\n",
"from PIL import Image\n",
"from torch.utils.data import Dataset\n",
"from torch.utils.data import DataLoader\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"import os\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import re"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.580301Z",
"iopub.status.idle": "2023-05-21T06:28:52.580679Z",
"shell.execute_reply": "2023-05-21T06:28:52.580498Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.580481Z"
}
},
"outputs": [],
"source": [
"device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.581548Z",
"iopub.status.idle": "2023-05-21T06:28:52.581894Z",
"shell.execute_reply": "2023-05-21T06:28:52.581734Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.581717Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['eight', 'eleven_2', 'eleven_3', 'fifty_1', 'fifty_2', 'fifty_3', 'five', 'four & fourteen_2', 'fourteen_1', 'fourteen_3', 'nine', 'one & ten_2 & eleven_1', 'seven', 'six', 'ten_1', 'ten_3', 'thirteen_1', 'thirteen_3', 'thirty_1', 'thirty_2', 'thirty_3', 'three & thirteen_2', 'twenty_1', 'twenty_2', 'twenty_3', 'two', 'what', 'when_1', 'when_2', 'when_3', 'who', 'why']\n"
]
}
],
"source": [
"# data_dir = \"\"\"../input/sinhala-sign-language-dataset-tdj/Sn_sign_language_dataset\"\"\"\n",
"data_dir = \"\"\"C:/Users/JanithGamage/Desktop/Research/pyhon/Mdoel-1/DataSet/Sn_sign_language_dataset\"\"\"\n",
"classes = []\n",
"\n",
"for directory in os.listdir(data_dir):\n",
" if \".\" not in directory: # Removes .txt and segmentation script\n",
" classes.append(directory) \n",
"print(classes)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.583017Z",
"iopub.status.idle": "2023-05-21T06:28:52.583353Z",
"shell.execute_reply": "2023-05-21T06:28:52.583186Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.583169Z"
}
},
"outputs": [],
"source": [
"fp = []\n",
"class_name = []\n",
"for cls in classes:\n",
" files = os.listdir(f'{data_dir}/{cls}')\n",
" for file in files:\n",
" fp.append(f'{data_dir}/{cls}/{file}')\n",
" class_name.append(cls)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.584414Z",
"iopub.status.idle": "2023-05-21T06:28:52.584759Z",
"shell.execute_reply": "2023-05-21T06:28:52.584598Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.584580Z"
}
},
"outputs": [],
"source": [
"data = pd.DataFrame({\"File Path\":fp,\"Class\":class_name})"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.585665Z",
"iopub.status.idle": "2023-05-21T06:28:52.585993Z",
"shell.execute_reply": "2023-05-21T06:28:52.585834Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.585817Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"thirteen_1 668\n",
"six 598\n",
"thirteen_3 576\n",
"ten_1 467\n",
"twenty_1 463\n",
"why 438\n",
"eleven_3 435\n",
"seven 432\n",
"four & fourteen_2 423\n",
"three & thirteen_2 417\n",
"eight 416\n",
"two 386\n",
"one & ten_2 & eleven_1 383\n",
"five 381\n",
"what 375\n",
"ten_3 358\n",
"twenty_2 355\n",
"who 348\n",
"when_1 318\n",
"eleven_2 301\n",
"when_2 290\n",
"when_3 268\n",
"thirty_3 262\n",
"twenty_3 259\n",
"fifty_2 258\n",
"thirty_2 257\n",
"fifty_1 255\n",
"fifty_3 249\n",
"fourteen_3 246\n",
"thirty_1 230\n",
"fourteen_1 208\n",
"nine 136\n",
"Name: Class, dtype: int64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Class'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.587067Z",
"iopub.status.idle": "2023-05-21T06:28:52.587408Z",
"shell.execute_reply": "2023-05-21T06:28:52.587241Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.587224Z"
}
},
"outputs": [],
"source": [
"data['Class'] = data['Class'].apply(lambda x:x.title().replace(\"_\",\"\"))\n",
"data['Class'] = data['Class'].apply(lambda x:re.sub(r'[0-9]+', '', x))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.588313Z",
"iopub.status.idle": "2023-05-21T06:28:52.588671Z",
"shell.execute_reply": "2023-05-21T06:28:52.588490Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.588473Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Thirteen 1244\n",
"Twenty 1077\n",
"When 876\n",
"Ten 825\n",
"Fifty 762\n",
"Thirty 749\n",
"Eleven 736\n",
"Six 598\n",
"Fourteen 454\n",
"Why 438\n",
"Seven 432\n",
"Four & Fourteen 423\n",
"Three & Thirteen 417\n",
"Eight 416\n",
"Two 386\n",
"One & Ten & Eleven 383\n",
"Five 381\n",
"What 375\n",
"Who 348\n",
"Nine 136\n",
"Name: Class, dtype: int64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Class'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.589898Z",
"iopub.status.idle": "2023-05-21T06:28:52.590257Z",
"shell.execute_reply": "2023-05-21T06:28:52.590086Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.590061Z"
}
},
"outputs": [],
"source": [
"clases_to_remove = ['When','Why','What','Who']"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.591203Z",
"iopub.status.idle": "2023-05-21T06:28:52.592014Z",
"shell.execute_reply": "2023-05-21T06:28:52.591818Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.591791Z"
}
},
"outputs": [],
"source": [
"data = data[data['Class'].apply(lambda x: True if x not in clases_to_remove else False)]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.594622Z",
"iopub.status.idle": "2023-05-21T06:28:52.595000Z",
"shell.execute_reply": "2023-05-21T06:28:52.594827Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.594802Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Thirteen 1244\n",
"Twenty 1077\n",
"Ten 825\n",
"Fifty 762\n",
"Thirty 749\n",
"Eleven 736\n",
"Six 598\n",
"Fourteen 454\n",
"Seven 432\n",
"Four & Fourteen 423\n",
"Three & Thirteen 417\n",
"Eight 416\n",
"Two 386\n",
"One & Ten & Eleven 383\n",
"Five 381\n",
"Nine 136\n",
"Name: Class, dtype: int64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Class'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.596472Z",
"iopub.status.idle": "2023-05-21T06:28:52.596896Z",
"shell.execute_reply": "2023-05-21T06:28:52.596708Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.596686Z"
}
},
"outputs": [],
"source": [
"transform = transforms.Compose([\n",
" transforms.Resize((300,300)),\n",
" transforms.Grayscale(num_output_channels=1),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize(mean=(0.5),std=(0.5))\n",
" ])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.598034Z",
"iopub.status.idle": "2023-05-21T06:28:52.598390Z",
"shell.execute_reply": "2023-05-21T06:28:52.598222Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.598200Z"
}
},
"outputs": [],
"source": [
"str_to_int = {key:val for val,key in enumerate(data['Class'].unique())}"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.600180Z",
"iopub.status.idle": "2023-05-21T06:28:52.600589Z",
"shell.execute_reply": "2023-05-21T06:28:52.600392Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.600367Z"
}
},
"outputs": [],
"source": [
"# str_to_int['One & Ten & Eleven'] = 1\n",
"# str_to_int['Two'] = 2\n",
"# str_to_int['Three & Thirteen'] = 3\n",
"# str_to_int['Four & Fourteen'] = 4\n",
"# str_to_int['Five'] = 5\n",
"# str_to_int['Six'] = 6\n",
"# str_to_int['Seven'] = 7\n",
"# str_to_int['Eight'] = 8\n",
"# str_to_int['Nine'] = 9\n",
"# str_to_int['Ten'] = 10\n",
"# str_to_int['Eleven'] = 11\n",
"# str_to_int['Thirteen'] = 13\n",
"# str_to_int['Fourteen'] = 14\n",
"# str_to_int['Twenty'] = 20\n",
"# str_to_int['Thirty'] = 30\n",
"# str_to_int['Fifty'] = 50"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T06:31:15.957270Z",
"iopub.status.busy": "2023-05-21T06:31:15.956916Z",
"iopub.status.idle": "2023-05-21T06:31:15.966787Z",
"shell.execute_reply": "2023-05-21T06:31:15.966023Z",
"shell.execute_reply.started": "2023-05-21T06:31:15.957230Z"
}
},
"outputs": [],
"source": [
"class MarkeDataset(Dataset):\n",
" def __init__(self, data, root_dir, transform=transforms.ToTensor()):\n",
" self.data = data\n",
" self.root_dir = root_dir\n",
" self.transform = transform\n",
" self.device = device\n",
" \n",
" def __len__(self):\n",
" return len(self.data)\n",
" \n",
" def __getitem__(self, idx):\n",
" if torch.is_tensor(idx):\n",
" idx = idx.tolist()\n",
" \n",
" img_name = self.data.iloc[idx, 0]\n",
" image = Image.open(img_name)\n",
" y_label = torch.tensor(str_to_int[self.data.iloc[idx, 1]]).to(self.device)\n",
" \n",
" if self.transform:\n",
" image = self.transform(image).to(self.device) \n",
" \n",
" return (image, y_label)\n",
"\n",
"# def __getitem__(self, idx):\n",
"# if torch.is_tensor(idx):\n",
"# idx = idx.tolist()\n",
"\n",
"# img_name = self.data.iloc[idx, 0]\n",
"# image = Image.open(img_name)\n",
"# y_label = torch.tensor(str_to_int[self.data.iloc[idx, 1]]).to(self.device)\n",
"# # y_label = torch.tensor(str_to_int[self.data.iloc[idx, 1]] - 1).to(self.device)\n",
"\n",
"# if self.transform:\n",
"# image = self.transform(image).to(self.device)\n",
"\n",
"# return (image, y_label)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T06:31:25.622005Z",
"iopub.status.busy": "2023-05-21T06:31:25.621491Z",
"iopub.status.idle": "2023-05-21T06:31:25.626122Z",
"shell.execute_reply": "2023-05-21T06:31:25.625148Z",
"shell.execute_reply.started": "2023-05-21T06:31:25.621968Z"
}
},
"outputs": [],
"source": [
"dataset = MarkeDataset(\n",
" data=data,\n",
" root_dir=data_dir,\n",
" transform=transform\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.606586Z",
"iopub.status.idle": "2023-05-21T06:28:52.606914Z",
"shell.execute_reply": "2023-05-21T06:28:52.606754Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.606737Z"
}
},
"outputs": [],
"source": [
"randIds = np.random.randint(0,1000,size=10)\n",
"fig,ax = plt.subplots(2,5,figsize=(15,5))\n",
"for i,axi in zip(randIds,ax.flatten()):\n",
" img, lab = dataset[i]\n",
" axi.imshow(img.cpu().numpy().transpose((1, 2, 0)),cmap='gray')\n",
" axi.text(x = 150,y =2,s =f'Label :{str(lab.item())}',ha='center',backgroundcolor='y')\n",
" axi.axis('off')\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T06:32:07.139527Z",
"iopub.status.busy": "2023-05-21T06:32:07.139161Z",
"iopub.status.idle": "2023-05-21T06:32:07.146617Z",
"shell.execute_reply": "2023-05-21T06:32:07.145658Z",
"shell.execute_reply.started": "2023-05-21T06:32:07.139487Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"9419"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(dataset)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T06:32:13.912232Z",
"iopub.status.busy": "2023-05-21T06:32:13.911889Z",
"iopub.status.idle": "2023-05-21T06:32:13.920718Z",
"shell.execute_reply": "2023-05-21T06:32:13.919857Z",
"shell.execute_reply.started": "2023-05-21T06:32:13.912196Z"
}
},
"outputs": [],
"source": [
"# batch_size = 16\n",
"# train_set, test_set = torch.utils.data.random_split(dataset, [8000,1419])\n",
"# trainLoader = DataLoader(dataset=train_set,batch_size=batch_size,shuffle=True,drop_last=True)\n",
"# testLoader = DataLoader(dataset=test_set,batch_size=1419)\n",
"\n",
"batch_size = 16\n",
"\n",
"train_size = int(0.85 * len(dataset))\n",
"test_size = len(dataset) - train_size\n",
"\n",
"train_set, test_set = torch.utils.data.random_split(dataset, [train_size, test_size])\n",
"trainLoader = DataLoader(dataset=train_set, batch_size=batch_size, shuffle=True, drop_last=True)\n",
"testLoader = DataLoader(dataset=test_set, batch_size=len(test_set))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T06:32:15.912440Z",
"iopub.status.busy": "2023-05-21T06:32:15.912118Z",
"iopub.status.idle": "2023-05-21T06:32:15.920625Z",
"shell.execute_reply": "2023-05-21T06:32:15.919790Z",
"shell.execute_reply.started": "2023-05-21T06:32:15.912404Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Metrix size after 1st conv. and maxpool layer: 149 X 149\n",
"Metrix size after 2nd conv. and maxpool layer: 73 X 73\n"
]
}
],
"source": [
"kernel_size = 5\n",
"stride = 1\n",
"padding = 1\n",
"\n",
"metrixSize1 = int(np.floor(300+2*padding-kernel_size/stride)+1)\n",
"metrixSize1 = int(np.floor(metrixSize1/2)) #applying 2x2 Max pooling operation\n",
"\n",
"metrixSize2 = int(np.floor(metrixSize1+2*padding-kernel_size/stride)+1)\n",
"metrixSize2 = int(np.floor(metrixSize2/2)) #applying 2x2 Max pooling operation\n",
"\n",
"print(f'Metrix size after 1st conv. and maxpool layer: {metrixSize1} X {metrixSize1}')\n",
"print(f'Metrix size after 2nd conv. and maxpool layer: {metrixSize2} X {metrixSize2}')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T06:32:18.919815Z",
"iopub.status.busy": "2023-05-21T06:32:18.919472Z",
"iopub.status.idle": "2023-05-21T06:32:18.931885Z",
"shell.execute_reply": "2023-05-21T06:32:18.930832Z",
"shell.execute_reply.started": "2023-05-21T06:32:18.919776Z"
}
},
"outputs": [],
"source": [
"class theCNN(nn.Module):\n",
" def __init__(self):\n",
" super().__init__()\n",
" \n",
" self.conv01 = nn.Conv2d(\n",
" in_channels = 1,\n",
" out_channels = 10,\n",
" kernel_size = 5,\n",
" stride = 1,\n",
" padding = 1\n",
" )\n",
" \n",
" self.conv02 = nn.Conv2d(\n",
" in_channels = 10,\n",
" out_channels = 20,\n",
" kernel_size = 5,\n",
" stride = 1,\n",
" padding = 1\n",
" )\n",
" \n",
" expectedSize = np.floor((73+2*0-1)/1) +1 \n",
" expectedSize = 20*int(expectedSize**2)\n",
" \n",
" self.fc01 = nn.Linear(expectedSize,50)\n",
" self.output = nn.Linear(50,16)\n",
" \n",
" def forward(self,x):\n",
" \n",
" #convo -> maxpool -> relu\n",
" x = F.relu(F.max_pool2d(self.conv01(x),2))\n",
" \n",
" #convo -> maxpool -> relu\n",
" x = F.relu(F.max_pool2d(self.conv02(x),2))\n",
" \n",
" nUnits = x.shape.numel()/x.shape[0]\n",
" x = x.view(-1,int(nUnits))\n",
" \n",
" x = F.relu(self.fc01(x))\n",
" \n",
" return torch.softmax(self.output(x),axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T06:32:24.959976Z",
"iopub.status.busy": "2023-05-21T06:32:24.959650Z",
"iopub.status.idle": "2023-05-21T06:32:24.966633Z",
"shell.execute_reply": "2023-05-21T06:32:24.965555Z",
"shell.execute_reply.started": "2023-05-21T06:32:24.959940Z"
}
},
"outputs": [],
"source": [
"def createModel(lr):\n",
" net = theCNN()\n",
" lossFun = nn.CrossEntropyLoss()\n",
" optimizer = torch.optim.Adam(params=net.parameters(),lr=lr)\n",
" \n",
" return net,lossFun,optimizer"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T06:32:26.930919Z",
"iopub.status.busy": "2023-05-21T06:32:26.930365Z",
"iopub.status.idle": "2023-05-21T06:32:26.991834Z",
"shell.execute_reply": "2023-05-21T06:32:26.990918Z",
"shell.execute_reply.started": "2023-05-21T06:32:26.930881Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[0.0721, 0.0794, 0.0530, 0.0484, 0.0692, 0.0618, 0.0585, 0.0723, 0.0629,\n",
" 0.0478, 0.0588, 0.0584, 0.0704, 0.0604, 0.0588, 0.0678]],\n",
" grad_fn=<SoftmaxBackward0>)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"createModel(0.01)[0](torch.randn(1,1,300,300))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T06:32:29.123842Z",
"iopub.status.busy": "2023-05-21T06:32:29.123530Z",
"iopub.status.idle": "2023-05-21T06:32:29.137330Z",
"shell.execute_reply": "2023-05-21T06:32:29.136105Z",
"shell.execute_reply.started": "2023-05-21T06:32:29.123809Z"
}
},
"outputs": [],
"source": [
"def trainModel(epochs, lr):\n",
" net, LossFun, optimizer = createModel(lr)\n",
" net = net.to(device)\n",
" losses = torch.zeros(epochs)\n",
" testAccuracy = torch.zeros(epochs)\n",
" trainAccuracy = torch.zeros(epochs)\n",
"\n",
" for i in range(epochs):\n",
" net.train()\n",
" batchLoss = []\n",
" batchAccuracy = []\n",
"\n",
" for X, y in trainLoader:\n",
" yHat = net(X)\n",
" loss = LossFun(yHat, y.squeeze())\n",
"\n",
" accuracy = 100 * torch.mean((torch.argmax(yHat, axis=1) == y.squeeze()).float())\n",
" batchAccuracy.append(accuracy.item())\n",
" batchLoss.append(loss.item())\n",
"\n",
" optimizer.zero_grad()\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" losses[i] = np.mean(batchLoss)\n",
" trainAccuracy[i] = np.mean(batchAccuracy)\n",
"\n",
" net.eval()\n",
" X, y = next(iter(testLoader))\n",
"\n",
" with torch.no_grad():\n",
" yHat = net(X)\n",
"\n",
" loss = LossFun(yHat, y.squeeze())\n",
" accuracy = 100 * torch.mean((torch.argmax(yHat, axis=1) == y.squeeze()).float())\n",
" testAccuracy[i] = accuracy\n",
" print(f'Test Accuracy: {accuracy:.3f}')\n",
" \n",
" # Save the trained model\n",
" torch.save(net.state_dict(), 'model.pth')\n",
"\n",
" return net, losses, testAccuracy, trainAccuracy\n",
"\n",
"net, losses, testAccuracy, trainAccuracy = trainModel(1, 1e-4)\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T06:32:33.702648Z",
"iopub.status.busy": "2023-05-21T06:32:33.702320Z"
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test Accuracy: 78.132\n"
]
},
{
"ename": "ValueError",
"evalue": "not enough values to unpack (expected 6, got 4)",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"File \u001b[1;32m<timed exec>:1\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: not enough values to unpack (expected 6, got 4)"
]
}
],
"source": [
"%%time\n",
"net,losses,testAccuracy,trainAccuracy,yHat,X = trainModel(1,1e-4)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.623133Z",
"iopub.status.idle": "2023-05-21T06:28:52.624249Z",
"shell.execute_reply": "2023-05-21T06:28:52.623682Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.623638Z"
}
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'testAccuracy' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[25], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFinal test accuracy: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtestAccuracy[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.3f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 2\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(testAccuracy,label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTest\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 3\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(trainAccuracy,label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTrain\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
"\u001b[1;31mNameError\u001b[0m: name 'testAccuracy' is not defined"
]
}
],
"source": [
"plt.title(f'Final test accuracy: {testAccuracy[-1]:.3f}')\n",
"plt.plot(testAccuracy,label='Test')\n",
"plt.plot(trainAccuracy,label='Train')\n",
"plt.legend()\n",
"plt.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.630115Z",
"iopub.status.idle": "2023-05-21T06:28:52.630937Z",
"shell.execute_reply": "2023-05-21T06:28:52.630583Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.630547Z"
}
},
"outputs": [],
"source": [
"plt.title(f'Final train loss: {losses[-1]:.3f}')\n",
"plt.plot(losses)\n",
"plt.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.635934Z",
"iopub.status.idle": "2023-05-21T06:28:52.637341Z",
"shell.execute_reply": "2023-05-21T06:28:52.636488Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.636414Z"
}
},
"outputs": [],
"source": [
"inv_map = dict(zip(str_to_int.values(), str_to_int.keys()))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.642433Z",
"iopub.status.idle": "2023-05-21T06:28:52.643393Z",
"shell.execute_reply": "2023-05-21T06:28:52.642984Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.642964Z"
}
},
"outputs": [],
"source": [
"predictions = torch.argmax(yHat,axis=1).cpu().detach().numpy().tolist()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.646236Z",
"iopub.status.idle": "2023-05-21T06:28:52.647698Z",
"shell.execute_reply": "2023-05-21T06:28:52.647036Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.646959Z"
}
},
"outputs": [],
"source": [
"predictions = list(map(lambda x: inv_map[x],predictions))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.653068Z",
"iopub.status.idle": "2023-05-21T06:28:52.654218Z",
"shell.execute_reply": "2023-05-21T06:28:52.653379Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.653291Z"
}
},
"outputs": [],
"source": [
"randIds = np.random.randint(0,1000,size=10)\n",
"fig,ax = plt.subplots(2,5,figsize=(15,5))\n",
"for i,axi in zip(randIds,ax.flatten()):\n",
" img = X[i]\n",
" axi.imshow(img.cpu().numpy().transpose((1, 2, 0)),cmap='gray')\n",
" axi.text(x = 150,y =2,s =f'Prediction :{predictions[i]}',ha='center',backgroundcolor='y')\n",
" axi.axis('off')\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Todo\n",
" - Add Dropout\n",
" - Try Image augmentation techniques"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
Project/Backend/ML_Models/Learning_Model/.ipynb_checkpoints/sinhala-sign-language-classifier-pytorch-cnn (1)-checkpoint.ipynb 0 → 100644
View file @ 39909344
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
"execution": {
"iopub.execute_input": "2023-05-21T15:21:22.473289Z",
"iopub.status.busy": "2023-05-21T15:21:22.472946Z",
"iopub.status.idle": "2023-05-21T15:21:22.479771Z",
"shell.execute_reply": "2023-05-21T15:21:22.478829Z",
"shell.execute_reply.started": "2023-05-21T15:21:22.473248Z"
}
},
"outputs": [],
"source": [
"import torch\n",
"import torchvision\n",
"import torchvision.transforms as transforms\n",
"from PIL import Image\n",
"from torch.utils.data import Dataset\n",
"from torch.utils.data import DataLoader\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"import os\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import re"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:21:25.461226Z",
"iopub.status.busy": "2023-05-21T15:21:25.460874Z",
"iopub.status.idle": "2023-05-21T15:21:25.466436Z",
"shell.execute_reply": "2023-05-21T15:21:25.465440Z",
"shell.execute_reply.started": "2023-05-21T15:21:25.461185Z"
}
},
"outputs": [],
"source": [
"device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:21:27.486270Z",
"iopub.status.busy": "2023-05-21T15:21:27.485805Z",
"iopub.status.idle": "2023-05-21T15:21:27.513366Z",
"shell.execute_reply": "2023-05-21T15:21:27.512417Z",
"shell.execute_reply.started": "2023-05-21T15:21:27.486235Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['eight', 'eleven_2', 'eleven_3', 'fifty_1', 'fifty_2', 'fifty_3', 'five', 'four & fourteen_2', 'fourteen_1', 'fourteen_3', 'nine', 'one & ten_2 & eleven_1', 'seven', 'six', 'ten_1', 'ten_3', 'thirteen_1', 'thirteen_3', 'thirty_1', 'thirty_2', 'thirty_3', 'three & thirteen_2', 'twenty_1', 'twenty_2', 'twenty_3', 'two', 'what', 'when_1', 'when_2', 'when_3', 'who', 'why']\n"
]
}
],
"source": [
"# data_dir = \"\"\"../input/sinhala-sign-language-dataset-tdj/Sn_sign_language_dataset\"\"\"\n",
"data_dir = \"\"\"C:/Users/JanithGamage/Desktop/Research/pyhon/Mdoel-1/DataSet/Sn_sign_language_dataset\"\"\"\n",
"classes = []\n",
"\n",
"for directory in os.listdir(data_dir):\n",
" if \".\" not in directory: # Removes .txt and segmentation script\n",
" classes.append(directory) \n",
"print(classes)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:21:29.464717Z",
"iopub.status.busy": "2023-05-21T15:21:29.463975Z",
"iopub.status.idle": "2023-05-21T15:21:32.769963Z",
"shell.execute_reply": "2023-05-21T15:21:32.769064Z",
"shell.execute_reply.started": "2023-05-21T15:21:29.464662Z"
}
},
"outputs": [],
"source": [
"fp = []\n",
"class_name = []\n",
"for cls in classes:\n",
" files = os.listdir(f'{data_dir}/{cls}')\n",
" for file in files:\n",
" fp.append(f'{data_dir}/{cls}/{file}')\n",
" class_name.append(cls)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:21:37.426901Z",
"iopub.status.busy": "2023-05-21T15:21:37.426595Z",
"iopub.status.idle": "2023-05-21T15:21:37.439605Z",
"shell.execute_reply": "2023-05-21T15:21:37.438542Z",
"shell.execute_reply.started": "2023-05-21T15:21:37.426864Z"
}
},
"outputs": [],
"source": [
"data = pd.DataFrame({\"File Path\":fp,\"Class\":class_name})"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:21:38.852702Z",
"iopub.status.busy": "2023-05-21T15:21:38.852366Z",
"iopub.status.idle": "2023-05-21T15:21:38.876210Z",
"shell.execute_reply": "2023-05-21T15:21:38.875333Z",
"shell.execute_reply.started": "2023-05-21T15:21:38.852663Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"thirteen_1 668\n",
"six 598\n",
"thirteen_3 576\n",
"ten_1 467\n",
"twenty_1 463\n",
"why 438\n",
"eleven_3 435\n",
"seven 432\n",
"four & fourteen_2 423\n",
"three & thirteen_2 417\n",
"eight 416\n",
"two 386\n",
"one & ten_2 & eleven_1 383\n",
"five 381\n",
"what 375\n",
"ten_3 358\n",
"twenty_2 355\n",
"who 348\n",
"when_1 318\n",
"eleven_2 301\n",
"when_2 290\n",
"when_3 268\n",
"thirty_3 262\n",
"twenty_3 259\n",
"fifty_2 258\n",
"thirty_2 257\n",
"fifty_1 255\n",
"fifty_3 249\n",
"fourteen_3 246\n",
"thirty_1 230\n",
"fourteen_1 208\n",
"nine 136\n",
"Name: Class, dtype: int64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Class'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:21:40.858223Z",
"iopub.status.busy": "2023-05-21T15:21:40.857670Z",
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"shell.execute_reply.started": "2023-05-21T15:21:40.858177Z"
}
},
"outputs": [],
"source": [
"data['Class'] = data['Class'].apply(lambda x:x.title().replace(\"_\",\"\"))\n",
"data['Class'] = data['Class'].apply(lambda x:re.sub(r'[0-9]+', '', x))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:21:43.784267Z",
"iopub.status.busy": "2023-05-21T15:21:43.783939Z",
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"shell.execute_reply": "2023-05-21T15:21:43.795716Z",
"shell.execute_reply.started": "2023-05-21T15:21:43.784229Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Thirteen 1244\n",
"Twenty 1077\n",
"When 876\n",
"Ten 825\n",
"Fifty 762\n",
"Thirty 749\n",
"Eleven 736\n",
"Six 598\n",
"Fourteen 454\n",
"Why 438\n",
"Seven 432\n",
"Four & Fourteen 423\n",
"Three & Thirteen 417\n",
"Eight 416\n",
"Two 386\n",
"One & Ten & Eleven 383\n",
"Five 381\n",
"What 375\n",
"Who 348\n",
"Nine 136\n",
"Name: Class, dtype: int64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Class'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:21:58.633864Z",
"iopub.status.busy": "2023-05-21T15:21:58.633538Z",
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"shell.execute_reply.started": "2023-05-21T15:21:58.633832Z"
}
},
"outputs": [],
"source": [
"clases_to_remove = ['When','Why','What','Who']"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:00.893674Z",
"iopub.status.busy": "2023-05-21T15:22:00.893340Z",
"iopub.status.idle": "2023-05-21T15:22:00.908223Z",
"shell.execute_reply": "2023-05-21T15:22:00.907346Z",
"shell.execute_reply.started": "2023-05-21T15:22:00.893635Z"
}
},
"outputs": [],
"source": [
"data = data[data['Class'].apply(lambda x: True if x not in clases_to_remove else False)]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:03.880606Z",
"iopub.status.busy": "2023-05-21T15:22:03.880248Z",
"iopub.status.idle": "2023-05-21T15:22:03.891999Z",
"shell.execute_reply": "2023-05-21T15:22:03.890999Z",
"shell.execute_reply.started": "2023-05-21T15:22:03.880571Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Thirteen 1244\n",
"Twenty 1077\n",
"Ten 825\n",
"Fifty 762\n",
"Thirty 749\n",
"Eleven 736\n",
"Six 598\n",
"Fourteen 454\n",
"Seven 432\n",
"Four & Fourteen 423\n",
"Three & Thirteen 417\n",
"Eight 416\n",
"Two 386\n",
"One & Ten & Eleven 383\n",
"Five 381\n",
"Nine 136\n",
"Name: Class, dtype: int64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Class'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:06.096597Z",
"iopub.status.busy": "2023-05-21T15:22:06.096301Z",
"iopub.status.idle": "2023-05-21T15:22:06.102135Z",
"shell.execute_reply": "2023-05-21T15:22:06.101082Z",
"shell.execute_reply.started": "2023-05-21T15:22:06.096565Z"
}
},
"outputs": [],
"source": [
"transform = transforms.Compose([\n",
" transforms.Resize((300,300)),\n",
" transforms.Grayscale(num_output_channels=1),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize(mean=(0.5),std=(0.5))\n",
" ])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:08.359982Z",
"iopub.status.busy": "2023-05-21T15:22:08.359687Z",
"iopub.status.idle": "2023-05-21T15:22:08.367491Z",
"shell.execute_reply": "2023-05-21T15:22:08.366322Z",
"shell.execute_reply.started": "2023-05-21T15:22:08.359950Z"
}
},
"outputs": [],
"source": [
"str_to_int = {key:val for val,key in enumerate(data['Class'].unique())}"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:10.520408Z",
"iopub.status.busy": "2023-05-21T15:22:10.520069Z",
"iopub.status.idle": "2023-05-21T15:22:10.526070Z",
"shell.execute_reply": "2023-05-21T15:22:10.525115Z",
"shell.execute_reply.started": "2023-05-21T15:22:10.520373Z"
}
},
"outputs": [],
"source": [
"# str_to_int['One & Ten & Eleven'] = 1\n",
"# str_to_int['Two'] = 2\n",
"# str_to_int['Three & Thirteen'] = 3\n",
"# str_to_int['Four & Fourteen'] = 4\n",
"# str_to_int['Five'] = 5\n",
"# str_to_int['Six'] = 6\n",
"# str_to_int['Seven'] = 7\n",
"# str_to_int['Eight'] = 8\n",
"# str_to_int['Nine'] = 9\n",
"# str_to_int['Ten'] = 10\n",
"# str_to_int['Eleven'] = 11\n",
"# str_to_int['Thirteen'] = 13\n",
"# str_to_int['Fourteen'] = 14\n",
"# str_to_int['Twenty'] = 20\n",
"# str_to_int['Thirty'] = 30\n",
"# str_to_int['Fifty'] = 50"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:13.056196Z",
"iopub.status.busy": "2023-05-21T15:22:13.055853Z",
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"shell.execute_reply.started": "2023-05-21T15:22:13.056132Z"
}
},
"outputs": [],
"source": [
"class MarkeDataset(Dataset):\n",
" def __init__(self, data, root_dir, transform=transforms.ToTensor()):\n",
" self.data = data\n",
" self.root_dir = root_dir\n",
" self.transform = transform\n",
" self.device = device\n",
" \n",
" def __len__(self):\n",
" return len(self.data)\n",
" \n",
" def __getitem__(self, idx):\n",
" if torch.is_tensor(idx):\n",
" idx = idx.tolist()\n",
" \n",
" img_name = self.data.iloc[idx, 0]\n",
" image = Image.open(img_name)\n",
" y_label = torch.tensor(str_to_int[self.data.iloc[idx, 1]]).to(self.device)\n",
" \n",
" if self.transform:\n",
" image = self.transform(image).to(self.device) \n",
" \n",
" return (image, y_label)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:26.257162Z",
"iopub.status.busy": "2023-05-21T15:22:26.256652Z",
"iopub.status.idle": "2023-05-21T15:22:26.261594Z",
"shell.execute_reply": "2023-05-21T15:22:26.260674Z",
"shell.execute_reply.started": "2023-05-21T15:22:26.257103Z"
}
},
"outputs": [],
"source": [
"dataset = MarkeDataset(\n",
" data=data,\n",
" root_dir=data_dir,\n",
" transform=transform\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:29.282820Z",
"iopub.status.busy": "2023-05-21T15:22:29.282505Z",
"iopub.status.idle": "2023-05-21T15:22:30.450098Z",
"shell.execute_reply": "2023-05-21T15:22:30.449111Z",
"shell.execute_reply.started": "2023-05-21T15:22:29.282785Z"
}
},
"outputs": [],
"source": [
"randIds = np.random.randint(0,1000,size=10)\n",
"fig,ax = plt.subplots(2,5,figsize=(15,5))\n",
"for i,axi in zip(randIds,ax.flatten()):\n",
" img, lab = dataset[i]\n",
" axi.imshow(img.cpu().numpy().transpose((1, 2, 0)),cmap='gray')\n",
" axi.text(x = 150,y =2,s =f'Label :{str(lab.item())}',ha='center',backgroundcolor='y')\n",
" axi.axis('off')\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:34.686493Z",
"iopub.status.busy": "2023-05-21T15:22:34.686198Z",
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"shell.execute_reply.started": "2023-05-21T15:22:34.686462Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"9419"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(dataset)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:38.619412Z",
"iopub.status.busy": "2023-05-21T15:22:38.618543Z",
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"shell.execute_reply.started": "2023-05-21T15:22:38.619366Z"
}
},
"outputs": [],
"source": [
"batch_size = 16\n",
"train_set, test_set = torch.utils.data.random_split(dataset, [8000,1419])\n",
"trainLoader = DataLoader(dataset=train_set,batch_size=batch_size,shuffle=True,drop_last=True)\n",
"testLoader = DataLoader(dataset=test_set,batch_size=1419)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:41.806762Z",
"iopub.status.busy": "2023-05-21T15:22:41.806452Z",
"iopub.status.idle": "2023-05-21T15:22:41.815670Z",
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}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Metrix size after 1st conv. and maxpool layer: 149 X 149\n",
"Metrix size after 2nd conv. and maxpool layer: 73 X 73\n"
]
}
],
"source": [
"kernel_size = 5\n",
"stride = 1\n",
"padding = 1\n",
"\n",
"metrixSize1 = int(np.floor(300+2*padding-kernel_size/stride)+1)\n",
"metrixSize1 = int(np.floor(metrixSize1/2)) #applying 2x2 Max pooling operation\n",
"\n",
"metrixSize2 = int(np.floor(metrixSize1+2*padding-kernel_size/stride)+1)\n",
"metrixSize2 = int(np.floor(metrixSize2/2)) #applying 2x2 Max pooling operation\n",
"\n",
"print(f'Metrix size after 1st conv. and maxpool layer: {metrixSize1} X {metrixSize1}')\n",
"print(f'Metrix size after 2nd conv. and maxpool layer: {metrixSize2} X {metrixSize2}')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:44.572322Z",
"iopub.status.busy": "2023-05-21T15:22:44.571998Z",
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"shell.execute_reply": "2023-05-21T15:22:44.584795Z",
"shell.execute_reply.started": "2023-05-21T15:22:44.572285Z"
}
},
"outputs": [],
"source": [
"class theCNN(nn.Module):\n",
" def __init__(self):\n",
" super().__init__()\n",
" \n",
" self.conv01 = nn.Conv2d(\n",
" in_channels = 1,\n",
" out_channels = 10,\n",
" kernel_size = 5,\n",
" stride = 1,\n",
" padding = 1\n",
" )\n",
" \n",
" self.conv02 = nn.Conv2d(\n",
" in_channels = 10,\n",
" out_channels = 20,\n",
" kernel_size = 5,\n",
" stride = 1,\n",
" padding = 1\n",
" )\n",
" \n",
" expectedSize = np.floor((73+2*0-1)/1) +1 \n",
" expectedSize = 20*int(expectedSize**2)\n",
" \n",
" self.fc01 = nn.Linear(expectedSize,50)\n",
" self.output = nn.Linear(50,16)\n",
" \n",
" def forward(self,x):\n",
" \n",
" #convo -> maxpool -> relu\n",
" x = F.relu(F.max_pool2d(self.conv01(x),2))\n",
" \n",
" #convo -> maxpool -> relu\n",
" x = F.relu(F.max_pool2d(self.conv02(x),2))\n",
" \n",
" nUnits = x.shape.numel()/x.shape[0]\n",
" x = x.view(-1,int(nUnits))\n",
" \n",
" x = F.relu(self.fc01(x))\n",
" \n",
" return torch.softmax(self.output(x),axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:49.860090Z",
"iopub.status.busy": "2023-05-21T15:22:49.859764Z",
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"shell.execute_reply": "2023-05-21T15:22:49.864657Z",
"shell.execute_reply.started": "2023-05-21T15:22:49.860052Z"
}
},
"outputs": [],
"source": [
"def createModel(lr):\n",
" net = theCNN()\n",
" lossFun = nn.CrossEntropyLoss()\n",
" optimizer = torch.optim.Adam(params=net.parameters(),lr=lr)\n",
" \n",
" return net,lossFun,optimizer"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:51.830628Z",
"iopub.status.busy": "2023-05-21T15:22:51.830107Z",
"iopub.status.idle": "2023-05-21T15:22:52.032717Z",
"shell.execute_reply": "2023-05-21T15:22:52.031798Z",
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}
},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[0.0636, 0.0599, 0.0594, 0.0579, 0.0574, 0.0701, 0.0585, 0.0668, 0.0578,\n",
" 0.0632, 0.0598, 0.0619, 0.0726, 0.0633, 0.0666, 0.0611]],\n",
" grad_fn=<SoftmaxBackward0>)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"createModel(0.01)[0](torch.randn(1,1,300,300))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:55.120195Z",
"iopub.status.busy": "2023-05-21T15:22:55.119083Z",
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"shell.execute_reply.started": "2023-05-21T15:22:55.120083Z"
}
},
"outputs": [],
"source": [
"def trainModel(epochs,lr):\n",
" net,LossFun,optimizer = createModel(lr)\n",
" net = net.to(device)\n",
" losses = torch.zeros(epochs)\n",
" testAccuracy = torch.zeros(epochs)\n",
" trainAccurscy = torch.zeros(epochs)\n",
"\n",
" for i in range(epochs):\n",
" print(\"start\")\n",
" net.train()\n",
" batchLoss = []\n",
" batchAccuracy = []\n",
" for X,y in trainLoader:\n",
" yHat = net(X)\n",
" loss = LossFun(yHat,y)\n",
" # print(yHat.item())\n",
"\n",
" accuracy = 100*torch.mean((torch.argmax(yHat,axis=1)==y).float())\n",
" batchAccuracy.append(accuracy.item())\n",
" batchLoss.append(loss.item())\n",
"\n",
" optimizer.zero_grad()\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" losses[i] = np.mean(batchLoss)\n",
" trainAccurscy[i] = np.mean(batchAccuracy)\n",
"\n",
" net.eval()\n",
" X,y = next(iter(testLoader))\n",
" with torch.no_grad():\n",
" yHat = net(X)\n",
" loss = LossFun(yHat,y)\n",
" accuracy = 100*torch.mean((torch.argmax(yHat,axis=1)==y).float())\n",
" testAccuracy[i] = accuracy\n",
" print(f'Test Accuracy: {accuracy:.3f}')\n",
" \n",
" # Save the trained model\n",
" print(\"save the model\")\n",
" #torch.save(net.state_dict(), 'model.pth') \n",
" torch.save(net, \"model1.pth\")\n",
"\n",
" \n",
" return net,losses,testAccuracy,trainAccurscy,yHat,X"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:58.112687Z",
"iopub.status.busy": "2023-05-21T15:22:58.112351Z"
}
},
"outputs": [],
"source": [
"%%time\n",
"net,losses,testAccuracy,trainAccuracy,yHat,X = trainModel(10,1e-4)\n",
"# net,losses,testAccuracy,trainAccuracy,yHat,X = trainModel(1,1e-4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.623133Z",
"iopub.status.idle": "2023-05-21T06:28:52.624249Z",
"shell.execute_reply": "2023-05-21T06:28:52.623682Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.623638Z"
}
},
"outputs": [],
"source": [
"import torch\n",
"import torchvision\n",
"import torchvision.transforms as transforms\n",
"from PIL import Image\n",
"from torch.utils.data import Dataset\n",
"from torch.utils.data import DataLoader\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"import os\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import re\n",
"\n",
"plt.title(f'Final test accuracy: {testAccuracy[-1]:.3f}')\n",
"plt.plot(testAccuracy,label='Test')\n",
"plt.plot(trainAccuracy,label='Train')\n",
"plt.legend()\n",
"plt.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.630115Z",
"iopub.status.idle": "2023-05-21T06:28:52.630937Z",
"shell.execute_reply": "2023-05-21T06:28:52.630583Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.630547Z"
}
},
"outputs": [],
"source": [
"plt.title(f'Final train loss: {losses[-1]:.3f}')\n",
"plt.plot(losses)\n",
"plt.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.635934Z",
"iopub.status.idle": "2023-05-21T06:28:52.637341Z",
"shell.execute_reply": "2023-05-21T06:28:52.636488Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.636414Z"
}
},
"outputs": [],
"source": [
"inv_map = dict(zip(str_to_int.values(), str_to_int.keys()))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.642433Z",
"iopub.status.idle": "2023-05-21T06:28:52.643393Z",
"shell.execute_reply": "2023-05-21T06:28:52.642984Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.642964Z"
}
},
"outputs": [],
"source": [
"predictions = torch.argmax(yHat,axis=1).cpu().detach().numpy().tolist()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.646236Z",
"iopub.status.idle": "2023-05-21T06:28:52.647698Z",
"shell.execute_reply": "2023-05-21T06:28:52.647036Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.646959Z"
}
},
"outputs": [],
"source": [
"predictions = list(map(lambda x: inv_map[x],predictions))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.653068Z",
"iopub.status.idle": "2023-05-21T06:28:52.654218Z",
"shell.execute_reply": "2023-05-21T06:28:52.653379Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.653291Z"
}
},
"outputs": [],
"source": [
"randIds = np.random.randint(0,1000,size=10)\n",
"fig,ax = plt.subplots(2,5,figsize=(15,5))\n",
"for i,axi in zip(randIds,ax.flatten()):\n",
" img = X[i]\n",
" axi.imshow(img.cpu().numpy().transpose((1, 2, 0)),cmap='gray')\n",
" axi.text(x = 150,y =2,s =f'Prediction :{predictions[i]}',ha='center',backgroundcolor='y')\n",
" axi.axis('off')\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Todo\n",
" - Add Dropout\n",
" - Try Image augmentation techniques"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
Project/Backend/ML_Models/Learning_Model/API_Calculate_Similarity_Score-1.ipynb 0 → 100644
View file @ 39909344
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "49b49c05",
"metadata": {},
"outputs": [],
"source": [
"import nest_asyncio\n",
"import asyncio\n",
"import torch.nn.functional as F\n",
"import torch.nn as nn\n",
"import torchvision.transforms as transforms\n",
"import numpy as np\n",
"import io\n",
"import uvicorn\n",
"from fastapi import FastAPI, UploadFile\n",
"from PIL import Image\n",
"import torch"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3f0148a4",
"metadata": {},
"outputs": [],
"source": [
"nest_asyncio.apply()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "26e5f198",
"metadata": {},
"outputs": [],
"source": [
"app = FastAPI()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d4359663",
"metadata": {},
"outputs": [],
"source": [
"transform = transforms.Compose([\n",
" transforms.Resize((300, 300)),\n",
" transforms.Grayscale(num_output_channels=1),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize(mean=(0.5), std=(0.5))\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "308a25d3",
"metadata": {},
"outputs": [],
"source": [
"class theCNN(nn.Module):\n",
" def __init__(self):\n",
" super(theCNN, self).__init__()\n",
" \n",
" self.conv01 = nn.Conv2d(\n",
" in_channels=1,\n",
" out_channels=10,\n",
" kernel_size=5,\n",
" stride=1,\n",
" padding=1\n",
" )\n",
" \n",
" self.conv02 = nn.Conv2d(\n",
" in_channels=10,\n",
" out_channels=20,\n",
" kernel_size=5,\n",
" stride=1,\n",
" padding=1\n",
" )\n",
" \n",
" expectedSize = int(np.floor((73 + 2 * 0 - 1) / 1) + 1)\n",
" expectedSize = 20 * int(expectedSize ** 2)\n",
" \n",
" self.fc01 = nn.Linear(expectedSize, 50)\n",
" self.output = nn.Linear(50, 16)\n",
"\n",
" def forward(self, x):\n",
" x = F.relu(F.max_pool2d(self.conv01(x), 2))\n",
" x = F.relu(F.max_pool2d(self.conv02(x), 2))\n",
" nUnits = x.shape.numel() / x.shape[0]\n",
" x = x.view(-1, int(nUnits))\n",
" x = F.relu(self.fc01(x))\n",
" return torch.softmax(self.output(x), axis=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4e3544d2",
"metadata": {},
"outputs": [],
"source": [
"model = theCNN()\n",
"model.load_state_dict(torch.load(\"model.pth\"))\n",
"model.eval()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d521a8ca",
"metadata": {},
"outputs": [],
"source": [
"@app.post(\"/score\")\n",
"async def calculate_score(image_file: UploadFile):\n",
" image = Image.open(io.BytesIO(await image_file.read())).convert(\"L\")\n",
" image = transform(image).unsqueeze(0)\n",
"\n",
" with torch.no_grad():\n",
" output = model(image)\n",
"\n",
" probabilities = torch.softmax(output, dim=1)[0]\n",
" predicted_class = torch.argmax(probabilities).item()\n",
"\n",
" # Get the actual number corresponding to the hand sign\n",
" actual_number = get_actual_number_from_image(image)\n",
" \n",
" print(actual_number)\n",
"\n",
" # Compare predicted class with actual number and calculate correctness percentage\n",
" correct = int(predicted_class + 1 == actual_number)\n",
" print(correct)\n",
" correctness_percentage = correct / 1.0 * 100.0\n",
"\n",
" return {\"predicted_class\": predicted_class, \"correctness_percentage\": correctness_percentage}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "030c5fbc",
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"import numpy as np\n",
"\n",
"def get_actual_number_from_image(image):\n",
" # Convert the image to numpy array\n",
" image_array = np.array(image)\n",
"\n",
" # Apply image processing techniques to detect and recognize digits\n",
" # Example steps: thresholding, contour detection, character segmentation, digit recognition\n",
"\n",
" # Apply thresholding\n",
" _, binary_image = cv2.threshold(image_array, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\n",
"\n",
" # Find contours\n",
" contours, _ = cv2.findContours(binary_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
"\n",
" # Sort contours based on their x-coordinate\n",
" contours = sorted(contours, key=lambda cnt: cv2.boundingRect(cnt)[0])\n",
"\n",
" # Initialize the recognized digit sequence\n",
" digit_sequence = \"\"\n",
"\n",
" # Iterate over the contours and recognize digits\n",
" for contour in contours:\n",
" # Get the bounding box of the contour\n",
" x, y, w, h = cv2.boundingRect(contour)\n",
"\n",
" # Crop the digit region from the image\n",
" digit_image = binary_image[y:y + h, x:x + w]\n",
"\n",
" # Resize the digit image to a fixed size (e.g., 28x28)\n",
" resized_digit_image = cv2.resize(digit_image, (28, 28))\n",
"\n",
" # Preprocess the resized digit image (e.g., normalize pixel values)\n",
" preprocessed_digit_image = resized_digit_image / 255.0\n",
"\n",
" # Flatten the preprocessed digit image\n",
" flattened_digit_image = preprocessed_digit_image.flatten()\n",
"\n",
" # Pass the flattened digit image to your digit recognition model\n",
" # to get the predicted digit (e.g., using a separate model or the same model you used for training)\n",
"\n",
" # Here, let's assume you have a function `predict_digit` that takes the flattened digit image\n",
" # and returns the predicted digit as an integer\n",
" predicted_digit = predict_digit(flattened_digit_image)\n",
"\n",
" # Add the predicted digit to the digit sequence\n",
" digit_sequence += str(predicted_digit)\n",
"\n",
" # Convert the digit sequence to an integer\n",
" actual_number = int(digit_sequence)\n",
"\n",
" return actual_number\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5035c79a",
"metadata": {},
"outputs": [],
"source": [
"if __name__ == \"__main__\":\n",
" loop = asyncio.get_event_loop()\n",
" loop.create_task(uvicorn.run(app, host=\"127.0.0.1\", port=8001))\n",
" loop.run_forever()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c0449757",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Project/Backend/ML_Models/Learning_Model/API_Calculate_Similarity_Score-2.ipynb 0 → 100644
View file @ 39909344
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "c898b57c",
"metadata": {},
"outputs": [],
"source": [
"pip install python-multipart"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a99b4156",
"metadata": {},
"outputs": [],
"source": [
"import nest_asyncio\n",
"import asyncio\n",
"import torch.nn.functional as F\n",
"\n",
"# Apply the patch\n",
"nest_asyncio.apply()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "01b6e57c",
"metadata": {},
"outputs": [],
"source": [
"from fastapi import FastAPI, UploadFile\n",
"from PIL import Image\n",
"import torch\n",
"import torchvision.transforms as transforms\n",
"import numpy as np\n",
"import io\n",
"import torch.nn as nn\n",
"import asyncio\n",
"import uvicorn "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2e561f13",
"metadata": {},
"outputs": [],
"source": [
"app = FastAPI()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1e9e4208",
"metadata": {},
"outputs": [],
"source": [
"transform = transforms.Compose([\n",
" transforms.Resize((300, 300)),\n",
" transforms.Grayscale(num_output_channels=1),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize(mean=(0.5), std=(0.5))\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4d86d2b8",
"metadata": {},
"outputs": [],
"source": [
"class theCNN(nn.Module):\n",
" def __init__(self):\n",
" super(theCNN, self).__init__()\n",
" \n",
" self.conv01 = nn.Conv2d(\n",
" in_channels=1,\n",
" out_channels=10,\n",
" kernel_size=5,\n",
" stride=1,\n",
" padding=1\n",
" )\n",
" \n",
" self.conv02 = nn.Conv2d(\n",
" in_channels=10,\n",
" out_channels=20,\n",
" kernel_size=5,\n",
" stride=1,\n",
" padding=1\n",
" )\n",
" \n",
" expectedSize = int(np.floor((73 + 2 * 0 - 1) / 1) + 1)\n",
" expectedSize = 20 * int(expectedSize ** 2)\n",
" \n",
" self.fc01 = nn.Linear(expectedSize, 50)\n",
" self.output = nn.Linear(50, 16)\n",
"\n",
" def forward(self, x):\n",
" x = F.relu(F.max_pool2d(self.conv01(x), 2))\n",
" x = F.relu(F.max_pool2d(self.conv02(x), 2))\n",
" nUnits = x.shape.numel() / x.shape[0]\n",
" x = x.view(-1, int(nUnits))\n",
" x = F.relu(self.fc01(x))\n",
" return torch.softmax(self.output(x), axis=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9379bf73",
"metadata": {},
"outputs": [],
"source": [
"model = theCNN()\n",
"model.load_state_dict(torch.load(\"model.pth\"))\n",
"model.eval()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "18b869d3",
"metadata": {},
"outputs": [],
"source": [
"@app.post(\"/score\")\n",
"async def calculate_score(image_file: UploadFile):\n",
" image = Image.open(io.BytesIO(await image_file.read())).convert(\"L\")\n",
" image = transform(image).unsqueeze(0)\n",
"\n",
" with torch.no_grad():\n",
" output = model(image)\n",
"\n",
" probabilities = torch.softmax(output, dim=1)[0]\n",
" similarity_scores = probabilities.numpy()\n",
"\n",
" return {\"similarity_scores\": similarity_scores.tolist()}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4a99a8b6",
"metadata": {},
"outputs": [],
"source": [
"@app.get(\"/\")\n",
"async def hello_world(): \n",
"\n",
" return {\"Hello World\"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7b30f5c6",
"metadata": {},
"outputs": [],
"source": [
"if __name__ == \"__main__\":\n",
" loop = asyncio.get_event_loop()\n",
" loop.create_task(uvicorn.run(app, host=\"127.0.0.1\", port=8001))\n",
" loop.run_forever()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3e2e07d8",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Project/Backend/ML_Models/Learning_Model/API_Calculate_Similarity_Score-3.ipynb 0 → 100644
View file @ 39909344
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "3d735e52",
"metadata": {},
"outputs": [],
"source": [
"import nest_asyncio\n",
"import asyncio\n",
"import torch.nn.functional as F\n",
"\n",
"# Apply the patch\n",
"nest_asyncio.apply()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "db756418",
"metadata": {},
"outputs": [],
"source": [
"from fastapi import FastAPI, UploadFile\n",
"from PIL import Image\n",
"import torch\n",
"import torchvision.transforms as transforms\n",
"import numpy as np\n",
"import io\n",
"import torch.nn as nn\n",
"import asyncio\n",
"import uvicorn \n",
"from io import BytesIO"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "fc83d1b8",
"metadata": {},
"outputs": [],
"source": [
"app = FastAPI()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "63dbfc01",
"metadata": {},
"outputs": [],
"source": [
"transform = transforms.Compose([\n",
" transforms.Resize((300, 300)),\n",
" transforms.Grayscale(num_output_channels=1),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize(mean=(0.5), std=(0.5))\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "f7e5cdba",
"metadata": {},
"outputs": [],
"source": [
"class theCNN(nn.Module):\n",
" def __init__(self):\n",
" super(theCNN, self).__init__()\n",
" \n",
" self.conv01 = nn.Conv2d(\n",
" in_channels=1,\n",
" out_channels=10,\n",
" kernel_size=5,\n",
" stride=1,\n",
" padding=1\n",
" )\n",
" \n",
" self.conv02 = nn.Conv2d(\n",
" in_channels=10,\n",
" out_channels=20,\n",
" kernel_size=5,\n",
" stride=1,\n",
" padding=1\n",
" )\n",
" \n",
" expectedSize = int(np.floor((73 + 2 * 0 - 1) / 1) + 1)\n",
" expectedSize = 20 * int(expectedSize ** 2)\n",
" \n",
" self.fc01 = nn.Linear(expectedSize, 50)\n",
" self.output = nn.Linear(50, 16)\n",
"\n",
" def forward(self, x):\n",
" x = F.relu(F.max_pool2d(self.conv01(x), 2))\n",
" x = F.relu(F.max_pool2d(self.conv02(x), 2))\n",
" nUnits = x.shape.numel() / x.shape[0]\n",
" x = x.view(-1, int(nUnits))\n",
" x = F.relu(self.fc01(x))\n",
" return torch.softmax(self.output(x), axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "d86a9515",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"theCNN(\n",
" (conv01): Conv2d(1, 10, kernel_size=(5, 5), stride=(1, 1), padding=(1, 1))\n",
" (conv02): Conv2d(10, 20, kernel_size=(5, 5), stride=(1, 1), padding=(1, 1))\n",
" (fc01): Linear(in_features=106580, out_features=50, bias=True)\n",
" (output): Linear(in_features=50, out_features=16, bias=True)\n",
")"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = theCNN()\n",
"model.load_state_dict(torch.load(\"model.pth\"))\n",
"model.eval()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4b527135",
"metadata": {},
"outputs": [],
"source": [
"# @app.post(\"/predict-similarity\")\n",
"# async def predict_similarity(image: UploadFile):\n",
"# image_bytes = await image.read()\n",
"# img = Image.open(BytesIO(image_bytes))\n",
"# img = transform(img).unsqueeze(0)\n",
"# output = model(img)\n",
"# similarity_score = torch.max(output).item() * 100 # Get the maximum predicted probability as the similarity score\n",
"# return {\"similarity_score\": similarity_score}\n",
"\n",
"\n",
"@app.post(\"/predict-similarity\")\n",
"async def predict_similarity(sign: str, image: UploadFile):\n",
" image_bytes = await image.read()\n",
" img = Image.open(BytesIO(image_bytes))\n",
" img = transform(img).unsqueeze(0)\n",
" output = model(img)\n",
" similarity_score = torch.max(output).item() * 100 # Get the maximum predicted probability as the similarity score\n",
" return {\"sign\": sign, \"similarity_score\": similarity_score}"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "df08611e",
"metadata": {},
"outputs": [],
"source": [
"@app.get(\"/\")\n",
"async def hello_world(): \n",
"\n",
" return {\"Hello World\"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "84b9601b",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO: Started server process [36440]\n",
"INFO: Waiting for application startup.\n",
"INFO: Application startup complete.\n",
"INFO: Uvicorn running on http://127.0.0.1:8001 (Press CTRL+C to quit)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO: 127.0.0.1:63408 - \"POST /predict-similarity HTTP/1.1\" 422 Unprocessable Entity\n",
"INFO: 127.0.0.1:63440 - \"POST /predict-similarity HTTP/1.1\" 422 Unprocessable Entity\n",
"INFO: 127.0.0.1:63461 - \"POST /predict-similarity HTTP/1.1\" 422 Unprocessable Entity\n",
"INFO: 127.0.0.1:63484 - \"POST /predict-similarity HTTP/1.1\" 422 Unprocessable Entity\n",
"INFO: 127.0.0.1:63502 - \"POST /predict-similarity HTTP/1.1\" 422 Unprocessable Entity\n",
"INFO: 127.0.0.1:63517 - \"POST /predict-similarity HTTP/1.1\" 422 Unprocessable Entity\n",
"INFO: 127.0.0.1:63523 - \"POST /predict-similarity HTTP/1.1\" 422 Unprocessable Entity\n",
"INFO: 127.0.0.1:63536 - \"POST /predict-similarity HTTP/1.1\" 422 Unprocessable Entity\n",
"INFO: 127.0.0.1:63536 - \"POST /predict-similarity HTTP/1.1\" 422 Unprocessable Entity\n",
"INFO: 127.0.0.1:63536 - \"POST /predict-similarity HTTP/1.1\" 422 Unprocessable Entity\n",
"INFO: 127.0.0.1:63546 - \"POST /predict-similarity HTTP/1.1\" 422 Unprocessable Entity\n"
]
}
],
"source": [
"if __name__ == \"__main__\":\n",
" loop = asyncio.get_event_loop()\n",
" loop.create_task(uvicorn.run(app, host=\"127.0.0.1\", port=8001))\n",
" loop.run_forever()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ab358400",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "504cf81d",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Project/Backend/ML_Models/Learning_Model/ML_Model_Calculate_Similarity_Score.ipynb 0 → 100644
View file @ 39909344
This source diff could not be displayed because it is too large. You can view the blob instead.
Project/Backend/ML_Models/Learning_Model/sinhala-sign-language-classifier-pytorch-cnn (1).ipynb 0 → 100644
View file @ 39909344
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
"execution": {
"iopub.execute_input": "2023-05-21T15:21:22.473289Z",
"iopub.status.busy": "2023-05-21T15:21:22.472946Z",
"iopub.status.idle": "2023-05-21T15:21:22.479771Z",
"shell.execute_reply": "2023-05-21T15:21:22.478829Z",
"shell.execute_reply.started": "2023-05-21T15:21:22.473248Z"
}
},
"outputs": [],
"source": [
"import torch\n",
"import torchvision\n",
"import torchvision.transforms as transforms\n",
"from PIL import Image\n",
"from torch.utils.data import Dataset\n",
"from torch.utils.data import DataLoader\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"import os\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import re"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:21:25.461226Z",
"iopub.status.busy": "2023-05-21T15:21:25.460874Z",
"iopub.status.idle": "2023-05-21T15:21:25.466436Z",
"shell.execute_reply": "2023-05-21T15:21:25.465440Z",
"shell.execute_reply.started": "2023-05-21T15:21:25.461185Z"
}
},
"outputs": [],
"source": [
"device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:21:27.486270Z",
"iopub.status.busy": "2023-05-21T15:21:27.485805Z",
"iopub.status.idle": "2023-05-21T15:21:27.513366Z",
"shell.execute_reply": "2023-05-21T15:21:27.512417Z",
"shell.execute_reply.started": "2023-05-21T15:21:27.486235Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['eight', 'eleven_2', 'eleven_3', 'fifty_1', 'fifty_2', 'fifty_3', 'five', 'four & fourteen_2', 'fourteen_1', 'fourteen_3', 'nine', 'one & ten_2 & eleven_1', 'seven', 'six', 'ten_1', 'ten_3', 'thirteen_1', 'thirteen_3', 'thirty_1', 'thirty_2', 'thirty_3', 'three & thirteen_2', 'twenty_1', 'twenty_2', 'twenty_3', 'two', 'what', 'when_1', 'when_2', 'when_3', 'who', 'why']\n"
]
}
],
"source": [
"# data_dir = \"\"\"../input/sinhala-sign-language-dataset-tdj/Sn_sign_language_dataset\"\"\"\n",
"data_dir = \"\"\"C:/Users/JanithGamage/Desktop/Research/pyhon/Mdoel-1/DataSet/Sn_sign_language_dataset\"\"\"\n",
"classes = []\n",
"\n",
"for directory in os.listdir(data_dir):\n",
" if \".\" not in directory: # Removes .txt and segmentation script\n",
" classes.append(directory) \n",
"print(classes)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:21:29.464717Z",
"iopub.status.busy": "2023-05-21T15:21:29.463975Z",
"iopub.status.idle": "2023-05-21T15:21:32.769963Z",
"shell.execute_reply": "2023-05-21T15:21:32.769064Z",
"shell.execute_reply.started": "2023-05-21T15:21:29.464662Z"
}
},
"outputs": [],
"source": [
"fp = []\n",
"class_name = []\n",
"for cls in classes:\n",
" files = os.listdir(f'{data_dir}/{cls}')\n",
" for file in files:\n",
" fp.append(f'{data_dir}/{cls}/{file}')\n",
" class_name.append(cls)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:21:37.426901Z",
"iopub.status.busy": "2023-05-21T15:21:37.426595Z",
"iopub.status.idle": "2023-05-21T15:21:37.439605Z",
"shell.execute_reply": "2023-05-21T15:21:37.438542Z",
"shell.execute_reply.started": "2023-05-21T15:21:37.426864Z"
}
},
"outputs": [],
"source": [
"data = pd.DataFrame({\"File Path\":fp,\"Class\":class_name})"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:21:38.852702Z",
"iopub.status.busy": "2023-05-21T15:21:38.852366Z",
"iopub.status.idle": "2023-05-21T15:21:38.876210Z",
"shell.execute_reply": "2023-05-21T15:21:38.875333Z",
"shell.execute_reply.started": "2023-05-21T15:21:38.852663Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"thirteen_1 668\n",
"six 598\n",
"thirteen_3 576\n",
"ten_1 467\n",
"twenty_1 463\n",
"why 438\n",
"eleven_3 435\n",
"seven 432\n",
"four & fourteen_2 423\n",
"three & thirteen_2 417\n",
"eight 416\n",
"two 386\n",
"one & ten_2 & eleven_1 383\n",
"five 381\n",
"what 375\n",
"ten_3 358\n",
"twenty_2 355\n",
"who 348\n",
"when_1 318\n",
"eleven_2 301\n",
"when_2 290\n",
"when_3 268\n",
"thirty_3 262\n",
"twenty_3 259\n",
"fifty_2 258\n",
"thirty_2 257\n",
"fifty_1 255\n",
"fifty_3 249\n",
"fourteen_3 246\n",
"thirty_1 230\n",
"fourteen_1 208\n",
"nine 136\n",
"Name: Class, dtype: int64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Class'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:21:40.858223Z",
"iopub.status.busy": "2023-05-21T15:21:40.857670Z",
"iopub.status.idle": "2023-05-21T15:21:40.901757Z",
"shell.execute_reply": "2023-05-21T15:21:40.900784Z",
"shell.execute_reply.started": "2023-05-21T15:21:40.858177Z"
}
},
"outputs": [],
"source": [
"data['Class'] = data['Class'].apply(lambda x:x.title().replace(\"_\",\"\"))\n",
"data['Class'] = data['Class'].apply(lambda x:re.sub(r'[0-9]+', '', x))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:21:43.784267Z",
"iopub.status.busy": "2023-05-21T15:21:43.783939Z",
"iopub.status.idle": "2023-05-21T15:21:43.796665Z",
"shell.execute_reply": "2023-05-21T15:21:43.795716Z",
"shell.execute_reply.started": "2023-05-21T15:21:43.784229Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Thirteen 1244\n",
"Twenty 1077\n",
"When 876\n",
"Ten 825\n",
"Fifty 762\n",
"Thirty 749\n",
"Eleven 736\n",
"Six 598\n",
"Fourteen 454\n",
"Why 438\n",
"Seven 432\n",
"Four & Fourteen 423\n",
"Three & Thirteen 417\n",
"Eight 416\n",
"Two 386\n",
"One & Ten & Eleven 383\n",
"Five 381\n",
"What 375\n",
"Who 348\n",
"Nine 136\n",
"Name: Class, dtype: int64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Class'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:21:58.633864Z",
"iopub.status.busy": "2023-05-21T15:21:58.633538Z",
"iopub.status.idle": "2023-05-21T15:21:58.639024Z",
"shell.execute_reply": "2023-05-21T15:21:58.638410Z",
"shell.execute_reply.started": "2023-05-21T15:21:58.633832Z"
}
},
"outputs": [],
"source": [
"clases_to_remove = ['When','Why','What','Who']"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:00.893674Z",
"iopub.status.busy": "2023-05-21T15:22:00.893340Z",
"iopub.status.idle": "2023-05-21T15:22:00.908223Z",
"shell.execute_reply": "2023-05-21T15:22:00.907346Z",
"shell.execute_reply.started": "2023-05-21T15:22:00.893635Z"
}
},
"outputs": [],
"source": [
"data = data[data['Class'].apply(lambda x: True if x not in clases_to_remove else False)]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:03.880606Z",
"iopub.status.busy": "2023-05-21T15:22:03.880248Z",
"iopub.status.idle": "2023-05-21T15:22:03.891999Z",
"shell.execute_reply": "2023-05-21T15:22:03.890999Z",
"shell.execute_reply.started": "2023-05-21T15:22:03.880571Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Thirteen 1244\n",
"Twenty 1077\n",
"Ten 825\n",
"Fifty 762\n",
"Thirty 749\n",
"Eleven 736\n",
"Six 598\n",
"Fourteen 454\n",
"Seven 432\n",
"Four & Fourteen 423\n",
"Three & Thirteen 417\n",
"Eight 416\n",
"Two 386\n",
"One & Ten & Eleven 383\n",
"Five 381\n",
"Nine 136\n",
"Name: Class, dtype: int64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Class'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:06.096597Z",
"iopub.status.busy": "2023-05-21T15:22:06.096301Z",
"iopub.status.idle": "2023-05-21T15:22:06.102135Z",
"shell.execute_reply": "2023-05-21T15:22:06.101082Z",
"shell.execute_reply.started": "2023-05-21T15:22:06.096565Z"
}
},
"outputs": [],
"source": [
"transform = transforms.Compose([\n",
" transforms.Resize((300,300)),\n",
" transforms.Grayscale(num_output_channels=1),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize(mean=(0.5),std=(0.5))\n",
" ])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:08.359982Z",
"iopub.status.busy": "2023-05-21T15:22:08.359687Z",
"iopub.status.idle": "2023-05-21T15:22:08.367491Z",
"shell.execute_reply": "2023-05-21T15:22:08.366322Z",
"shell.execute_reply.started": "2023-05-21T15:22:08.359950Z"
}
},
"outputs": [],
"source": [
"str_to_int = {key:val for val,key in enumerate(data['Class'].unique())}"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:10.520408Z",
"iopub.status.busy": "2023-05-21T15:22:10.520069Z",
"iopub.status.idle": "2023-05-21T15:22:10.526070Z",
"shell.execute_reply": "2023-05-21T15:22:10.525115Z",
"shell.execute_reply.started": "2023-05-21T15:22:10.520373Z"
}
},
"outputs": [],
"source": [
"# str_to_int['One & Ten & Eleven'] = 1\n",
"# str_to_int['Two'] = 2\n",
"# str_to_int['Three & Thirteen'] = 3\n",
"# str_to_int['Four & Fourteen'] = 4\n",
"# str_to_int['Five'] = 5\n",
"# str_to_int['Six'] = 6\n",
"# str_to_int['Seven'] = 7\n",
"# str_to_int['Eight'] = 8\n",
"# str_to_int['Nine'] = 9\n",
"# str_to_int['Ten'] = 10\n",
"# str_to_int['Eleven'] = 11\n",
"# str_to_int['Thirteen'] = 13\n",
"# str_to_int['Fourteen'] = 14\n",
"# str_to_int['Twenty'] = 20\n",
"# str_to_int['Thirty'] = 30\n",
"# str_to_int['Fifty'] = 50"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:13.056196Z",
"iopub.status.busy": "2023-05-21T15:22:13.055853Z",
"iopub.status.idle": "2023-05-21T15:22:13.066406Z",
"shell.execute_reply": "2023-05-21T15:22:13.065062Z",
"shell.execute_reply.started": "2023-05-21T15:22:13.056132Z"
}
},
"outputs": [],
"source": [
"class MarkeDataset(Dataset):\n",
" def __init__(self, data, root_dir, transform=transforms.ToTensor()):\n",
" self.data = data\n",
" self.root_dir = root_dir\n",
" self.transform = transform\n",
" self.device = device\n",
" \n",
" def __len__(self):\n",
" return len(self.data)\n",
" \n",
" def __getitem__(self, idx):\n",
" if torch.is_tensor(idx):\n",
" idx = idx.tolist()\n",
" \n",
" img_name = self.data.iloc[idx, 0]\n",
" image = Image.open(img_name)\n",
" y_label = torch.tensor(str_to_int[self.data.iloc[idx, 1]]).to(self.device)\n",
" \n",
" if self.transform:\n",
" image = self.transform(image).to(self.device) \n",
" \n",
" return (image, y_label)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:26.257162Z",
"iopub.status.busy": "2023-05-21T15:22:26.256652Z",
"iopub.status.idle": "2023-05-21T15:22:26.261594Z",
"shell.execute_reply": "2023-05-21T15:22:26.260674Z",
"shell.execute_reply.started": "2023-05-21T15:22:26.257103Z"
}
},
"outputs": [],
"source": [
"dataset = MarkeDataset(\n",
" data=data,\n",
" root_dir=data_dir,\n",
" transform=transform\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:29.282820Z",
"iopub.status.busy": "2023-05-21T15:22:29.282505Z",
"iopub.status.idle": "2023-05-21T15:22:30.450098Z",
"shell.execute_reply": "2023-05-21T15:22:30.449111Z",
"shell.execute_reply.started": "2023-05-21T15:22:29.282785Z"
}
},
"outputs": [],
"source": [
"randIds = np.random.randint(0,1000,size=10)\n",
"fig,ax = plt.subplots(2,5,figsize=(15,5))\n",
"for i,axi in zip(randIds,ax.flatten()):\n",
" img, lab = dataset[i]\n",
" axi.imshow(img.cpu().numpy().transpose((1, 2, 0)),cmap='gray')\n",
" axi.text(x = 150,y =2,s =f'Label :{str(lab.item())}',ha='center',backgroundcolor='y')\n",
" axi.axis('off')\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:34.686493Z",
"iopub.status.busy": "2023-05-21T15:22:34.686198Z",
"iopub.status.idle": "2023-05-21T15:22:34.692454Z",
"shell.execute_reply": "2023-05-21T15:22:34.691684Z",
"shell.execute_reply.started": "2023-05-21T15:22:34.686462Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"9419"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(dataset)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:38.619412Z",
"iopub.status.busy": "2023-05-21T15:22:38.618543Z",
"iopub.status.idle": "2023-05-21T15:22:38.635127Z",
"shell.execute_reply": "2023-05-21T15:22:38.634375Z",
"shell.execute_reply.started": "2023-05-21T15:22:38.619366Z"
}
},
"outputs": [],
"source": [
"batch_size = 16\n",
"train_set, test_set = torch.utils.data.random_split(dataset, [8000,1419])\n",
"trainLoader = DataLoader(dataset=train_set,batch_size=batch_size,shuffle=True,drop_last=True)\n",
"testLoader = DataLoader(dataset=test_set,batch_size=1419)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:41.806762Z",
"iopub.status.busy": "2023-05-21T15:22:41.806452Z",
"iopub.status.idle": "2023-05-21T15:22:41.815670Z",
"shell.execute_reply": "2023-05-21T15:22:41.814797Z",
"shell.execute_reply.started": "2023-05-21T15:22:41.806729Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Metrix size after 1st conv. and maxpool layer: 149 X 149\n",
"Metrix size after 2nd conv. and maxpool layer: 73 X 73\n"
]
}
],
"source": [
"kernel_size = 5\n",
"stride = 1\n",
"padding = 1\n",
"\n",
"metrixSize1 = int(np.floor(300+2*padding-kernel_size/stride)+1)\n",
"metrixSize1 = int(np.floor(metrixSize1/2)) #applying 2x2 Max pooling operation\n",
"\n",
"metrixSize2 = int(np.floor(metrixSize1+2*padding-kernel_size/stride)+1)\n",
"metrixSize2 = int(np.floor(metrixSize2/2)) #applying 2x2 Max pooling operation\n",
"\n",
"print(f'Metrix size after 1st conv. and maxpool layer: {metrixSize1} X {metrixSize1}')\n",
"print(f'Metrix size after 2nd conv. and maxpool layer: {metrixSize2} X {metrixSize2}')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:44.572322Z",
"iopub.status.busy": "2023-05-21T15:22:44.571998Z",
"iopub.status.idle": "2023-05-21T15:22:44.585933Z",
"shell.execute_reply": "2023-05-21T15:22:44.584795Z",
"shell.execute_reply.started": "2023-05-21T15:22:44.572285Z"
}
},
"outputs": [],
"source": [
"class theCNN(nn.Module):\n",
" def __init__(self):\n",
" super().__init__()\n",
" \n",
" self.conv01 = nn.Conv2d(\n",
" in_channels = 1,\n",
" out_channels = 10,\n",
" kernel_size = 5,\n",
" stride = 1,\n",
" padding = 1\n",
" )\n",
" \n",
" self.conv02 = nn.Conv2d(\n",
" in_channels = 10,\n",
" out_channels = 20,\n",
" kernel_size = 5,\n",
" stride = 1,\n",
" padding = 1\n",
" )\n",
" \n",
" expectedSize = np.floor((73+2*0-1)/1) +1 \n",
" expectedSize = 20*int(expectedSize**2)\n",
" \n",
" self.fc01 = nn.Linear(expectedSize,50)\n",
" self.output = nn.Linear(50,16)\n",
" \n",
" def forward(self,x):\n",
" \n",
" #convo -> maxpool -> relu\n",
" x = F.relu(F.max_pool2d(self.conv01(x),2))\n",
" \n",
" #convo -> maxpool -> relu\n",
" x = F.relu(F.max_pool2d(self.conv02(x),2))\n",
" \n",
" nUnits = x.shape.numel()/x.shape[0]\n",
" x = x.view(-1,int(nUnits))\n",
" \n",
" x = F.relu(self.fc01(x))\n",
" \n",
" return torch.softmax(self.output(x),axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:49.860090Z",
"iopub.status.busy": "2023-05-21T15:22:49.859764Z",
"iopub.status.idle": "2023-05-21T15:22:49.865939Z",
"shell.execute_reply": "2023-05-21T15:22:49.864657Z",
"shell.execute_reply.started": "2023-05-21T15:22:49.860052Z"
}
},
"outputs": [],
"source": [
"def createModel(lr):\n",
" net = theCNN()\n",
" lossFun = nn.CrossEntropyLoss()\n",
" optimizer = torch.optim.Adam(params=net.parameters(),lr=lr)\n",
" \n",
" return net,lossFun,optimizer"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:51.830628Z",
"iopub.status.busy": "2023-05-21T15:22:51.830107Z",
"iopub.status.idle": "2023-05-21T15:22:52.032717Z",
"shell.execute_reply": "2023-05-21T15:22:52.031798Z",
"shell.execute_reply.started": "2023-05-21T15:22:51.830587Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[0.0636, 0.0599, 0.0594, 0.0579, 0.0574, 0.0701, 0.0585, 0.0668, 0.0578,\n",
" 0.0632, 0.0598, 0.0619, 0.0726, 0.0633, 0.0666, 0.0611]],\n",
" grad_fn=<SoftmaxBackward0>)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"createModel(0.01)[0](torch.randn(1,1,300,300))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:55.120195Z",
"iopub.status.busy": "2023-05-21T15:22:55.119083Z",
"iopub.status.idle": "2023-05-21T15:22:55.134205Z",
"shell.execute_reply": "2023-05-21T15:22:55.133312Z",
"shell.execute_reply.started": "2023-05-21T15:22:55.120083Z"
}
},
"outputs": [],
"source": [
"def trainModel(epochs,lr):\n",
" net,LossFun,optimizer = createModel(lr)\n",
" net = net.to(device)\n",
" losses = torch.zeros(epochs)\n",
" testAccuracy = torch.zeros(epochs)\n",
" trainAccurscy = torch.zeros(epochs)\n",
"\n",
" for i in range(epochs):\n",
" print(\"start\")\n",
" net.train()\n",
" batchLoss = []\n",
" batchAccuracy = []\n",
" for X,y in trainLoader:\n",
" yHat = net(X)\n",
" loss = LossFun(yHat,y)\n",
" # print(yHat.item())\n",
"\n",
" accuracy = 100*torch.mean((torch.argmax(yHat,axis=1)==y).float())\n",
" batchAccuracy.append(accuracy.item())\n",
" batchLoss.append(loss.item())\n",
"\n",
" optimizer.zero_grad()\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" losses[i] = np.mean(batchLoss)\n",
" trainAccurscy[i] = np.mean(batchAccuracy)\n",
"\n",
" net.eval()\n",
" X,y = next(iter(testLoader))\n",
" with torch.no_grad():\n",
" yHat = net(X)\n",
" loss = LossFun(yHat,y)\n",
" accuracy = 100*torch.mean((torch.argmax(yHat,axis=1)==y).float())\n",
" testAccuracy[i] = accuracy\n",
" print(f'Test Accuracy: {accuracy:.3f}')\n",
" \n",
" # Save the trained model\n",
" print(\"save the model\")\n",
" #torch.save(net.state_dict(), 'model.pth') \n",
" torch.save(net, \"model1.pth\")\n",
"\n",
" \n",
" return net,losses,testAccuracy,trainAccurscy,yHat,X"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-21T15:22:58.112687Z",
"iopub.status.busy": "2023-05-21T15:22:58.112351Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"start\n",
"Test Accuracy: 77.590\n",
"save the model\n",
"start\n",
"Test Accuracy: 84.003\n",
"save the model\n",
"start\n",
"Test Accuracy: 84.496\n",
"save the model\n",
"start\n",
"Test Accuracy: 85.976\n",
"save the model\n",
"start\n",
"Test Accuracy: 86.469\n",
"save the model\n",
"start\n",
"Test Accuracy: 86.610\n",
"save the model\n",
"start\n",
"Test Accuracy: 86.328\n",
"save the model\n",
"start\n",
"Test Accuracy: 86.751\n",
"save the model\n",
"start\n",
"Test Accuracy: 86.399\n",
"save the model\n",
"start\n",
"Test Accuracy: 86.681\n",
"save the model\n",
"CPU times: total: 4h 42s\n",
"Wall time: 1h 3min 31s\n"
]
}
],
"source": [
"%%time\n",
"net,losses,testAccuracy,trainAccuracy,yHat,X = trainModel(10,1e-4)\n",
"# net,losses,testAccuracy,trainAccuracy,yHat,X = trainModel(1,1e-4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.623133Z",
"iopub.status.idle": "2023-05-21T06:28:52.624249Z",
"shell.execute_reply": "2023-05-21T06:28:52.623682Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.623638Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import torch\n",
"import torchvision\n",
"import torchvision.transforms as transforms\n",
"from PIL import Image\n",
"from torch.utils.data import Dataset\n",
"from torch.utils.data import DataLoader\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"import os\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import re\n",
"\n",
"plt.title(f'Final test accuracy: {testAccuracy[-1]:.3f}')\n",
"plt.plot(testAccuracy,label='Test')\n",
"plt.plot(trainAccuracy,label='Train')\n",
"plt.legend()\n",
"plt.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.630115Z",
"iopub.status.idle": "2023-05-21T06:28:52.630937Z",
"shell.execute_reply": "2023-05-21T06:28:52.630583Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.630547Z"
}
},
"outputs": [],
"source": [
"plt.title(f'Final train loss: {losses[-1]:.3f}')\n",
"plt.plot(losses)\n",
"plt.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.635934Z",
"iopub.status.idle": "2023-05-21T06:28:52.637341Z",
"shell.execute_reply": "2023-05-21T06:28:52.636488Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.636414Z"
}
},
"outputs": [],
"source": [
"inv_map = dict(zip(str_to_int.values(), str_to_int.keys()))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.642433Z",
"iopub.status.idle": "2023-05-21T06:28:52.643393Z",
"shell.execute_reply": "2023-05-21T06:28:52.642984Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.642964Z"
}
},
"outputs": [],
"source": [
"predictions = torch.argmax(yHat,axis=1).cpu().detach().numpy().tolist()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.646236Z",
"iopub.status.idle": "2023-05-21T06:28:52.647698Z",
"shell.execute_reply": "2023-05-21T06:28:52.647036Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.646959Z"
}
},
"outputs": [],
"source": [
"predictions = list(map(lambda x: inv_map[x],predictions))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-21T06:28:52.653068Z",
"iopub.status.idle": "2023-05-21T06:28:52.654218Z",
"shell.execute_reply": "2023-05-21T06:28:52.653379Z",
"shell.execute_reply.started": "2023-05-21T06:28:52.653291Z"
}
},
"outputs": [],
"source": [
"randIds = np.random.randint(0,1000,size=10)\n",
"fig,ax = plt.subplots(2,5,figsize=(15,5))\n",
"for i,axi in zip(randIds,ax.flatten()):\n",
" img = X[i]\n",
" axi.imshow(img.cpu().numpy().transpose((1, 2, 0)),cmap='gray')\n",
" axi.text(x = 150,y =2,s =f'Prediction :{predictions[i]}',ha='center',backgroundcolor='y')\n",
" axi.axis('off')\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Todo\n",
" - Add Dropout\n",
" - Try Image augmentation techniques"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
Project/Backend/Server_Node/controllers/user.controller.js
View file @ 39909344
...@@ -41,7 +41,7 @@ export const signUp = async (req, res) => { ...@@ -41,7 +41,7 @@ export const signUp = async (req, res) => {
} = req.body; } = req.body;
try { try {
if (!type) return res.status(400).json({ code: "02", message: "Type Field Required" }) // if (!type) return res.status(400).json({ code: "02", message: "Type Field Required" })
if (!email) return res.status(400).json({ code: "02", message: "Email Field Required" }) if (!email) return res.status(400).json({ code: "02", message: "Email Field Required" })
if (!userFirstName) return res.status(400).json({ code: "02", message: "User First Name Field Required" }) if (!userFirstName) return res.status(400).json({ code: "02", message: "User First Name Field Required" })
if (!userLastName) return res.status(400).json({ code: "02", message: "User Last Name Field Required" }) if (!userLastName) return res.status(400).json({ code: "02", message: "User Last Name Field Required" })
...@@ -50,7 +50,52 @@ export const signUp = async (req, res) => { ...@@ -50,7 +50,52 @@ export const signUp = async (req, res) => {
const existingUser = await User.findOne({ email }) const existingUser = await User.findOne({ email })
if (existingUser) return res.status(400).json({ code: "02", message: "User already exists" }) if (existingUser) return res.status(400).json({ code: "02", message: "User already exists" })
if (type === "buyer") { // if (type === "buyer") {
// if (!password) return res.status(400).json({ code: "02", message: "Password Field Required" })
// if (password !== confirmPassword) return res.status(400).json({ code: "02", message: "Passwords do not match" })
// const hashedPassword = await bcrypt.hash(password, 12)
// const userDetails = new User({
// email,
// password: hashedPassword,
// type,
// userDetails: {
// userQNumber: uuidv4(),
// userEmail: email,
// userName: `${userFirstName} ${userLastName}`,
// userContactNumber,
// userAddress: `${userAddressLine1}, ${userAddressLine2}, ${userAddressLine3}`,
// userType: type,
// }
// })
// const userResult = await userDetails.save()
// const token = jwt.sign({ email: userResult.email, id: userResult._id }, 'test', { expiresIn: "1h" })
// res.status(200).json({ code: "01", result: userResult, token })
// } else if (type === "trader") {
// const userDetails = new User({
// email,
// type,
// userDetails: {
// userQNumber: uuidv4(),
// userEmail: email,
// userName: `${userFirstName} ${userLastName}`,
// userContactNumber,
// userAddress: `${userAddressLine1}, ${userAddressLine2}, ${userAddressLine3}`,
// userType: type,
// },
// states: 2
// })
// const userResult = await userDetails.save()
// const token = jwt.sign({ email: userResult.email, id: userResult._id }, 'test', { expiresIn: "1h" })
// res.status(200).json({ code: "01", result: userResult, token })
// }
if (!password) return res.status(400).json({ code: "02", message: "Password Field Required" }) if (!password) return res.status(400).json({ code: "02", message: "Password Field Required" })
if (password !== confirmPassword) return res.status(400).json({ code: "02", message: "Passwords do not match" }) if (password !== confirmPassword) return res.status(400).json({ code: "02", message: "Passwords do not match" })
const hashedPassword = await bcrypt.hash(password, 12) const hashedPassword = await bcrypt.hash(password, 12)
...@@ -65,7 +110,6 @@ export const signUp = async (req, res) => { ...@@ -65,7 +110,6 @@ export const signUp = async (req, res) => {
userName: `${userFirstName} ${userLastName}`, userName: `${userFirstName} ${userLastName}`,
userContactNumber, userContactNumber,
userAddress: `${userAddressLine1}, ${userAddressLine2}, ${userAddressLine3}`, userAddress: `${userAddressLine1}, ${userAddressLine2}, ${userAddressLine3}`,
userType: type,
} }
}) })
...@@ -74,27 +118,6 @@ export const signUp = async (req, res) => { ...@@ -74,27 +118,6 @@ export const signUp = async (req, res) => {
const token = jwt.sign({ email: userResult.email, id: userResult._id }, 'test', { expiresIn: "1h" }) const token = jwt.sign({ email: userResult.email, id: userResult._id }, 'test', { expiresIn: "1h" })
res.status(200).json({ code: "01", result: userResult, token }) res.status(200).json({ code: "01", result: userResult, token })
} else if (type === "trader") {
const userDetails = new User({
email,
type,
userDetails: {
userQNumber: uuidv4(),
userEmail: email,
userName: `${userFirstName} ${userLastName}`,
userContactNumber,
userAddress: `${userAddressLine1}, ${userAddressLine2}, ${userAddressLine3}`,
userType: type,
},
states: 2
})
const userResult = await userDetails.save()
const token = jwt.sign({ email: userResult.email, id: userResult._id }, 'test', { expiresIn: "1h" })
res.status(200).json({ code: "01", result: userResult, token })
}
} catch (error) { } catch (error) {
res.status(500).json({ code: "00", message: "Something went wrong" }) res.status(500).json({ code: "00", message: "Something went wrong" })
......
Project/Backend/Server_Node/mocq_data/Curriculums.ts 0 → 100644
View file @ 39909344
const curriculums = [
{
"curriculumCode": "01",
"curriculumLevel": "Base Level",
"curriculumName": "Learn Sign Language",
"curriculumImage": "https://drive.google.com/uc?export=view&id=1YACBlu7X-O7-DKv5DoW3AM9kgfT7Yhdc",
"tutorials": [
{
"tutorialCode": "01",
"tutorialTitle": "Numbers and Counting in Sign Language",
"tutorialImage": "https://drive.google.com/uc?export=view&id=1YACBlu7X-O7-DKv5DoW3AM9kgfT7Yhdc",
"taskItems": [
{
"title": "Learn Number One",
"description": "Learn how to sign the number one in sign language.",
"howToDo": "- Extend your index finger straight up.\n- Keep the rest of your fingers closed.\n- Hold your hand in front of your chest.",
"referenceImage": "https://example.com/number_one.jpg",
"referenceVideo": "https://example.com/number_one_video.mp4"
},
{
"title": "Learn Number Two",
"description": "Learn how to sign the number two in sign language.",
"howToDo": "- Extend your index and middle fingers straight up.\n- Keep the rest of your fingers closed.\n- Hold your hand in front of your chest.",
"referenceImage": "https://example.com/number_two.jpg",
"referenceVideo": "https://example.com/number_two_video.mp4"
},
{
"title": "Learn Number Three",
"description": "Learn how to sign the number three in sign language.",
"howToDo": "- Extend your index, middle, and ring fingers straight up.\n- Keep the rest of your fingers closed.\n- Hold your hand in front of your chest.",
"referenceImage": "https://example.com/number_three.jpg",
"referenceVideo": "https://example.com/number_three_video.mp4"
},
{
"title": "Learn Number Four",
"description": "Learn how to sign the number four in sign language.",
"howToDo": "- Extend your thumb, index, middle, and ring fingers straight up.\n- Keep your pinky finger folded.\n- Hold your hand in front of your chest.",
"referenceImage": "https://example.com/number_four.jpg",
"referenceVideo": "https://example.com/number_four_video.mp4"
},
{
"title": "Learn Number Five",
"description": "Learn how to sign the number five in sign language.",
"howToDo": "- Extend all your fingers straight up.\n- Keep your thumb resting on the side of your palm.\n- Hold your hand in front of your chest.",
"referenceImage": "https://example.com/number_five.jpg",
"referenceVideo": "https://example.com/number_five_video.mp4"
},
{
"title": "Learn Number Six",
"description": "Learn how to sign the number six in sign language.",
"howToDo": "- Extend your thumb and pinky finger straight up.\n- Keep the rest of your fingers closed.\n- Hold your hand in front of your chest.",
"referenceImage": "https://example.com/number_six.jpg",
"referenceVideo": "https://example.com/number_six_video.mp4"
},
{
"title": "Learn Number Seven",
"description": "Learn how to sign the number seven in sign language.",
"howToDo": "- Extend your index, middle, and ring fingers straight up.\n- Keep your thumb, pinky, and pinky finger folded.\n- Hold your hand in front of your chest.",
"referenceImage": "https://example.com/number_seven.jpg",
"referenceVideo": "https://example.com/number_seven_video.mp4"
},
{
"title": "Learn Number Eight",
"description": "Learn how to sign the number eight in sign language.",
"howToDo": "- Extend all your fingers straight up.\n- Cross your index and middle fingers over your ring and pinky fingers.\n- Hold your hand in front of your chest.",
"referenceImage": "https://example.com/number_eight.jpg",
"referenceVideo": "https://example.com/number_eight_video.mp4"
},
{
"title": "Learn Number Nine",
"description": "Learn how to sign the number nine in sign language.",
"howToDo": "- Extend your thumb and all your fingers straight up.\n- Keep your pinky finger folded.\n- Hold your hand in front of your chest.",
"referenceImage": "https://example.com/number_nine.jpg",
"referenceVideo": "https://example.com/number_nine_video.mp4"
},
{
"title": "Learn Number Ten",
"description": "Learn how to sign the number ten in sign language.",
"howToDo": "- Extend your thumb, index, and middle fingers straight up.\n- Keep the rest of your fingers closed.\n- Hold your hand in front of your chest.",
"referenceImage": "https://example.com/number_ten.jpg",
"referenceVideo": "https://example.com/number_ten_video.mp4"
}
]
},
{
"tutorialCode": "02",
"tutorialTitle": "Learn the Basics of Sign Language",
"tutorialImage": "https://drive.google.com/uc?export=view&id=1YACBlu7X-O7-DKv5DoW3AM9kgfT7Yhdc",
"tutorialContent": "Introduce the concept of sign language and its importance.\nTeach basic greetings and expressions, such as hello, goodbye, thank you, and sorry.\nProvide visual demonstrations and practice exercises for learners to practice these basic signs."
},
{
"tutorialCode": "03",
"tutorialTitle": "Family Signs in Sign Language",
"tutorialImage": "https://drive.google.com/uc?export=view&id=1YACBlu7X-O7-DKv5DoW3AM9kgfT7Yhdc",
"tutorialContent": "Teach signs for family members, such as mother, father, sister, brother, etc.\nIntroduce signs for common family-related words, such as family, love, and home.\nProvide visual demonstrations and practice exercises for learners to practice these family signs."
},
{
"tutorialCode": "04",
"tutorialTitle": "Everyday Vocabulary in Sign Language",
"tutorialImage": "https://drive.google.com/uc?export=view&id=1YACBlu7X-O7-DKv5DoW3AM9kgfT7Yhdc",
"tutorialContent": "Teach signs for everyday objects and activities, such as eat, drink, sleep, book, pen, etc.\nIntroduce signs for common words used in daily life.\nProvide visual demonstrations and interactive exercises for learners to practice using these signs."
},
{
"tutorialCode": "05",
"tutorialTitle": "Basic Conversational Phrases in Sign Language",
"tutorialImage": "https://drive.google.com/uc?export=view&id=1YACBlu7X-O7-DKv5DoW3AM9kgfT7Yhdc",
"tutorialContent": "Teach simple conversational phrases, such as \"What is your name?\" or \"How are you?\"\nIntroduce signs for common question words and phrases.\nProvide visual demonstrations and practice exercises for learners to practice these conversational phrases."
}
]
},
{
"curriculumCode": "02",
"curriculumLevel": "Medium Level",
"curriculumName": "Learn Sign Language",
"curriculumImage": "https://drive.google.com/uc?export=view&id=1YACBlu7X-O7-DKv5DoW3AM9kgfT7Yhdc",
"tutorials": []
},
{
"curriculumCode": "03",
"curriculumLevel": "Advance Level",
"curriculumName": "Learn Sign Language",
"curriculumImage": "https://drive.google.com/uc?export=view&id=1YACBlu7X-O7-DKv5DoW3AM9kgfT7Yhdc",
"tutorials": []
}
]
Project/Backend/Server_Node/models/user.model.js
View file @ 39909344
...@@ -36,6 +36,7 @@ const userSchema = mongoose.Schema({ ...@@ -36,6 +36,7 @@ const userSchema = mongoose.Schema({
}, },
userType: { userType: {
type: String, type: String,
default: "N/A",
required: true required: true
}, },
}, },
......
Project/Backend/Server_Node/server.js
View file @ 39909344
...@@ -5,8 +5,8 @@ import express from "express"; ...@@ -5,8 +5,8 @@ import express from "express";
import mongoose from "mongoose"; import mongoose from "mongoose";
//import routes //import routes
import userRoutes from "./routes/user.routes.js";
import translateRoutes from "./routes/translate.routes.js"; import translateRoutes from "./routes/translate.routes.js";
import userRoutes from "./routes/user.routes.js";
dotenv.config(); dotenv.config();
const app = express(); const app = express();
......
Project/Frontend/Web_App/package-lock.json deleted 100644 → 0
View file @ b67a974b
This source diff could not be displayed because it is too large. You can view the blob instead.
Project/Frontend/Web_App/public/assets/icons/navbar/ic_learning.svg 0 → 100644
View file @ 39909344
<svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512" version="1.1"><path d="" stroke="none" fill="#080404" fill-rule="evenodd"/><path d="M 240.500 0.655 C 192.751 8.281, 155.481 46.800, 149.473 94.735 C 148.859 99.631, 149.012 100.166, 151.831 102.985 C 155.092 106.246, 156.921 106.645, 160.316 104.837 C 163.705 103.031, 164.739 101.025, 165.431 94.904 C 167.480 76.798, 177.669 56.294, 191.481 42.481 C 218.452 15.511, 260.448 7.930, 294.496 23.885 C 319.274 35.496, 335.922 54.504, 344.523 81 C 346.899 88.321, 347.342 91.580, 347.715 104.500 C 348.110 118.166, 347.921 120.383, 345.588 129.421 C 344.180 134.878, 341.694 142.193, 340.064 145.678 C 338.433 149.162, 324.840 171.023, 309.857 194.256 L 282.616 236.500 282.200 243.717 C 281.919 248.595, 281.285 251.349, 280.242 252.214 C 278.865 253.357, 278.767 252.634, 279.333 245.498 C 279.682 241.099, 280.663 224.675, 281.513 209 C 282.363 193.325, 283.298 178.588, 283.591 176.250 L 284.123 172 289.695 172 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393.948 291.141, 390.180 291, 349.064 291 L 304.349 291 302.174 293.314 M 119.308 328.010 C 115.886 329.388, 113.714 333.422, 114.491 336.958 C 115.840 343.099, 115.029 343, 164.048 343 L 208.651 343 210.826 340.686 C 214.814 336.441, 213.301 329.998, 207.872 328.105 C 204.009 326.758, 122.633 326.671, 119.308 328.010 M 304.400 328.412 C 299.382 330.622, 298.301 337.055, 302.314 340.826 C 304.619 342.991, 304.814 343, 349.204 343 C 390.186 343, 393.948 342.859, 395.886 341.250 C 399.301 338.415, 399.635 333.850, 396.680 330.413 L 394.175 327.500 350.837 327.273 C 316.003 327.090, 306.892 327.314, 304.400 328.412 M 117.201 366.028 C 112.920 370.016, 113.426 375.268, 118.406 378.531 C 120.431 379.857, 126.626 380.024, 164.960 379.781 L 209.178 379.500 211.089 377.139 C 213.917 373.645, 213.588 368.607, 210.365 366.073 C 207.790 364.047, 206.732 364, 163.554 364 C 120.558 364, 119.319 364.054, 117.201 366.028 M 302.635 366.073 C 299.096 368.856, 299.014 374.104, 302.455 377.545 L 304.909 380 349.031 380 L 393.154 380 396.077 377.077 C 399.248 373.906, 399.635 372.061, 397.837 368.684 C 395.350 364.015, 395.874 364.065, 349.385 364.032 C 306.244 364.001, 305.212 364.046, 302.635 366.073 M 292.286 432.451 C 279.717 436.379, 269.545 447.137, 265.529 460.750 L 264.865 463 342.998 463 C 394.945 463, 421.780 462.653, 423.066 461.965 C 424.846 461.012, 425 459.737, 425 445.965 L 425 431 360.750 431.067 C 310.560 431.119, 295.578 431.422, 292.286 432.451" stroke="none" fill="#040404" fill-rule="evenodd"/></svg>
\ No newline at end of file
Project/Frontend/Web_App/public/manifest.json
View file @ 39909344
{ {
"name": "React Material Minimal UI", "name": "React Material SignConnect+",
"short_name": "Minimal-UI", "short_name": "Minimal-UI",
"display": "standalone", "display": "standalone",
"start_url": "/", "start_url": "/",
......
Project/Frontend/Web_App/src/components/mega-menu/MegaMenuDesktopVertical.tsx
View file @ 39909344
...@@ -17,7 +17,7 @@ import MegaMenuCarousel from './MenuCarousel'; ...@@ -17,7 +17,7 @@ import MegaMenuCarousel from './MenuCarousel';
// ---------------------------------------------------------------------- // ----------------------------------------------------------------------
const MENU_PAPER_WIDTH = 800; const MENU_PAPER_WIDTH = 800;
const PARENT_ITEM_HEIGHT = 40; const PARENT_ITEM_HEIGHT = 60;
type Props = { type Props = {
data: MegaMenuItemProps[]; data: MegaMenuItemProps[];
......
Project/Frontend/Web_App/src/layouts/dashboard/nav/NavVertical.tsx
View file @ 39909344
...@@ -2,20 +2,18 @@ import { useEffect } from 'react'; ...@@ -2,20 +2,18 @@ import { useEffect } from 'react';
// next // next
import { useRouter } from 'next/router'; import { useRouter } from 'next/router';
// @mui // @mui
import { Box, Stack, Drawer, Button } from '@mui/material'; import { Box, Button, Drawer, Stack } from '@mui/material';
// hooks // hooks
import useResponsive from '../../../hooks/useResponsive'; import useResponsive from '../../../hooks/useResponsive';
// config // config
import { NAV } from '../../../config'; import { NAV } from '../../../config';
// components // components
import Logo from '../../../components/logo';
import Scrollbar from '../../../components/scrollbar';
import { NavSectionVertical } from '../../../components/nav-section'; import { NavSectionVertical } from '../../../components/nav-section';
import Scrollbar from '../../../components/scrollbar';
// //
import navConfig from './config';
import NavDocs from './NavDocs';
import NavAccount from './NavAccount';
import { useSettingsContext } from 'src/components/settings'; import { useSettingsContext } from 'src/components/settings';
import NavAccount from './NavAccount';
import navConfig from './config';
// ---------------------------------------------------------------------- // ----------------------------------------------------------------------
...@@ -38,12 +36,12 @@ export default function NavVertical({ openNav, onCloseNav }: Props) { ...@@ -38,12 +36,12 @@ export default function NavVertical({ openNav, onCloseNav }: Props) {
value: 'mini' value: 'mini'
} }
} as React.ChangeEvent<HTMLInputElement>) } as React.ChangeEvent<HTMLInputElement>)
}else { } else {
onChangeLayout({ onChangeLayout({
target: { target: {
value: 'vertical' value: 'vertical'
} }
} as React.ChangeEvent<HTMLInputElement> ) } as React.ChangeEvent<HTMLInputElement>)
} }
} }
...@@ -75,7 +73,7 @@ export default function NavVertical({ openNav, onCloseNav }: Props) { ...@@ -75,7 +73,7 @@ export default function NavVertical({ openNav, onCloseNav }: Props) {
}} }}
> >
<Button onClick={onClickHan}>Change Layout</Button> <Button onClick={onClickHan}>Change Layout</Button>
{/* <Logo /> */} {/* <Logo /> */}
<NavAccount /> <NavAccount />
...@@ -85,7 +83,6 @@ export default function NavVertical({ openNav, onCloseNav }: Props) { ...@@ -85,7 +83,6 @@ export default function NavVertical({ openNav, onCloseNav }: Props) {
<Box sx={{ flexGrow: 1 }} /> <Box sx={{ flexGrow: 1 }} />
<NavDocs />
</Scrollbar> </Scrollbar>
); );
......
Project/Frontend/Web_App/src/layouts/dashboard/nav/config.tsx
View file @ 39909344
// routes // routes
import { PATH_DASHBOARD } from '../../../routes/paths'; import { PATH_DASHBOARD } from '../../../routes/paths';
// components // components
import Label from '../../../components/label';
import Iconify from '../../../components/iconify';
import SvgColor from '../../../components/svg-color'; import SvgColor from '../../../components/svg-color';
// ---------------------------------------------------------------------- // ----------------------------------------------------------------------
...@@ -33,206 +31,40 @@ const ICONS = { ...@@ -33,206 +31,40 @@ const ICONS = {
ecommerce: icon('ic_ecommerce'), ecommerce: icon('ic_ecommerce'),
analytics: icon('ic_analytics'), analytics: icon('ic_analytics'),
dashboard: icon('ic_dashboard'), dashboard: icon('ic_dashboard'),
learning: icon('ic_learning')
}; };
const navConfig = [ const navConfig = [
// GENERAL // GENERAL
// ----------------------------------------------------------------------
{ {
subheader: 'general', subheader: 'general',
items: [ items: [
{ title: 'app', path: PATH_DASHBOARD.general.app, icon: ICONS.dashboard }, { title: 'app', path: PATH_DASHBOARD.general.app, icon: ICONS.dashboard },
{ title: 'ecommerce', path: PATH_DASHBOARD.general.ecommerce, icon: ICONS.ecommerce }, { title: 'blank', path: PATH_DASHBOARD.blank, icon: ICONS.blank },
{ title: 'analytics', path: PATH_DASHBOARD.general.analytics, icon: ICONS.analytics }, //Spoken language to Sign Language Module items
{ title: 'banking', path: PATH_DASHBOARD.general.banking, icon: ICONS.banking },
{ title: 'booking', path: PATH_DASHBOARD.general.booking, icon: ICONS.booking },
{ title: 'file', path: PATH_DASHBOARD.general.file, icon: ICONS.file },
],
},
// MANAGEMENT
// ----------------------------------------------------------------------
{
subheader: 'management',
items: [
// USER
{
title: 'user',
path: PATH_DASHBOARD.user.root,
icon: ICONS.user,
children: [
{ title: 'profile', path: PATH_DASHBOARD.user.profile },
{ title: 'cards', path: PATH_DASHBOARD.user.cards },
{ title: 'list', path: PATH_DASHBOARD.user.list },
{ title: 'create', path: PATH_DASHBOARD.user.new },
{ title: 'edit', path: PATH_DASHBOARD.user.demoEdit },
{ title: 'account', path: PATH_DASHBOARD.user.account },
],
},
// E-COMMERCE
{ {
title: 'ecommerce', title: 'Spoken language Translation Module',
path: PATH_DASHBOARD.eCommerce.root, path: PATH_DASHBOARD.spokenLanguageTranslationModule.root,
icon: ICONS.cart,
children: [
{ title: 'shop', path: PATH_DASHBOARD.eCommerce.shop },
{ title: 'product', path: PATH_DASHBOARD.eCommerce.demoView },
{ title: 'list', path: PATH_DASHBOARD.eCommerce.list },
{ title: 'create', path: PATH_DASHBOARD.eCommerce.new },
{ title: 'edit', path: PATH_DASHBOARD.eCommerce.demoEdit },
{ title: 'checkout', path: PATH_DASHBOARD.eCommerce.checkout },
],
},
// INVOICE
{
title: 'invoice',
path: PATH_DASHBOARD.invoice.root,
icon: ICONS.invoice,
children: [
{ title: 'list', path: PATH_DASHBOARD.invoice.list },
{ title: 'details', path: PATH_DASHBOARD.invoice.demoView },
{ title: 'create', path: PATH_DASHBOARD.invoice.new },
{ title: 'edit', path: PATH_DASHBOARD.invoice.demoEdit },
],
},
// BLOG
{
title: 'blog',
path: PATH_DASHBOARD.blog.root,
icon: ICONS.blog,
children: [
{ title: 'posts', path: PATH_DASHBOARD.blog.posts },
{ title: 'post', path: PATH_DASHBOARD.blog.demoView },
{ title: 'create', path: PATH_DASHBOARD.blog.new },
],
},
{
title: 'File manager',
path: PATH_DASHBOARD.fileManager,
icon: ICONS.folder,
},
],
},
// APP
// ----------------------------------------------------------------------
{
subheader: 'app',
items: [
{
title: 'mail',
path: PATH_DASHBOARD.mail.root,
icon: ICONS.mail,
info: <Label color="error">+32</Label>,
},
{
title: 'chat',
path: PATH_DASHBOARD.chat.root,
icon: ICONS.chat, icon: ICONS.chat,
},
{
title: 'calendar',
path: PATH_DASHBOARD.calendar,
icon: ICONS.calendar,
},
{
title: 'kanban',
path: PATH_DASHBOARD.kanban,
icon: ICONS.kanban,
},
],
},
// DEMO MENU STATES
{
subheader: 'Other cases',
items: [
{
// default roles : All roles can see this entry.
// roles: ['user'] Only users can see this item.
// roles: ['admin'] Only admin can see this item.
// roles: ['admin', 'manager'] Only admin/manager can see this item.
// Reference from 'src/guards/RoleBasedGuard'.
title: 'item_by_roles',
path: PATH_DASHBOARD.permissionDenied,
icon: ICONS.lock,
roles: ['admin'],
caption: 'only_admin_can_see_this_item',
},
{
title: 'menu_level',
path: '#/dashboard/menu_level',
icon: ICONS.menuItem,
children: [ children: [
{ { title: 'Home', path: PATH_DASHBOARD.spokenLanguageTranslationModule.spokenLanguageTranslationHome },
title: 'menu_level_2a', ],
path: '#/dashboard/menu_level/menu_level_2a',
},
{
title: 'menu_level_2b',
path: '#/dashboard/menu_level/menu_level_2b',
children: [
{
title: 'menu_level_3a',
path: '#/dashboard/menu_level/menu_level_2b/menu_level_3a',
}, },
// Sign Language Learning Module items
{ {
title: 'menu_level_3b', title: 'Learning Module',
path: '#/dashboard/menu_level/menu_level_2b/menu_level_3b', path: PATH_DASHBOARD.learningModule.root,
icon: ICONS.learning,
children: [ children: [
{ { title: 'Curriculum', path: PATH_DASHBOARD.learningModule.curriculumHome },
title: 'menu_level_4a', { title: 'Question and Answers', path: PATH_DASHBOARD.learningModule.questionAndAnswersHome },
path: '#/dashboard/menu_level/menu_level_2b/menu_level_3b/menu_level_4a', { title: 'Lead Board', path: PATH_DASHBOARD.learningModule.leadBoardHome },
}, { title: 'Feedback', path: PATH_DASHBOARD.learningModule.feedbackHome },
{
title: 'menu_level_4b',
path: '#/dashboard/menu_level/menu_level_2b/menu_level_3b/menu_level_4b',
},
],
},
], ],
}, },
], ],
}, },
{
title: 'item_disabled',
path: '#disabled',
icon: ICONS.disabled,
disabled: true,
},
{
title: 'item_label',
path: '#label',
icon: ICONS.label,
info: (
<Label color="info" startIcon={<Iconify icon="eva:email-fill" />}>
NEW
</Label>
),
},
{
title: 'item_caption',
path: '#caption',
icon: ICONS.menuItem,
caption:
'Quisque malesuada placerat nisl. In hac habitasse platea dictumst. Cras id dui. Pellentesque commodo eros a enim. Morbi mollis tellus ac sapien.',
},
{
title: 'item_external_link',
path: 'https://www.google.com/',
icon: ICONS.external,
},
{
title: 'blank',
path: PATH_DASHBOARD.blank,
icon: ICONS.blank,
},
],
},
]; ];
export default navConfig; export default navConfig;
Project/Frontend/Web_App/src/layouts/main/Footer.tsx
View file @ 39909344
...@@ -97,7 +97,7 @@ export default function Footer() { ...@@ -97,7 +97,7 @@ export default function Footer() {
<Grid item xs={8} md={3}> <Grid item xs={8} md={3}>
<Typography variant="body2" sx={{ pr: { md: 5 } }}> <Typography variant="body2" sx={{ pr: { md: 5 } }}>
The starting point for your next project with Minimal UI Kit, built on the newest The starting point for your next project with SignConnect+ Kit, built on the newest
version of Material-UI ©, ready to be customized to your style. version of Material-UI ©, ready to be customized to your style.
</Typography> </Typography>
......
Project/Frontend/Web_App/src/pages/403.tsx
View file @ 39909344
...@@ -21,7 +21,7 @@ export default function Page403() { ...@@ -21,7 +21,7 @@ export default function Page403() {
return ( return (
<> <>
<Head> <Head>
<title> 403 Forbidden | Minimal UI</title> <title> 403 Forbidden | SignConnect+</title>
</Head> </Head>
<MotionContainer> <MotionContainer>
......
Project/Frontend/Web_App/src/pages/404.tsx
View file @ 39909344
...@@ -21,7 +21,7 @@ export default function Page404() { ...@@ -21,7 +21,7 @@ export default function Page404() {
return ( return (
<> <>
<Head> <Head>
<title> 404 Page Not Found | Minimal UI</title> <title> 404 Page Not Found | SignConnect+</title>
</Head> </Head>
<MotionContainer> <MotionContainer>
......
Project/Frontend/Web_App/src/pages/500.tsx
View file @ 39909344
...@@ -21,7 +21,7 @@ export default function Page500() { ...@@ -21,7 +21,7 @@ export default function Page500() {
return ( return (
<> <>
<Head> <Head>
<title> 500 Internal Server Error | Minimal UI</title> <title> 500 Internal Server Error | SignConnect+</title>
</Head> </Head>
<MotionContainer> <MotionContainer>
......
Project/Frontend/Web_App/src/pages/_document.tsx
View file @ 39909344
...@@ -45,10 +45,10 @@ export default class MyDocument extends Document { ...@@ -45,10 +45,10 @@ export default class MyDocument extends Document {
{/* Meta */} {/* Meta */}
<meta <meta
name="description" name="description"
content="The starting point for your next project with Minimal UI Kit, built on the newest version of Material-UI ©, ready to be customized to your style" content="The starting point for your next project with SignConnect+ Kit, built on the newest version of Material-UI ©, ready to be customized to your style"
/> />
<meta name="keywords" content="react,material,kit,application,dashboard,admin,template" /> <meta name="keywords" content="react,material,kit,application,dashboard,admin,template" />
<meta name="author" content="Minimal UI Kit" /> <meta name="author" content="SignConnect+ Kit" />
</Head> </Head>
<body> <body>
......
Project/Frontend/Web_App/src/pages/about-us.tsx
View file @ 39909344
...@@ -17,7 +17,7 @@ export default function AboutPage() { ...@@ -17,7 +17,7 @@ export default function AboutPage() {
return ( return (
<> <>
<Head> <Head>
<title> About us | Minimal UI</title> <title> About us | SignConnect+</title>
</Head> </Head>
<AboutHero /> <AboutHero />
......
Project/Frontend/Web_App/src/pages/auth/login-unprotected.tsx
View file @ 39909344
...@@ -9,7 +9,7 @@ export default function LoginUnprotectedPage() { ...@@ -9,7 +9,7 @@ export default function LoginUnprotectedPage() {
return ( return (
<> <>
<Head> <Head>
<title> Login Unprotected | Minimal UI</title> <title> Login Unprotected | SignConnect+</title>
</Head> </Head>
<Login /> <Login />
......
Project/Frontend/Web_App/src/pages/auth/login.tsx
View file @ 39909344
...@@ -12,7 +12,7 @@ export default function LoginPage() { ...@@ -12,7 +12,7 @@ export default function LoginPage() {
return ( return (
<> <>
<Head> <Head>
<title> Login | Minimal UI</title> <title> Login | SignConnect+</title>
</Head> </Head>
<GuestGuard> <GuestGuard>
......
Project/Frontend/Web_App/src/pages/auth/new-password.tsx
View file @ 39909344
...@@ -24,7 +24,7 @@ export default function NewPasswordPage() { ...@@ -24,7 +24,7 @@ export default function NewPasswordPage() {
return ( return (
<> <>
<Head> <Head>
<title> New Password | Minimal UI</title> <title> New Password | SignConnect+</title>
</Head> </Head>
<SentIcon sx={{ mb: 5, height: 96 }} /> <SentIcon sx={{ mb: 5, height: 96 }} />
......
Project/Frontend/Web_App/src/pages/auth/register-unprotected.tsx
View file @ 39909344
...@@ -9,7 +9,7 @@ export default function RegisterUnprotectedPage() { ...@@ -9,7 +9,7 @@ export default function RegisterUnprotectedPage() {
return ( return (
<> <>
<Head> <Head>
<title> Register Unprotected | Minimal UI</title> <title> Register Unprotected | SignConnect+</title>
</Head> </Head>
<Register /> <Register />
......
Project/Frontend/Web_App/src/pages/auth/register.tsx
View file @ 39909344
// next // next
import Head from 'next/head'; import Head from 'next/head';
// auth // auth
import GuestGuard from '../../auth/GuestGuard';
// sections // sections
import Register from '../../sections/auth/Register'; import Register from '../../sections/auth/Register';
...@@ -11,12 +10,10 @@ export default function RegisterPage() { ...@@ -11,12 +10,10 @@ export default function RegisterPage() {
return ( return (
<> <>
<Head> <Head>
<title> Register | Minimal UI</title> <title> Register | SignConnect+</title>
</Head> </Head>
<GuestGuard>
<Register /> <Register />
</GuestGuard>
</> </>
); );
} }
Project/Frontend/Web_App/src/pages/auth/reset-password.tsx
View file @ 39909344
...@@ -24,7 +24,7 @@ export default function ResetPasswordPage() { ...@@ -24,7 +24,7 @@ export default function ResetPasswordPage() {
return ( return (
<> <>
<Head> <Head>
<title> Reset Password | Minimal UI</title> <title> Reset Password | SignConnect+</title>
</Head> </Head>
<PasswordIcon sx={{ mb: 5, height: 96 }} /> <PasswordIcon sx={{ mb: 5, height: 96 }} />
......
Project/Frontend/Web_App/src/pages/auth/verify.tsx
View file @ 39909344
...@@ -24,7 +24,7 @@ export default function VerifyCodePage() { ...@@ -24,7 +24,7 @@ export default function VerifyCodePage() {
return ( return (
<> <>
<Head> <Head>
<title> Verify Code | Minimal UI</title> <title> Verify Code | SignConnect+</title>
</Head> </Head>
<EmailInboxIcon sx={{ mb: 5, height: 96 }} /> <EmailInboxIcon sx={{ mb: 5, height: 96 }} />
......
Project/Frontend/Web_App/src/pages/coming-soon.tsx
View file @ 39909344
...@@ -27,7 +27,7 @@ export default function ComingSoonPage() { ...@@ -27,7 +27,7 @@ export default function ComingSoonPage() {
return ( return (
<> <>
<Head> <Head>
<title> Coming Soon | Minimal UI</title> <title> Coming Soon | SignConnect+</title>
</Head> </Head>
<Typography variant="h3" paragraph> <Typography variant="h3" paragraph>
......
Project/Frontend/Web_App/src/pages/components/extra/animate.tsx
View file @ 39909344
...@@ -40,7 +40,7 @@ export default function DemoAnimatePage() { ...@@ -40,7 +40,7 @@ export default function DemoAnimatePage() {
return ( return (
<> <>
<Head> <Head>
<title> Extra Components: Animate | Minimal UI</title> <title> Extra Components: Animate | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/extra/carousel.tsx
View file @ 39909344
...@@ -41,7 +41,7 @@ export default function DemoCarouselsPage() { ...@@ -41,7 +41,7 @@ export default function DemoCarouselsPage() {
return ( return (
<> <>
<Head> <Head>
<title> Extra Components: Carousels | Minimal UI</title> <title> Extra Components: Carousels | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/extra/chart.tsx
View file @ 39909344
...@@ -34,7 +34,7 @@ export default function DemoChartsPage() { ...@@ -34,7 +34,7 @@ export default function DemoChartsPage() {
return ( return (
<> <>
<Head> <Head>
<title> Extra Components: Charts | Minimal UI</title> <title> Extra Components: Charts | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/extra/copy-to-clipboard.tsx
View file @ 39909344
...@@ -65,7 +65,7 @@ export default function DemoCopyToClipboardPage() { ...@@ -65,7 +65,7 @@ export default function DemoCopyToClipboardPage() {
return ( return (
<> <>
<Head> <Head>
<title> Extra Components: Copy To Clipboard | Minimal UI</title> <title> Extra Components: Copy To Clipboard | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/extra/editor.tsx
View file @ 39909344
...@@ -35,7 +35,7 @@ export default function DemoEditorPage() { ...@@ -35,7 +35,7 @@ export default function DemoEditorPage() {
return ( return (
<> <>
<Head> <Head>
<title> Extra Components: Editor | Minimal UI</title> <title> Extra Components: Editor | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/extra/form-validation.tsx
View file @ 39909344
...@@ -21,7 +21,7 @@ export default function DemoFormValidationPage() { ...@@ -21,7 +21,7 @@ export default function DemoFormValidationPage() {
return ( return (
<> <>
<Head> <Head>
<title> Extra Components: Form Validation | Minimal UI</title> <title> Extra Components: Form Validation | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/extra/image.tsx
View file @ 39909344
...@@ -29,7 +29,7 @@ export default function DemoImagePage() { ...@@ -29,7 +29,7 @@ export default function DemoImagePage() {
return ( return (
<> <>
<Head> <Head>
<title> Extra Components: Image | Minimal UI</title> <title> Extra Components: Image | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/extra/label.tsx
View file @ 39909344
...@@ -26,7 +26,7 @@ export default function DemoLabelPage() { ...@@ -26,7 +26,7 @@ export default function DemoLabelPage() {
return ( return (
<> <>
<Head> <Head>
<title> Extra Components: Label | Minimal UI</title> <title> Extra Components: Label | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/extra/lightbox.tsx
View file @ 39909344
...@@ -42,7 +42,7 @@ export default function DemoLightboxPage() { ...@@ -42,7 +42,7 @@ export default function DemoLightboxPage() {
return ( return (
<> <>
<Head> <Head>
<title> Extra Components: Lightbox | Minimal UI</title> <title> Extra Components: Lightbox | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/extra/map.tsx
View file @ 39909344
...@@ -74,7 +74,7 @@ export default function DemoMapPage() { ...@@ -74,7 +74,7 @@ export default function DemoMapPage() {
return ( return (
<> <>
<Head> <Head>
<title> Extra Components: Map | Minimal UI</title> <title> Extra Components: Map | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/extra/mega-menu.tsx
View file @ 39909344
...@@ -30,7 +30,7 @@ export default function DemoMegaMenuPage() { ...@@ -30,7 +30,7 @@ export default function DemoMegaMenuPage() {
return ( return (
<> <>
<Head> <Head>
<title> Extra Components: Mega Menu | Minimal UI</title> <title> Extra Components: Mega Menu | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/extra/multi-language.tsx
View file @ 39909344
...@@ -60,7 +60,7 @@ export default function DemoMultiLanguagePage() { ...@@ -60,7 +60,7 @@ export default function DemoMultiLanguagePage() {
return ( return (
<> <>
<Head> <Head>
<title> Extra Components: Multi Language | Minimal UI</title> <title> Extra Components: Multi Language | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/extra/navigation-bar.tsx
View file @ 39909344
...@@ -27,7 +27,7 @@ export default function DemoNavigationBarPage() { ...@@ -27,7 +27,7 @@ export default function DemoNavigationBarPage() {
return ( return (
<> <>
<Head> <Head>
<title> Extra Components: Navigation Bar | Minimal UI</title> <title> Extra Components: Navigation Bar | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/extra/organization-chart.tsx
View file @ 39909344
...@@ -29,7 +29,7 @@ export default function DemoOrganizationalChartPage() { ...@@ -29,7 +29,7 @@ export default function DemoOrganizationalChartPage() {
return ( return (
<> <>
<Head> <Head>
<title> Extra Components: Organizational Chart | Minimal UI</title> <title> Extra Components: Organizational Chart | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/extra/scroll.tsx
View file @ 39909344
...@@ -20,7 +20,7 @@ export default function DemoScrollbarPage() { ...@@ -20,7 +20,7 @@ export default function DemoScrollbarPage() {
return ( return (
<> <>
<Head> <Head>
<title> Extra Components: Scrollbar | Minimal UI</title> <title> Extra Components: Scrollbar | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/extra/snackbar.tsx
View file @ 39909344
...@@ -59,7 +59,7 @@ export default function DemoSnackbarPage() { ...@@ -59,7 +59,7 @@ export default function DemoSnackbarPage() {
return ( return (
<> <>
<Head> <Head>
<title> Extra Components: Snackbar | Minimal UI</title> <title> Extra Components: Snackbar | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/extra/text-max-line.tsx
View file @ 39909344
...@@ -21,7 +21,7 @@ export default function DemoTextMaxLinePage() { ...@@ -21,7 +21,7 @@ export default function DemoTextMaxLinePage() {
return ( return (
<> <>
<Head> <Head>
<title> Extra Components: Text Max Line | Minimal UI</title> <title> Extra Components: Text Max Line | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/extra/upload.tsx
View file @ 39909344
...@@ -87,7 +87,7 @@ export default function DemoUploadPage() { ...@@ -87,7 +87,7 @@ export default function DemoUploadPage() {
return ( return (
<> <>
<Head> <Head>
<title> Extra Components: Upload | Minimal UI</title> <title> Extra Components: Upload | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/foundation/colors.tsx
View file @ 39909344
...@@ -54,7 +54,7 @@ export default function FoundationColorsPage() { ...@@ -54,7 +54,7 @@ export default function FoundationColorsPage() {
return ( return (
<> <>
<Head> <Head>
<title> Foundations: Colors | Minimal UI</title> <title> Foundations: Colors | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/foundation/grid.tsx
View file @ 39909344
...@@ -60,7 +60,7 @@ export default function FoundationGridPage() { ...@@ -60,7 +60,7 @@ export default function FoundationGridPage() {
return ( return (
<> <>
<Head> <Head>
<title> Foundations: Grid | Minimal UI</title> <title> Foundations: Grid | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/foundation/icons.tsx
View file @ 39909344
...@@ -33,7 +33,7 @@ export default function FoundationIconsPage() { ...@@ -33,7 +33,7 @@ export default function FoundationIconsPage() {
return ( return (
<> <>
<Head> <Head>
<title> Foundations: Icons | Minimal UI</title> <title> Foundations: Icons | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/foundation/shadows.tsx
View file @ 39909344
...@@ -51,7 +51,7 @@ export default function FoundationShadowsPage() { ...@@ -51,7 +51,7 @@ export default function FoundationShadowsPage() {
return ( return (
<> <>
<Head> <Head>
<title> Foundations: Shadows | Minimal UI</title> <title> Foundations: Shadows | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/foundation/typography.tsx
View file @ 39909344
...@@ -55,7 +55,7 @@ export default function FoundationTypographyPage() { ...@@ -55,7 +55,7 @@ export default function FoundationTypographyPage() {
return ( return (
<> <>
<Head> <Head>
<title> Foundations: Typography | Minimal UI</title> <title> Foundations: Typography | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/index.tsx
View file @ 39909344
...@@ -19,7 +19,7 @@ export default function ComponentsOverviewPage() { ...@@ -19,7 +19,7 @@ export default function ComponentsOverviewPage() {
return ( return (
<> <>
<Head> <Head>
<title> Components Overview | Minimal UI</title> <title> Components Overview | SignConnect+</title>
</Head> </Head>
<ComponentHero /> <ComponentHero />
......
Project/Frontend/Web_App/src/pages/components/mui/accordion.tsx
View file @ 39909344
...@@ -50,7 +50,7 @@ export default function MUIAccordionPage() { ...@@ -50,7 +50,7 @@ export default function MUIAccordionPage() {
return ( return (
<> <>
<Head> <Head>
<title> MUI Components: Accordion | Minimal UI</title> <title> MUI Components: Accordion | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/mui/alert.tsx
View file @ 39909344
...@@ -36,7 +36,7 @@ export default function MUIAlertPage() { ...@@ -36,7 +36,7 @@ export default function MUIAlertPage() {
return ( return (
<> <>
<Head> <Head>
<title> MUI Components: Alert | Minimal UI</title> <title> MUI Components: Alert | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/mui/autocomplete.tsx
View file @ 39909344
...@@ -47,7 +47,7 @@ export default function MUIAutocompletePage() { ...@@ -47,7 +47,7 @@ export default function MUIAutocompletePage() {
return ( return (
<> <>
<Head> <Head>
<title> MUI Components: Autocomplete | Minimal UI</title> <title> MUI Components: Autocomplete | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/mui/avatar.tsx
View file @ 39909344
...@@ -40,7 +40,7 @@ export default function MUIAvatarPage() { ...@@ -40,7 +40,7 @@ export default function MUIAvatarPage() {
return ( return (
<> <>
<Head> <Head>
<title> MUI Components: Avatar | Minimal UI</title> <title> MUI Components: Avatar | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/mui/badge.tsx
View file @ 39909344
...@@ -23,7 +23,7 @@ export default function MUIBadgePage() { ...@@ -23,7 +23,7 @@ export default function MUIBadgePage() {
return ( return (
<> <>
<Head> <Head>
<title> MUI Components: Badge | Minimal UI</title> <title> MUI Components: Badge | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/mui/breadcrumbs.tsx
View file @ 39909344
...@@ -22,7 +22,7 @@ export default function MUIBreadcrumbsPage() { ...@@ -22,7 +22,7 @@ export default function MUIBreadcrumbsPage() {
return ( return (
<> <>
<Head> <Head>
<title> MUI Components: Breadcrumbs | Minimal UI</title> <title> MUI Components: Breadcrumbs | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/mui/buttons.tsx
View file @ 39909344
...@@ -49,7 +49,7 @@ export default function MUIButtonsPage() { ...@@ -49,7 +49,7 @@ export default function MUIButtonsPage() {
return ( return (
<> <>
<Head> <Head>
<title> MUI Components: Buttons | Minimal UI</title> <title> MUI Components: Buttons | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/mui/checkbox.tsx
View file @ 39909344
...@@ -52,7 +52,7 @@ export default function MUICheckboxPage() { ...@@ -52,7 +52,7 @@ export default function MUICheckboxPage() {
return ( return (
<> <>
<Head> <Head>
<title> MUI Components: Checkbox | Minimal UI</title> <title> MUI Components: Checkbox | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/mui/chip.tsx
View file @ 39909344
...@@ -21,7 +21,7 @@ export default function MUIChipPage() { ...@@ -21,7 +21,7 @@ export default function MUIChipPage() {
return ( return (
<> <>
<Head> <Head>
<title> MUI Components: Chip | Minimal UI</title> <title> MUI Components: Chip | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/mui/data-grid.tsx
View file @ 39909344
...@@ -40,7 +40,7 @@ export default function MUIDataGridPage() { ...@@ -40,7 +40,7 @@ export default function MUIDataGridPage() {
return ( return (
<> <>
<Head> <Head>
<title> MUI Components: DataGrid | Minimal UI</title> <title> MUI Components: DataGrid | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/mui/dialog.tsx
View file @ 39909344
...@@ -37,7 +37,7 @@ export default function MUIDialogPage() { ...@@ -37,7 +37,7 @@ export default function MUIDialogPage() {
return ( return (
<> <>
<Head> <Head>
<title> MUI Components: Dialog | Minimal UI</title> <title> MUI Components: Dialog | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/mui/list.tsx
View file @ 39909344
...@@ -97,7 +97,7 @@ export default function MUIListPage() { ...@@ -97,7 +97,7 @@ export default function MUIListPage() {
return ( return (
<> <>
<Head> <Head>
<title> MUI Components: List | Minimal UI</title> <title> MUI Components: List | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/mui/menu.tsx
View file @ 39909344
...@@ -98,7 +98,7 @@ export default function MUIMenuPage() { ...@@ -98,7 +98,7 @@ export default function MUIMenuPage() {
return ( return (
<> <>
<Head> <Head>
<title> MUI Components: Menu | Minimal UI</title> <title> MUI Components: Menu | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/mui/pagination.tsx
View file @ 39909344
...@@ -60,7 +60,7 @@ export default function MUIPaginationPage() { ...@@ -60,7 +60,7 @@ export default function MUIPaginationPage() {
return ( return (
<> <>
<Head> <Head>
<title> MUI Components: Pagination | Minimal UI</title> <title> MUI Components: Pagination | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/mui/pickers.tsx
View file @ 39909344
...@@ -36,7 +36,7 @@ export default function MUIPickersPage() { ...@@ -36,7 +36,7 @@ export default function MUIPickersPage() {
return ( return (
<> <>
<Head> <Head>
<title> MUI Components: Pickers | Minimal UI</title> <title> MUI Components: Pickers | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/mui/popover.tsx
View file @ 39909344
...@@ -73,7 +73,7 @@ export default function MUIPopoverPage() { ...@@ -73,7 +73,7 @@ export default function MUIPopoverPage() {
return ( return (
<> <>
<Head> <Head>
<title> MUI Components: Popover | Minimal UI</title> <title> MUI Components: Popover | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/mui/progress.tsx
View file @ 39909344
...@@ -70,7 +70,7 @@ export default function MUIProgressPage() { ...@@ -70,7 +70,7 @@ export default function MUIProgressPage() {
return ( return (
<> <>
<Head> <Head>
<title> MUI Components: Progress | Minimal UI</title> <title> MUI Components: Progress | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/mui/radio-button.tsx
View file @ 39909344
...@@ -43,7 +43,7 @@ export default function MUIRadioButtonsPage() { ...@@ -43,7 +43,7 @@ export default function MUIRadioButtonsPage() {
return ( return (
<> <>
<Head> <Head>
<title> MUI Components: Radio Buttons | Minimal UI</title> <title> MUI Components: Radio Buttons | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/mui/rating.tsx
View file @ 39909344
...@@ -88,7 +88,7 @@ export default function MUIRatingPage() { ...@@ -88,7 +88,7 @@ export default function MUIRatingPage() {
return ( return (
<> <>
<Head> <Head>
<title> MUI Components: Rating | Minimal UI</title> <title> MUI Components: Rating | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/mui/slider.tsx
View file @ 39909344
...@@ -79,7 +79,7 @@ export default function MUISliderPage() { ...@@ -79,7 +79,7 @@ export default function MUISliderPage() {
return ( return (
<> <>
<Head> <Head>
<title> MUI Components: Slider | Minimal UI</title> <title> MUI Components: Slider | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/mui/stepper.tsx
View file @ 39909344
...@@ -25,7 +25,7 @@ export default function MUIStepperPage() { ...@@ -25,7 +25,7 @@ export default function MUIStepperPage() {
return ( return (
<> <>
<Head> <Head>
<title> MUI Components: Stepper | Minimal UI</title> <title> MUI Components: Stepper | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/mui/switch.tsx
View file @ 39909344
...@@ -36,7 +36,7 @@ export default function MUISwitchPage() { ...@@ -36,7 +36,7 @@ export default function MUISwitchPage() {
return ( return (
<> <>
<Head> <Head>
<title> MUI Components: Switch | Minimal UI</title> <title> MUI Components: Switch | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/mui/table.tsx
View file @ 39909344
...@@ -24,7 +24,7 @@ export default function MUITablePage() { ...@@ -24,7 +24,7 @@ export default function MUITablePage() {
return ( return (
<> <>
<Head> <Head>
<title> MUI Components: Table | Minimal UI</title> <title> MUI Components: Table | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/mui/tabs.tsx
View file @ 39909344
...@@ -78,7 +78,7 @@ export default function MUITabsPage() { ...@@ -78,7 +78,7 @@ export default function MUITabsPage() {
return ( return (
<> <>
<Head> <Head>
<title> MUI Components: Tabs | Minimal UI</title> <title> MUI Components: Tabs | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/mui/textfield.tsx
View file @ 39909344
...@@ -32,7 +32,7 @@ export default function MUITextFieldPage() { ...@@ -32,7 +32,7 @@ export default function MUITextFieldPage() {
return ( return (
<> <>
<Head> <Head>
<title> MUI Components: Textfield | Minimal UI</title> <title> MUI Components: Textfield | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/mui/timeline.tsx
View file @ 39909344
...@@ -106,7 +106,7 @@ export default function MUITimelinePage() { ...@@ -106,7 +106,7 @@ export default function MUITimelinePage() {
return ( return (
<> <>
<Head> <Head>
<title> MUI Components: Timeline | Minimal UI</title> <title> MUI Components: Timeline | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/mui/tooltip.tsx
View file @ 39909344
...@@ -40,7 +40,7 @@ export default function MUITooltipPage() { ...@@ -40,7 +40,7 @@ export default function MUITooltipPage() {
return ( return (
<> <>
<Head> <Head>
<title> MUI Components: Tooltip | Minimal UI</title> <title> MUI Components: Tooltip | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/mui/transfer-list.tsx
View file @ 39909344
...@@ -32,7 +32,7 @@ export default function MUITransferListPage() { ...@@ -32,7 +32,7 @@ export default function MUITransferListPage() {
return ( return (
<> <>
<Head> <Head>
<title> MUI Components: Transfer List | Minimal UI</title> <title> MUI Components: Transfer List | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/components/mui/tree-view.tsx
View file @ 39909344
...@@ -45,7 +45,7 @@ export default function MUITreesViewPage() { ...@@ -45,7 +45,7 @@ export default function MUITreesViewPage() {
return ( return (
<> <>
<Head> <Head>
<title> MUI Components: Tree View | Minimal UI</title> <title> MUI Components: Tree View | SignConnect+</title>
</Head> </Head>
<Box <Box
......
Project/Frontend/Web_App/src/pages/contact-us.tsx
View file @ 39909344
...@@ -19,7 +19,7 @@ export default function ContactPage() { ...@@ -19,7 +19,7 @@ export default function ContactPage() {
return ( return (
<> <>
<Head> <Head>
<title> Contact us | Minimal UI</title> <title> Contact us | SignConnect+</title>
</Head> </Head>
<ContactHero /> <ContactHero />
......
Project/Frontend/Web_App/src/pages/dashboard/analytics.tsx
View file @ 39909344
...@@ -38,7 +38,7 @@ export default function GeneralAnalyticsPage() { ...@@ -38,7 +38,7 @@ export default function GeneralAnalyticsPage() {
return ( return (
<> <>
<Head> <Head>
<title> General: Analytics | Minimal UI</title> <title> General: Analytics | SignConnect+</title>
</Head> </Head>
<Container maxWidth={themeStretch ? false : 'xl'}> <Container maxWidth={themeStretch ? false : 'xl'}>
......
Project/Frontend/Web_App/src/pages/dashboard/app.tsx
View file @ 39909344
...@@ -49,7 +49,7 @@ export default function GeneralAppPage() { ...@@ -49,7 +49,7 @@ export default function GeneralAppPage() {
return ( return (
<> <>
<Head> <Head>
<title> General: App | Minimal UI</title> <title> General: App | SignConnect+</title>
</Head> </Head>
<Container maxWidth={themeStretch ? false : 'xl'}> <Container maxWidth={themeStretch ? false : 'xl'}>
......
Project/Frontend/Web_App/src/pages/dashboard/banking.tsx
View file @ 39909344
...@@ -40,7 +40,7 @@ export default function GeneralBankingPage() { ...@@ -40,7 +40,7 @@ export default function GeneralBankingPage() {
return ( return (
<> <>
<Head> <Head>
<title> General: Banking | Minimal UI</title> <title> General: Banking | SignConnect+</title>
</Head> </Head>
<Container maxWidth={themeStretch ? false : 'xl'}> <Container maxWidth={themeStretch ? false : 'xl'}>
......
Project/Frontend/Web_App/src/pages/dashboard/blank.tsx
View file @ 39909344
...@@ -18,7 +18,7 @@ export default function BlankPage() { ...@@ -18,7 +18,7 @@ export default function BlankPage() {
return ( return (
<> <>
<Head> <Head>
<title> Blank Page | Minimal UI</title> <title> Blank Page | SignConnect+</title>
</Head> </Head>
<Container maxWidth={themeStretch ? false : 'xl'}> <Container maxWidth={themeStretch ? false : 'xl'}>
......
Project/Frontend/Web_App/src/pages/dashboard/blog/new.tsx
View file @ 39909344
...@@ -24,7 +24,7 @@ export default function BlogNewPostPage() { ...@@ -24,7 +24,7 @@ export default function BlogNewPostPage() {
return ( return (
<> <>
<Head> <Head>
<title> Blog: New Post | Minimal UI</title> <title> Blog: New Post | SignConnect+</title>
</Head> </Head>
<Container maxWidth={themeStretch ? false : 'lg'}> <Container maxWidth={themeStretch ? false : 'lg'}>
......
Project/Frontend/Web_App/src/pages/dashboard/blog/post/[title].tsx
View file @ 39909344
...@@ -87,7 +87,7 @@ export default function BlogPostPage() { ...@@ -87,7 +87,7 @@ export default function BlogPostPage() {
return ( return (
<> <>
<Head> <Head>
<title>{`Blog: ${post?.title || ''} | Minimal UI`}</title> <title>{`Blog: ${post?.title || ''} | SignConnect+`}</title>
</Head> </Head>
<Container maxWidth={themeStretch ? false : 'lg'}> <Container maxWidth={themeStretch ? false : 'lg'}>
......
Project/Frontend/Web_App/src/pages/dashboard/blog/posts.tsx
View file @ 39909344
...@@ -64,7 +64,7 @@ export default function BlogPostsPage() { ...@@ -64,7 +64,7 @@ export default function BlogPostsPage() {
return ( return (
<> <>
<Head> <Head>
<title> Blog: Posts | Minimal UI</title> <title> Blog: Posts | SignConnect+</title>
</Head> </Head>
<Container maxWidth={themeStretch ? false : 'lg'}> <Container maxWidth={themeStretch ? false : 'lg'}>
......
Project/Frontend/Web_App/src/pages/dashboard/booking.tsx
View file @ 39909344
...@@ -43,7 +43,7 @@ export default function GeneralBookingPage() { ...@@ -43,7 +43,7 @@ export default function GeneralBookingPage() {
return ( return (
<> <>
<Head> <Head>
<title> General: Booking | Minimal UI</title> <title> General: Booking | SignConnect+</title>
</Head> </Head>
<Container maxWidth={themeStretch ? false : 'xl'}> <Container maxWidth={themeStretch ? false : 'xl'}>
......
Project/Frontend/Web_App/src/pages/dashboard/calendar.tsx
View file @ 39909344
...@@ -253,7 +253,7 @@ export default function CalendarPage() { ...@@ -253,7 +253,7 @@ export default function CalendarPage() {
return ( return (
<> <>
<Head> <Head>
<title> Calendar | Minimal UI</title> <title> Calendar | SignConnect+</title>
</Head> </Head>
<Container maxWidth={themeStretch ? false : 'xl'}> <Container maxWidth={themeStretch ? false : 'xl'}>
......
Project/Frontend/Web_App/src/pages/dashboard/chat/[conversationKey].tsx
View file @ 39909344
...@@ -15,7 +15,7 @@ export default function ChatPage() { ...@@ -15,7 +15,7 @@ export default function ChatPage() {
return ( return (
<> <>
<Head> <Head>
<title> Chat | Minimal UI</title> <title> Chat | SignConnect+</title>
</Head> </Head>
<Chat /> <Chat />
......
Project/Frontend/Web_App/src/pages/dashboard/chat/index.tsx
View file @ 39909344
...@@ -15,7 +15,7 @@ export default function ChatPage() { ...@@ -15,7 +15,7 @@ export default function ChatPage() {
return ( return (
<> <>
<Head> <Head>
<title> Chat | Minimal UI</title> <title> Chat | SignConnect+</title>
</Head> </Head>
<Chat /> <Chat />
......
Project/Frontend/Web_App/src/pages/dashboard/chat/new.tsx
View file @ 39909344
...@@ -15,7 +15,7 @@ export default function ChatPage() { ...@@ -15,7 +15,7 @@ export default function ChatPage() {
return ( return (
<> <>
<Head> <Head>
<title> Chat | Minimal UI</title> <title> Chat | SignConnect+</title>
</Head> </Head>
<Chat /> <Chat />
......
Project/Frontend/Web_App/src/pages/dashboard/e-commerce/checkout.tsx
View file @ 39909344
...@@ -121,7 +121,7 @@ export default function EcommerceCheckoutPage() { ...@@ -121,7 +121,7 @@ export default function EcommerceCheckoutPage() {
return ( return (
<> <>
<Head> <Head>
<title> Ecommerce: Checkout | Minimal UI</title> <title> Ecommerce: Checkout | SignConnect+</title>
</Head> </Head>
<Container maxWidth={themeStretch ? false : 'lg'}> <Container maxWidth={themeStretch ? false : 'lg'}>
......
Project/Frontend/Web_App/src/pages/dashboard/e-commerce/list.tsx
View file @ 39909344
...@@ -198,7 +198,7 @@ export default function EcommerceProductListPage() { ...@@ -198,7 +198,7 @@ export default function EcommerceProductListPage() {
return ( return (
<> <>
<Head> <Head>
<title> Ecommerce: Product List | Minimal UI</title> <title> Ecommerce: Product List | SignConnect+</title>
</Head> </Head>
<Container maxWidth={themeStretch ? false : 'lg'}> <Container maxWidth={themeStretch ? false : 'lg'}>
......
Project/Frontend/Web_App/src/pages/dashboard/e-commerce/product/[name].tsx
View file @ 39909344
...@@ -99,7 +99,7 @@ export default function EcommerceProductDetailsPage() { ...@@ -99,7 +99,7 @@ export default function EcommerceProductDetailsPage() {
return ( return (
<> <>
<Head> <Head>
<title>{`Ecommerce: ${product?.name || ''} | Minimal UI`}</title> <title>{`Ecommerce: ${product?.name || ''} | SignConnect+`}</title>
</Head> </Head>
<Container maxWidth={themeStretch ? false : 'lg'}> <Container maxWidth={themeStretch ? false : 'lg'}>
......
Project/Frontend/Web_App/src/pages/dashboard/e-commerce/product/[name]/edit.tsx
View file @ 39909344
...@@ -46,7 +46,7 @@ export default function EcommerceProductEditPage() { ...@@ -46,7 +46,7 @@ export default function EcommerceProductEditPage() {
return ( return (
<> <>
<Head> <Head>
<title> Ecommerce: Edit product | Minimal UI</title> <title> Ecommerce: Edit product | SignConnect+</title>
</Head> </Head>
<Container maxWidth={themeStretch ? false : 'lg'}> <Container maxWidth={themeStretch ? false : 'lg'}>
......
Project/Frontend/Web_App/src/pages/dashboard/e-commerce/product/new.tsx
View file @ 39909344
...@@ -26,7 +26,7 @@ export default function EcommerceProductCreatePage() { ...@@ -26,7 +26,7 @@ export default function EcommerceProductCreatePage() {
return ( return (
<> <>
<Head> <Head>
<title> Ecommerce: Create a new product | Minimal UI</title> <title> Ecommerce: Create a new product | SignConnect+</title>
</Head> </Head>
<Container maxWidth={themeStretch ? false : 'lg'}> <Container maxWidth={themeStretch ? false : 'lg'}>
......
Project/Frontend/Web_App/src/pages/dashboard/e-commerce/shop.tsx
View file @ 39909344
...@@ -96,7 +96,7 @@ export default function EcommerceShopPage() { ...@@ -96,7 +96,7 @@ export default function EcommerceShopPage() {
return ( return (
<> <>
<Head> <Head>
<title> Ecommerce: Shop | Minimal UI</title> <title> Ecommerce: Shop | SignConnect+</title>
</Head> </Head>
<FormProvider methods={methods}> <FormProvider methods={methods}>
......
Project/Frontend/Web_App/src/pages/dashboard/ecommerce.tsx
View file @ 39909344
...@@ -49,7 +49,7 @@ export default function GeneralEcommercePage() { ...@@ -49,7 +49,7 @@ export default function GeneralEcommercePage() {
return ( return (
<> <>
<Head> <Head>
<title> General: E-commerce | Minimal UI</title> <title> General: E-commerce | SignConnect+</title>
</Head> </Head>
<Container maxWidth={themeStretch ? false : 'xl'}> <Container maxWidth={themeStretch ? false : 'xl'}>
......
Project/Frontend/Web_App/src/pages/dashboard/file.tsx
View file @ 39909344
...@@ -135,7 +135,7 @@ export default function GeneralFilePage() { ...@@ -135,7 +135,7 @@ export default function GeneralFilePage() {
return ( return (
<> <>
<Head> <Head>
<title> General: File | Minimal UI</title> <title> General: File | SignConnect+</title>
</Head> </Head>
<Container maxWidth={themeStretch ? false : 'xl'}> <Container maxWidth={themeStretch ? false : 'xl'}>
......
Project/Frontend/Web_App/src/pages/dashboard/files-manager.tsx
View file @ 39909344
...@@ -196,7 +196,7 @@ export default function FileManagerPage() { ...@@ -196,7 +196,7 @@ export default function FileManagerPage() {
return ( return (
<> <>
<Head> <Head>
<title> File Manager | Minimal UI</title> <title> File Manager | SignConnect+</title>
</Head> </Head>
<Container maxWidth={themeStretch ? false : 'lg'}> <Container maxWidth={themeStretch ? false : 'lg'}>
......
Project/Frontend/Web_App/src/pages/dashboard/invoice/[id]/edit.tsx
View file @ 39909344
...@@ -33,7 +33,7 @@ export default function InvoiceEditPage() { ...@@ -33,7 +33,7 @@ export default function InvoiceEditPage() {
return ( return (
<> <>
<Head> <Head>
<title> Invoice: Edit | Minimal UI</title> <title> Invoice: Edit | SignConnect+</title>
</Head> </Head>
<Container maxWidth={themeStretch ? false : 'lg'}> <Container maxWidth={themeStretch ? false : 'lg'}>
......
Project/Frontend/Web_App/src/pages/dashboard/invoice/[id]/index.tsx
View file @ 39909344
...@@ -35,7 +35,7 @@ export default function InvoiceDetailsPage() { ...@@ -35,7 +35,7 @@ export default function InvoiceDetailsPage() {
return ( return (
<> <>
<Head> <Head>
<title> Invoice: View | Minimal UI</title> <title> Invoice: View | SignConnect+</title>
</Head> </Head>
<Container maxWidth={themeStretch ? false : 'lg'}> <Container maxWidth={themeStretch ? false : 'lg'}>
......
Project/Frontend/Web_App/src/pages/dashboard/invoice/list.tsx
View file @ 39909344
...@@ -236,7 +236,7 @@ export default function InvoiceListPage() { ...@@ -236,7 +236,7 @@ export default function InvoiceListPage() {
return ( return (
<> <>
<Head> <Head>
<title> Invoice: List | Minimal UI</title> <title> Invoice: List | SignConnect+</title>
</Head> </Head>
<Container maxWidth={themeStretch ? false : 'lg'}> <Container maxWidth={themeStretch ? false : 'lg'}>
......
Project/Frontend/Web_App/src/pages/dashboard/invoice/new.tsx
View file @ 39909344
...@@ -26,7 +26,7 @@ export default function InvoiceCreatePage() { ...@@ -26,7 +26,7 @@ export default function InvoiceCreatePage() {
return ( return (
<> <>
<Head> <Head>
<title> Invoices: Create a new invoice | Minimal UI</title> <title> Invoices: Create a new invoice | SignConnect+</title>
</Head> </Head>
<Container maxWidth={themeStretch ? false : 'lg'}> <Container maxWidth={themeStretch ? false : 'lg'}>
......
Project/Frontend/Web_App/src/pages/dashboard/kanban.tsx
View file @ 39909344
...@@ -107,7 +107,7 @@ export default function KanbanPage() { ...@@ -107,7 +107,7 @@ export default function KanbanPage() {
return ( return (
<> <>
<Head> <Head>
<title> Kanban | Minimal UI</title> <title> Kanban | SignConnect+</title>
</Head> </Head>
<Container maxWidth={false} sx={{ height: 1 }}> <Container maxWidth={false} sx={{ height: 1 }}>
......
Project/Frontend/Web_App/src/pages/dashboard/learning-module/curriculum/index.tsx 0 → 100644
View file @ 39909344
import { Container, Grid } from '@mui/material';
import Head from 'next/head';
import _mock from 'src/_mock/_mock';
import CustomBreadcrumbs from 'src/components/custom-breadcrumbs/CustomBreadcrumbs';
import Iconify from 'src/components/iconify/Iconify';
import CarouselAnimation from 'src/sections/@dashboard/learning-module/curriculum/CarouselAnimation';
import CarouselCenterMode from 'src/sections/@dashboard/learning-module/curriculum/CarouselCenterMode';
import { useSettingsContext } from '../../../../components/settings';
import DashboardLayout from '../../../../layouts/dashboard';
CurriculumHomePage.getLayout = (page: React.ReactElement) => <DashboardLayout>{page}</DashboardLayout>;
export default function CurriculumHomePage() {
const { themeStretch } = useSettingsContext();
const _carouselsExample = [...Array(5)].map((_, index) => ({
id: _mock.id(index),
title: _mock.text.title(index),
image: _mock.image.cover(index),
description: _mock.text.description(index),
}));
return (
<>
<Head>
<title> Curriculum Home | SignLink </title>
</Head>
<Container maxWidth={themeStretch ? false : 'xl'}>
<CustomBreadcrumbs
heading="Curriculum"
links={[
{
name: 'Dashboard',
href: '#',
icon: <Iconify icon="eva:home-fill" />,
},
{ name: 'Learning Module', href: '#', icon: <Iconify icon="eva:cube-outline" /> },
{ name: 'Curriculum', icon: <Iconify icon="eva:cube-outline" /> },
]}
/>
</Container>
<Container maxWidth={themeStretch ? false : 'xl'}>
<Grid container spacing={2} rowSpacing={2}>
<Grid md={12} item>
<CarouselAnimation data={_carouselsExample} />
</Grid>
<Grid md={12} item>
<CarouselCenterMode data={_carouselsExample} />
</Grid>
</Grid>
</Container>
</>
);
}
Project/Frontend/Web_App/src/pages/dashboard/learning-module/curriculum/process.tsx 0 → 100644
View file @ 39909344
import { Card, CardContent, CardHeader, Container, Grid, Typography } from '@mui/material';
import Head from 'next/head';
import { useState } from 'react';
import CustomBreadcrumbs from 'src/components/custom-breadcrumbs/CustomBreadcrumbs';
import Editor from 'src/components/editor/Editor';
import Iconify from 'src/components/iconify/Iconify';
import { MegaMenuDesktopVertical, MegaMenuItemProps } from 'src/components/mega-menu';
import Scrollbar from 'src/components/scrollbar/Scrollbar';
import { NAV } from 'src/config';
import { useSettingsContext } from '../../../../components/settings';
import DashboardLayout from '../../../../layouts/dashboard';
CurriculumProcessPage.getLayout = (page: React.ReactElement) => <DashboardLayout>{page}</DashboardLayout>;
export default function CurriculumProcessPage() {
const { themeStretch } = useSettingsContext();
const [quillSimple, setQuillSimple] = useState('');
const ICON_SIZE = {
width: '100%',
height: '100%',
};
const data: MegaMenuItemProps[] = [
{
title: 'Parent 1',
path: '#',
icon: <Iconify icon="eva:file-fill" {...ICON_SIZE} />,
},
{
title: 'Parent 2',
path: '#',
icon: <Iconify icon="eva:file-fill" {...ICON_SIZE} />,
},
{
title: 'Parent 3',
path: '#',
icon: <Iconify icon="eva:file-fill" {...ICON_SIZE} />,
},
{
title: 'Parent 4',
path: '#',
icon: <Iconify icon="eva:file-fill" {...ICON_SIZE} />,
},
{
title: 'Parent 5',
path: '#',
icon: <Iconify icon="eva:file-fill" {...ICON_SIZE} />,
},
{
title: 'Parent 6',
path: '#',
icon: <Iconify icon="eva:file-fill" {...ICON_SIZE} />,
},
{
title: 'Parent 7',
path: '#',
icon: <Iconify icon="eva:file-fill" {...ICON_SIZE} />,
},
{
title: 'Parent 8',
path: '#',
icon: <Iconify icon="eva:file-fill" {...ICON_SIZE} />,
},
{
title: 'Parent 9',
path: '#',
icon: <Iconify icon="eva:file-fill" {...ICON_SIZE} />,
},
{
title: 'Parent 10',
path: '#',
icon: <Iconify icon="eva:file-fill" {...ICON_SIZE} />,
},
];
return (
<>
<Head>
<title> Curriculum Process | SignLink </title>
</Head>
<Container maxWidth={themeStretch ? false : 'xl'}>
<CustomBreadcrumbs
heading="Curriculum Process"
links={[
{
name: 'Dashboard',
href: '#',
icon: <Iconify icon="eva:home-fill" />,
},
{ name: 'Learning Module', href: '#', icon: <Iconify icon="eva:cube-outline" /> },
{ name: 'Curriculum', href: '#', icon: <Iconify icon="eva:cube-outline" /> },
{ name: 'Curriculum Process', icon: <Iconify icon="eva:cube-outline" /> },
]}
/>
</Container>
<Container maxWidth={themeStretch ? false : 'xl'}>
<Grid container spacing={2} rowSpacing={2}>
<Grid md={3} item>
<Card sx={{ width: NAV.W_BASE, flexShrink: 0, overflow: 'unset', zIndex: 9 }}>
<Typography variant="h6" sx={{ p: 2, display: 'flex', alignItems: 'center' }}>
<Iconify icon="eva:list-fill" width={24} sx={{ mr: 1 }} /> Curriculum Items
</Typography>
<MegaMenuDesktopVertical data={data} />
</Card>
</Grid>
<Grid md={9} item>
<Grid container spacing={2} rowSpacing={2}>
<Grid md={12} item>
<Card>
<CardHeader title="Curriculum Item 01" />
<CardContent sx={{ height: 200 }}>
<Scrollbar>
Donec mi odio, faucibus at, scelerisque quis, convallis in, nisi. Quisque ut nisi.
Suspendisse nisl elit, rhoncus eget, elementum ac, condimentum eget, diam.
Vestibulum eu odio. Proin sapien ipsum, porta a, auctor quis, euismod ut, mi. Cras
ultricies mi eu turpis hendrerit fringilla. Phasellus consectetuer vestibulum
elit. Phasellus magna. Nullam tincidunt adipiscing enim. Vestibulum volutpat
pretium libero. Nullam quis ante.
</Scrollbar>
</CardContent>
</Card>
</Grid>
<Grid md={12} item>
<Card>
<CardHeader title="Curriculum item 01 work space" />
<CardContent>
<Editor
simple
id="simple-editor"
value={quillSimple}
onChange={(value) => setQuillSimple(value)}
/>
</CardContent>
</Card>
</Grid>
</Grid>
</Grid>
</Grid>
</Container>
</>
);
}
Project/Frontend/Web_App/src/pages/dashboard/learning-module/feedback/index.tsx 0 → 100644
View file @ 39909344
import { Container, Typography } from '@mui/material';
import Head from 'next/head';
import { useSettingsContext } from '../../../../components/settings';
import DashboardLayout from '../../../../layouts/dashboard';
import CustomBreadcrumbs from 'src/components/custom-breadcrumbs/CustomBreadcrumbs';
import Iconify from 'src/components/iconify/Iconify';
FeedbackHomePage.getLayout = (page: React.ReactElement) => <DashboardLayout>{page}</DashboardLayout>;
export default function FeedbackHomePage() {
const { themeStretch } = useSettingsContext();
return (
<>
<Head>
<title> Feedback Home | SignLink </title>
</Head>
<Container maxWidth={themeStretch ? false : 'xl'}>
<CustomBreadcrumbs
heading="Feedback"
links={[
{
name: 'Dashboard',
href: '#',
icon: <Iconify icon="eva:home-fill" />,
},
{ name: 'Learning Module', href: '#', icon: <Iconify icon="eva:cube-outline" /> },
{ name: 'Feedback', icon: <Iconify icon="eva:cube-outline" /> },
]}
/>
</Container>
<Container maxWidth={themeStretch ? false : 'xl'}>
</Container>
</>
);
}
Project/Frontend/Web_App/src/pages/dashboard/learning-module/leadBoard/index.tsx 0 → 100644
View file @ 39909344
import { Box, Container, Stack, Typography } from '@mui/material';
import Head from 'next/head';
import { ComingSoonIllustration } from 'src/assets/illustrations';
import useCountdown from 'src/hooks/useCountdown';
import { useSettingsContext } from '../../../../components/settings';
import DashboardLayout from '../../../../layouts/dashboard';
LeadBoardHomePage.getLayout = (page: React.ReactElement) => <DashboardLayout>{page}</DashboardLayout>;
export default function LeadBoardHomePage() {
const { themeStretch } = useSettingsContext();
const { days, hours, minutes, seconds } = useCountdown(new Date('06/20/2023 21:30'));
return (
<>
{/* <Head>
<title> Lead Board Home | SignLink </title>
</Head>
<Container maxWidth={themeStretch ? false : 'xl'}>
<CustomBreadcrumbs
heading="Lead Board"
links={[
{
name: 'Dashboard',
href: '#',
icon: <Iconify icon="eva:home-fill" />,
},
{ name: 'Learning Module', href: '#', icon: <Iconify icon="eva:cube-outline" /> },
{ name: 'Lead Board', icon: <Iconify icon="eva:cube-outline" /> },
]}
/>
</Container>
<Container maxWidth={themeStretch ? false : 'xl'}>
</Container> */}
<Head>
<title> Coming Soon | SignConnect+</title>
</Head>
<Container maxWidth={themeStretch ? false : 'xl'}>
<Typography variant="h3" paragraph>
Coming Soon!
</Typography>
<Typography sx={{ color: 'text.secondary' }}>
We are currently working hard on this page!
</Typography>
<ComingSoonIllustration sx={{ my: 10, height: 240 }} />
<Stack
direction="row"
justifyContent="center"
divider={<Box sx={{ mx: { xs: 1, sm: 2.5 } }}>:</Box>}
sx={{ typography: 'h2' }}
>
<TimeBlock label="Days" value={days} />
<TimeBlock label="Hours" value={hours} />
<TimeBlock label="Minutes" value={minutes} />
<TimeBlock label="Seconds" value={seconds} />
</Stack>
</Container>
</>
);
}
type TimeBlockProps = {
label: string;
value: string;
};
function TimeBlock({ label, value }: TimeBlockProps) {
return (
<div>
<Box> {value} </Box>
<Box sx={{ color: 'text.secondary', typography: 'body1' }}>{label}</Box>
</div>
);
}
\ No newline at end of file
Project/Frontend/Web_App/src/pages/dashboard/learning-module/questionAndAnswers/index.tsx 0 → 100644
View file @ 39909344
import { Box, Container, Stack, Typography } from '@mui/material';
import Head from 'next/head';
import { ComingSoonIllustration } from 'src/assets/illustrations';
import useCountdown from 'src/hooks/useCountdown';
import { useSettingsContext } from '../../../../components/settings';
import DashboardLayout from '../../../../layouts/dashboard';
QuestionAndAnswersHomePage.getLayout = (page: React.ReactElement) => <DashboardLayout>{page}</DashboardLayout>;
export default function QuestionAndAnswersHomePage() {
const { themeStretch } = useSettingsContext();
const { days, hours, minutes, seconds } = useCountdown(new Date('06/20/2023 21:30'));
return (
<>
{/* <Head>
<title> Questions & Answers Home | SignLink </title>
</Head>
<Container maxWidth={themeStretch ? false : 'xl'}>
<CustomBreadcrumbs
heading="Questions & Answers"
links={[
{
name: 'Dashboard',
href: '#',
icon: <Iconify icon="eva:home-fill" />,
},
{ name: 'Learning Module', href: '#', icon: <Iconify icon="eva:cube-outline" /> },
{ name: 'Questions & Answers', icon: <Iconify icon="eva:cube-outline" /> },
]}
/>
</Container>
<Container maxWidth={themeStretch ? false : 'xl'}>
</Container> */}
<Head>
<title> Coming Soon | SignConnect+</title>
</Head>
<Container maxWidth={themeStretch ? false : 'xl'}>
<Typography variant="h3" paragraph>
Coming Soon!
</Typography>
<Typography sx={{ color: 'text.secondary' }}>
We are currently working hard on this page!
</Typography>
<ComingSoonIllustration sx={{ my: 10, height: 240 }} />
<Stack
direction="row"
justifyContent="center"
divider={<Box sx={{ mx: { xs: 1, sm: 2.5 } }}>:</Box>}
sx={{ typography: 'h2' }}
>
<TimeBlock label="Days" value={days} />
<TimeBlock label="Hours" value={hours} />
<TimeBlock label="Minutes" value={minutes} />
<TimeBlock label="Seconds" value={seconds} />
</Stack>
</Container>
</>
);
}
type TimeBlockProps = {
label: string;
value: string;
};
function TimeBlock({ label, value }: TimeBlockProps) {
return (
<div>
<Box> {value} </Box>
<Box sx={{ color: 'text.secondary', typography: 'body1' }}>{label}</Box>
</div>
);
}
\ No newline at end of file
Project/Frontend/Web_App/src/pages/dashboard/mail/[systemLabel].tsx
View file @ 39909344
...@@ -15,7 +15,7 @@ export default function MailPage() { ...@@ -15,7 +15,7 @@ export default function MailPage() {
return ( return (
<> <>
<Head> <Head>
<title> Mail | Minimal UI</title> <title> Mail | SignConnect+</title>
</Head> </Head>
<Mail /> <Mail />
......
Project/Frontend/Web_App/src/pages/dashboard/mail/[systemLabel]/[mailId].tsx
View file @ 39909344
...@@ -15,7 +15,7 @@ export default function MailPage() { ...@@ -15,7 +15,7 @@ export default function MailPage() {
return ( return (
<> <>
<Head> <Head>
<title> Mail | Minimal UI</title> <title> Mail | SignConnect+</title>
</Head> </Head>
<Mail /> <Mail />
......
Project/Frontend/Web_App/src/pages/dashboard/mail/label/[customLabel].tsx
View file @ 39909344
...@@ -15,7 +15,7 @@ export default function MailPage() { ...@@ -15,7 +15,7 @@ export default function MailPage() {
return ( return (
<> <>
<Head> <Head>
<title> Mail | Minimal UI</title> <title> Mail | SignConnect+</title>
</Head> </Head>
<Mail /> <Mail />
......
Project/Frontend/Web_App/src/pages/dashboard/mail/label/[customLabel]/[mailId].tsx
View file @ 39909344
...@@ -15,7 +15,7 @@ export default function MailPage() { ...@@ -15,7 +15,7 @@ export default function MailPage() {
return ( return (
<> <>
<Head> <Head>
<title> Mail | Minimal UI</title> <title> Mail | SignConnect+</title>
</Head> </Head>
<Mail /> <Mail />
......
Project/Frontend/Web_App/src/pages/dashboard/permission-denied.tsx
View file @ 39909344
...@@ -43,7 +43,7 @@ export default function PermissionDeniedPage() { ...@@ -43,7 +43,7 @@ export default function PermissionDeniedPage() {
return ( return (
<> <>
<Head> <Head>
<title> Other Cases: Permission Denied | Minimal UI</title> <title> Other Cases: Permission Denied | SignConnect+</title>
</Head> </Head>
<Container maxWidth={themeStretch ? false : 'lg'}> <Container maxWidth={themeStretch ? false : 'lg'}>
......
Project/Frontend/Web_App/src/pages/dashboard/spokenLanguageTranslation-module/index.tsx 0 → 100644
View file @ 39909344
import { Container, Typography } from '@mui/material';
import Head from 'next/head';
import { useSettingsContext } from '../../../components/settings';
import DashboardLayout from '../../../layouts/dashboard';
SpokenLanguageTranslationHomePage.getLayout = (page: React.ReactElement) => <DashboardLayout>{page}</DashboardLayout>;
export default function SpokenLanguageTranslationHomePage() {
const { themeStretch } = useSettingsContext();
return (
<>
<Head>
<title> Blank Page | SignConnect+</title>
</Head>
<Container maxWidth={themeStretch ? false : 'xl'}>
<Typography variant="h6"> Blank </Typography>
</Container>
</>
);
}
Project/Frontend/Web_App/src/pages/dashboard/user/[name]/edit.tsx
View file @ 39909344
...@@ -34,7 +34,7 @@ export default function UserEditPage() { ...@@ -34,7 +34,7 @@ export default function UserEditPage() {
return ( return (
<> <>
<Head> <Head>
<title> User: Edit user | Minimal UI</title> <title> User: Edit user | SignConnect+</title>
</Head> </Head>
<Container maxWidth={themeStretch ? false : 'lg'}> <Container maxWidth={themeStretch ? false : 'lg'}>
......
Project/Frontend/Web_App/src/pages/dashboard/user/account.tsx
View file @ 39909344
...@@ -75,7 +75,7 @@ export default function UserAccountPage() { ...@@ -75,7 +75,7 @@ export default function UserAccountPage() {
return ( return (
<> <>
<Head> <Head>
<title> User: Account Settings | Minimal UI</title> <title> User: Account Settings | SignConnect+</title>
</Head> </Head>
<Container maxWidth={themeStretch ? false : 'lg'}> <Container maxWidth={themeStretch ? false : 'lg'}>
......
Project/Frontend/Web_App/src/pages/dashboard/user/cards.tsx
View file @ 39909344
...@@ -26,7 +26,7 @@ export default function UserCardsPage() { ...@@ -26,7 +26,7 @@ export default function UserCardsPage() {
return ( return (
<> <>
<Head> <Head>
<title> User: Cards | Minimal UI</title> <title> User: Cards | SignConnect+</title>
</Head> </Head>
<Container maxWidth={themeStretch ? false : 'lg'}> <Container maxWidth={themeStretch ? false : 'lg'}>
......
Project/Frontend/Web_App/src/pages/dashboard/user/list.tsx
View file @ 39909344
...@@ -195,7 +195,7 @@ export default function UserListPage() { ...@@ -195,7 +195,7 @@ export default function UserListPage() {
return ( return (
<> <>
<Head> <Head>
<title> User: List | Minimal UI</title> <title> User: List | SignConnect+</title>
</Head> </Head>
<Container maxWidth={themeStretch ? false : 'lg'}> <Container maxWidth={themeStretch ? false : 'lg'}>
......
Project/Frontend/Web_App/src/pages/dashboard/user/new.tsx
View file @ 39909344
...@@ -24,7 +24,7 @@ export default function UserCreatePage() { ...@@ -24,7 +24,7 @@ export default function UserCreatePage() {
return ( return (
<> <>
<Head> <Head>
<title> User: Create a new user | Minimal UI</title> <title> User: Create a new user | SignConnect+</title>
</Head> </Head>
<Container maxWidth={themeStretch ? false : 'lg'}> <Container maxWidth={themeStretch ? false : 'lg'}>
......
Project/Frontend/Web_App/src/pages/dashboard/user/profile.tsx
View file @ 39909344
...@@ -83,7 +83,7 @@ export default function UserProfilePage() { ...@@ -83,7 +83,7 @@ export default function UserProfilePage() {
return ( return (
<> <>
<Head> <Head>
<title> User: Profile | Minimal UI</title> <title> User: Profile | SignConnect+</title>
</Head> </Head>
<Container maxWidth={themeStretch ? false : 'lg'}> <Container maxWidth={themeStretch ? false : 'lg'}>
......
Project/Frontend/Web_App/src/pages/faqs.tsx
View file @ 39909344
...@@ -17,7 +17,7 @@ export default function FaqsPage() { ...@@ -17,7 +17,7 @@ export default function FaqsPage() {
return ( return (
<> <>
<Head> <Head>
<title> Faqs | Minimal UI</title> <title> Faqs | SignConnect+</title>
</Head> </Head>
<FaqsHero /> <FaqsHero />
......
Project/Frontend/Web_App/src/pages/home.tsx 0 → 100644
View file @ 39909344
import { m, useScroll, useSpring } from 'framer-motion';
// next
import Head from 'next/head';
// @mui
import { useTheme } from '@mui/material/styles';
import { Box } from '@mui/material';
// layouts
import MainLayout from '../layouts/main';
// sections
import {
HomeHero,
HomeMinimal,
HomeDarkMode,
HomeLookingFor,
HomeForDesigner,
HomeColorPresets,
HomePricingPlans,
HomeAdvertisement,
HomeCleanInterfaces,
HomeHugePackElements,
} from '../sections/home';
// ----------------------------------------------------------------------
HomePage.getLayout = (page: React.ReactElement) => <MainLayout> {page} </MainLayout>;
// ----------------------------------------------------------------------
export default function HomePage() {
const theme = useTheme();
const { scrollYProgress } = useScroll();
const scaleX = useSpring(scrollYProgress, {
stiffness: 100,
damping: 30,
restDelta: 0.001,
});
const progress = (
<m.div
style={{
top: 0,
left: 0,
right: 0,
height: 3,
zIndex: 1999,
position: 'fixed',
transformOrigin: '0%',
backgroundColor: theme.palette.primary.main,
scaleX,
}}
/>
);
return (
<>
<Head>
<title> The starting point for your next project | SignConnect+</title>
</Head>
{progress}
<HomeHero />
<Box
sx={{
overflow: 'hidden',
position: 'relative',
bgcolor: 'background.default',
}}
>
<HomeMinimal />
<HomeHugePackElements />
<HomeForDesigner />
<HomeDarkMode />
<HomeColorPresets />
<HomeCleanInterfaces />
<HomePricingPlans />
<HomeLookingFor />
<HomeAdvertisement />
</Box>
</>
);
}
Project/Frontend/Web_App/src/pages/index.tsx
View file @ 39909344
import { m, useScroll, useSpring } from 'framer-motion'; import LoginPage from "./auth/login";
// next
import Head from 'next/head';
// @mui
import { useTheme } from '@mui/material/styles';
import { Box } from '@mui/material';
// layouts
import MainLayout from '../layouts/main';
// sections
import {
HomeHero,
HomeMinimal,
HomeDarkMode,
HomeLookingFor,
HomeForDesigner,
HomeColorPresets,
HomePricingPlans,
HomeAdvertisement,
HomeCleanInterfaces,
HomeHugePackElements,
} from '../sections/home';
// ---------------------------------------------------------------------- export default LoginPage
\ No newline at end of file
HomePage.getLayout = (page: React.ReactElement) => <MainLayout> {page} </MainLayout>;
// ----------------------------------------------------------------------
export default function HomePage() {
const theme = useTheme();
const { scrollYProgress } = useScroll();
const scaleX = useSpring(scrollYProgress, {
stiffness: 100,
damping: 30,
restDelta: 0.001,
});
const progress = (
<m.div
style={{
top: 0,
left: 0,
right: 0,
height: 3,
zIndex: 1999,
position: 'fixed',
transformOrigin: '0%',
backgroundColor: theme.palette.primary.main,
scaleX,
}}
/>
);
return (
<>
<Head>
<title> The starting point for your next project | Minimal UI</title>
</Head>
{progress}
<HomeHero />
<Box
sx={{
overflow: 'hidden',
position: 'relative',
bgcolor: 'background.default',
}}
>
<HomeMinimal />
<HomeHugePackElements />
<HomeForDesigner />
<HomeDarkMode />
<HomeColorPresets />
<HomeCleanInterfaces />
<HomePricingPlans />
<HomeLookingFor />
<HomeAdvertisement />
</Box>
</>
);
}
Project/Frontend/Web_App/src/pages/maintenance.tsx
View file @ 39909344
...@@ -18,7 +18,7 @@ export default function MaintenancePage() { ...@@ -18,7 +18,7 @@ export default function MaintenancePage() {
return ( return (
<> <>
<Head> <Head>
<title> Maintenance | Minimal UI</title> <title> Maintenance | SignConnect+</title>
</Head> </Head>
<Stack sx={{ alignItems: 'center' }}> <Stack sx={{ alignItems: 'center' }}>
......
Project/Frontend/Web_App/src/pages/payment.tsx
View file @ 39909344
...@@ -21,7 +21,7 @@ export default function PaymentPage() { ...@@ -21,7 +21,7 @@ export default function PaymentPage() {
return ( return (
<> <>
<Head> <Head>
<title> Payment | Minimal UI</title> <title> Payment | SignConnect+</title>
</Head> </Head>
<Container <Container
......
Project/Frontend/Web_App/src/pages/pricing.tsx
View file @ 39909344
...@@ -19,7 +19,7 @@ export default function PricingPage() { ...@@ -19,7 +19,7 @@ export default function PricingPage() {
return ( return (
<> <>
<Head> <Head>
<title> Pricing | Minimal UI</title> <title> Pricing | SignConnect+</title>
</Head> </Head>
<Container <Container
......
Project/Frontend/Web_App/src/routes/paths.ts
View file @ 39909344
...@@ -97,6 +97,18 @@ export const PATH_DASHBOARD = { ...@@ -97,6 +97,18 @@ export const PATH_DASHBOARD = {
view: (title: string) => path(ROOTS_DASHBOARD, `/blog/post/${title}`), view: (title: string) => path(ROOTS_DASHBOARD, `/blog/post/${title}`),
demoView: path(ROOTS_DASHBOARD, '/blog/post/apply-these-7-secret-techniques-to-improve-event'), demoView: path(ROOTS_DASHBOARD, '/blog/post/apply-these-7-secret-techniques-to-improve-event'),
}, },
spokenLanguageTranslationModule: {
root: path(ROOTS_DASHBOARD, '/spokenLanguageTranslation-module'),
spokenLanguageTranslationHome: path(ROOTS_DASHBOARD, '/spokenLanguageTranslation-module'),
},
learningModule: {
root: path(ROOTS_DASHBOARD, '/learning-module'),
curriculumHome: path(ROOTS_DASHBOARD, '/learning-module/curriculum'),
curriculumView: path(ROOTS_DASHBOARD, '/learning-module/curriculum/process'),
questionAndAnswersHome: path(ROOTS_DASHBOARD, '/learning-module/questionAndAnswers'),
leadBoardHome: path(ROOTS_DASHBOARD, '/learning-module/leadBoard'),
feedbackHome: path(ROOTS_DASHBOARD, '/learning-module/feedback'),
},
}; };
export const PATH_DOCS = { export const PATH_DOCS = {
......
Project/Frontend/Web_App/src/sections/@dashboard/learning-module/curriculum/CarouselAnimation.tsx 0 → 100644
View file @ 39909344
import { m } from 'framer-motion';
import { useRef, useState } from 'react';
// @mui
import { Box, Button, Card, CardContent, Paper, Typography } from '@mui/material';
import { alpha, useTheme } from '@mui/material/styles';
// utils
import { bgGradient } from '../../../../utils/cssStyles';
// components
import Link from 'next/link';
import { PATH_DASHBOARD } from 'src/routes/paths';
import { MotionContainer, varFade } from '../../../../components/animate';
import Carousel, { CarouselArrowIndex } from '../../../../components/carousel';
import Image from '../../../../components/image';
// ----------------------------------------------------------------------
type Props = {
data: {
id: string;
title: string;
image: string;
description: string;
}[];
};
export default function CarouselAnimation({ data }: Props) {
const theme = useTheme();
const carouselRef = useRef<Carousel | null>(null);
const [currentIndex, setCurrentIndex] = useState(theme.direction === 'rtl' ? data.length - 1 : 0);
const carouselSettings = {
speed: 800,
dots: false,
arrows: false,
autoplay: true,
slidesToShow: 1,
slidesToScroll: 1,
rtl: Boolean(theme.direction === 'rtl'),
beforeChange: (current: number, next: number) => setCurrentIndex(next),
};
const handlePrev = () => {
carouselRef.current?.slickPrev();
};
const handleNext = () => {
carouselRef.current?.slickNext();
};
return (
<Card>
<Carousel ref={carouselRef} {...carouselSettings}>
{data.map((item, index) => (
<CarouselItem key={item.id} item={item} isActive={index === currentIndex} />
))}
</Carousel>
<CarouselArrowIndex
index={currentIndex}
total={data.length}
onNext={handleNext}
onPrevious={handlePrev}
/>
</Card>
);
}
// ----------------------------------------------------------------------
type CarouselItemProps = {
item: {
title: string;
description: string;
image: string;
};
isActive: boolean;
};
function CarouselItem({ item, isActive }: CarouselItemProps) {
const theme = useTheme();
const { image, title } = item;
return (
<Paper sx={{ position: 'relative', height: 300 }}>
<Image alt={title} src={image} ratio="21/9" />
<Box
sx={{
top: 0,
width: 1,
height: 1,
position: 'absolute',
...bgGradient({
direction: 'to top',
startColor: `${theme.palette.grey[900]} 0%`,
endColor: `${alpha(theme.palette.grey[900], 0)} 100%`,
}),
}}
/>
<CardContent
component={MotionContainer}
animate={isActive}
action
sx={{
bottom: 0,
width: 1,
maxWidth: 720,
textAlign: 'left',
position: 'absolute',
color: 'common.white',
}}
>
<m.div variants={varFade().inRight}>
<Typography variant="h3" gutterBottom>
{item.title}
</Typography>
</m.div>
<m.div variants={varFade().inRight}>
<Typography variant="body2" noWrap gutterBottom>
{item.description}
</Typography>
</m.div>
<m.div variants={varFade().inRight}>
<Link href={PATH_DASHBOARD.learningModule.curriculumView}>
<Button variant="contained" sx={{ mt: 3 }}>
View More
</Button>
</Link>
</m.div>
</CardContent>
</Paper>
);
}
Project/Frontend/Web_App/src/sections/@dashboard/learning-module/curriculum/CarouselCenterMode.tsx 0 → 100644
View file @ 39909344
import { useRef } from 'react';
// @mui
import { alpha, useTheme } from '@mui/material/styles';
import { Box, Paper, Link, CardContent } from '@mui/material';
// utils
import { bgGradient } from '../../../../utils/cssStyles';
// components
import Image from '../../../../components/image';
import Iconify from '../../../../components/iconify';
import TextMaxLine from '../../../../components/text-max-line';
import Carousel, { CarouselArrows } from '../../../../components/carousel';
import { PATH_DASHBOARD } from 'src/routes/paths';
// ----------------------------------------------------------------------
type Props = {
data: {
id: string;
title: string;
image: string;
description: string;
}[];
};
export default function CarouselCenterMode({ data }: Props) {
const carouselRef = useRef<Carousel | null>(null);
const theme = useTheme();
const carouselSettings = {
slidesToShow: 3,
centerMode: true,
centerPadding: '60px',
rtl: Boolean(theme.direction === 'rtl'),
responsive: [
{
breakpoint: 1024,
settings: { slidesToShow: 2 },
},
{
breakpoint: 600,
settings: { slidesToShow: 2 },
},
{
breakpoint: 480,
settings: { slidesToShow: 1, centerPadding: '0' },
},
],
};
const handlePrev = () => {
carouselRef.current?.slickPrev();
};
const handleNext = () => {
carouselRef.current?.slickNext();
};
return (
<Box
sx={{
overflow: 'hidden',
position: 'relative',
}}
>
<CarouselArrows
filled
icon="noto:rightwards-hand"
onNext={handleNext}
onPrevious={handlePrev}
>
<Carousel ref={carouselRef} {...carouselSettings}>
{data.map((item) => (
<Box key={item.id} sx={{ px: 1 }}>
<CarouselItem item={item} />
</Box>
))}
</Carousel>
</CarouselArrows>
</Box>
);
}
// ----------------------------------------------------------------------
type CarouselItemProps = {
title: string;
description: string;
image: string;
};
function CarouselItem({ item }: { item: CarouselItemProps }) {
const theme = useTheme();
const { image, title } = item;
return (
<Paper
sx={{
borderRadius: 2,
overflow: 'hidden',
position: 'relative'
}}
>
<Image alt={title} src={image} ratio="3/4" />
<CardContent
sx={{
bottom: 0,
zIndex: 9,
width: '100%',
textAlign: 'left',
position: 'absolute',
color: 'common.white',
...bgGradient({
direction: 'to top',
startColor: `${theme.palette.grey[900]} 25%`,
endColor: `${alpha(theme.palette.grey[900], 0)} 100%`,
}),
}}
>
<TextMaxLine variant="h4" paragraph>
{title}
</TextMaxLine>
<Link
color="inherit"
variant="overline"
sx={{
opacity: 0.72,
alignItems: 'center',
display: 'inline-flex',
transition: (theme) => theme.transitions.create('opacity'),
'&:hover': { opacity: 1 },
}}
href={PATH_DASHBOARD.learningModule.curriculumView}
>
learn More
<Iconify icon="eva:arrow-forward-fill" width={16} sx={{ ml: 1 }} />
</Link>
</CardContent>
</Paper>
);
}
Project/Frontend/Web_App/src/sections/@dashboard/spokenLanguageTranslation-module/HomeWidget.tsx 0 → 100644
View file @ 39909344
import { Box, CardProps, Stack, Typography } from '@mui/material';
import { useTheme } from '@mui/material/styles';
import Iconify from 'src/components/iconify/Iconify';
import { ColorSchema } from 'src/theme/palette';
interface Props extends CardProps {
icon: string;
title: string;
subTitle: string
color?: ColorSchema;
handleClick?: any
}
export default function HomeWidget({
title,
icon,
color = 'primary',
subTitle,
sx,
handleClick,
...other
}: Props) {
const theme = useTheme();
return (
<Stack
direction="row"
alignItems="center"
sx={{
p: 3,
borderRadius: 2,
overflow: 'hidden',
position: 'relative',
color: 'common.white',
bgcolor: `${color}.dark`,
...sx,
}}
{...other}
>
<Box sx={{ ml: 3 }}>
<Typography variant="h4"
onClick={() => {
console.log(title);
handleClick(title)
}}
sx={{ cursor: 'pointer' }}
> {title}</Typography>
<Typography variant="body2" sx={{ opacity: 0.72 }}>
{subTitle}
</Typography>
</Box>
<Iconify
icon={icon}
sx={{
width: 120,
height: 120,
opacity: 0.12,
position: 'absolute',
right: theme.spacing(-0),
}}
/>
</Stack>
);
}
Project/Frontend/Web_App/src/sections/auth/Login.tsx
View file @ 39909344
...@@ -20,7 +20,7 @@ export default function Login() { ...@@ -20,7 +20,7 @@ export default function Login() {
return ( return (
<LoginLayout> <LoginLayout>
<Stack spacing={2} sx={{ mb: 5, position: 'relative' }}> <Stack spacing={2} sx={{ mb: 5, position: 'relative' }}>
<Typography variant="h4">Sign in to Minimal</Typography> <Typography variant="h4">Sign in to SignConnect+</Typography>
<Stack direction="row" spacing={0.5}> <Stack direction="row" spacing={0.5}>
<Typography variant="body2">New user?</Typography> <Typography variant="body2">New user?</Typography>
...@@ -45,8 +45,6 @@ export default function Login() { ...@@ -45,8 +45,6 @@ export default function Login() {
</Alert> </Alert>
<AuthLoginForm /> <AuthLoginForm />
<AuthWithSocial />
</LoginLayout> </LoginLayout>
); );
} }
Project/Frontend/Web_App/src/sections/auth/Register.tsx
View file @ 39909344
// next // next
import NextLink from 'next/link'; import NextLink from 'next/link';
// @mui // @mui
import { Stack, Typography, Link } from '@mui/material'; import { Link, Stack, Typography } from '@mui/material';
// layouts // layouts
import LoginLayout from '../../layouts/login'; import LoginLayout from '../../layouts/login';
// routes // routes
import { PATH_AUTH } from '../../routes/paths'; import { PATH_AUTH } from '../../routes/paths';
// //
import AuthWithSocial from './AuthWithSocial';
import AuthRegisterForm from './AuthRegisterForm'; import AuthRegisterForm from './AuthRegisterForm';
// ---------------------------------------------------------------------- // ----------------------------------------------------------------------
...@@ -44,7 +43,6 @@ export default function Register() { ...@@ -44,7 +43,6 @@ export default function Register() {
. .
</Typography> </Typography>
<AuthWithSocial />
</LoginLayout> </LoginLayout>
); );
} }
Project/Frontend/Web_App/src/sections/home/HomeMinimal.tsx
View file @ 39909344
...@@ -58,7 +58,7 @@ export default function HomeMinimal() { ...@@ -58,7 +58,7 @@ export default function HomeMinimal() {
> >
<m.div variants={varFade().inUp}> <m.div variants={varFade().inUp}>
<Typography component="div" variant="overline" sx={{ color: 'text.disabled' }}> <Typography component="div" variant="overline" sx={{ color: 'text.disabled' }}>
Minimal UI SignConnect+
</Typography> </Typography>
</m.div> </m.div>
......
Project/Frontend/Web_App/yarn.lock
View file @ 39909344
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