Skip to content

A
ASD_Detection
Commit 15c30659 authored by Wijegunarathna K. P. S. G. G.'s avatar Wijegunarathna K. P. S. G. G.
Browse files
Options

Merge branch 'IT20244170' into 'master' 

Merge with IT20244170 branch with Main branch.

See merge request !4
parents a26d560f 40c54640
Hide whitespace changes
Inline Side-by-side
Showing with 817 additions and 0 deletions
Backend/abnomility-sentiment.ipynb 0 → 100644
View file @ 15c30659
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\sulak\\anaconda3\\lib\\site-packages\\paramiko\\transport.py:219: CryptographyDeprecationWarning: Blowfish has been deprecated\n",
" \"class\": algorithms.Blowfish,\n"
]
}
],
"source": [
"import os, glob\n",
"import warnings\n",
"import numpy as np\n",
"import pandas as pd\n",
"import tensorflow as tf\n",
"import matplotlib.pyplot as plt\n",
"from pyannote.audio import Model, Inference\n",
"warnings.filterwarnings(\"ignore\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"embedding_model = Model.from_pretrained(\n",
" \"pyannote/embedding\", \n",
" use_auth_token=\"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\"\n",
" )\n",
"embedding_inference = Inference(\n",
" embedding_model, \n",
" window=\"whole\"\n",
" )\n",
"\n",
"class_dict = {\n",
" 'autism': 0,\n",
" 'non-autism': 1\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def load_dataset(audio_dir='data/abnormality'):\n",
" voice_files = glob.glob(f'{audio_dir}/*/*/*.wav')\n",
" voice_files = [voice_file.replace('\\\\', '/') for voice_file in voice_files]\n",
"\n",
" folder_names = [voice_file.split('/')[-3] for voice_file in voice_files]\n",
" labels = [class_dict[folder_name] for folder_name in folder_names]\n",
"\n",
" embeddings = np.zeros((len(voice_files), 512))\n",
" labels = np.array(labels)\n",
" errorneous_idxs = []\n",
" for i, voice_file in enumerate(voice_files):\n",
" try:\n",
" embeddings[i] = embedding_inference(voice_file)\n",
" except:\n",
" errorneous_idxs.append(i)\n",
" print('Errorneous file: ', voice_file)\n",
"\n",
" embeddings = np.delete(embeddings, errorneous_idxs, axis=0)\n",
" labels = np.delete(labels, errorneous_idxs, axis=0)\n",
" return embeddings, labels"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Embedding shape: (32, 512)\n",
"labels shape: (32,)\n"
]
}
],
"source": [
"embeddings, labels = load_dataset()\n",
"\n",
"print(\"Embedding shape: \", embeddings.shape)\n",
"print(\"labels shape: \", labels.shape)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def build_model():\n",
" inputs = tf.keras.Input(shape=(512,))\n",
" x = tf.keras.layers.Dense(256, activation='relu')(inputs)\n",
" x = tf.keras.layers.Dense(128)(x)\n",
" x = tf.keras.layers.BatchNormalization()(x)\n",
" x = tf.keras.layers.Activation('relu')(x)\n",
" x = tf.keras.layers.Dropout(0.2)(x)\n",
"\n",
" x = tf.keras.layers.Dense(64)(x)\n",
" x = tf.keras.layers.BatchNormalization()(x)\n",
" x = tf.keras.layers.Activation('relu')(x)\n",
" x = tf.keras.layers.Dropout(0.2)(x)\n",
"\n",
" x = tf.keras.layers.Dense(32)(x)\n",
" x = tf.keras.layers.BatchNormalization()(x)\n",
" x = tf.keras.layers.Activation('relu')(x)\n",
" x = tf.keras.layers.Dropout(0.2)(x)\n",
"\n",
" outputs = tf.keras.layers.Dense(1, activation='sigmoid', name='detection')(x)\n",
"\n",
" model = tf.keras.Model(\n",
" inputs=inputs, \n",
" outputs=outputs\n",
" )\n",
" model.compile(\n",
" optimizer='adam',\n",
" loss='binary_crossentropy',\n",
" metrics=[\n",
" tf.keras.metrics.BinaryAccuracy(name='accuracy'),\n",
" tf.keras.metrics.Precision(name='precision'),\n",
" tf.keras.metrics.Recall(name='recall'),\n",
" tf.keras.metrics.AUC(name='auc')\n",
" ]\n",
" )\n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"model\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" input_1 (InputLayer) [(None, 512)] 0 \n",
" \n",
" dense (Dense) (None, 256) 131328 \n",
" \n",
" dense_1 (Dense) (None, 128) 32896 \n",
" \n",
" batch_normalization (BatchN (None, 128) 512 \n",
" ormalization) \n",
" \n",
" activation (Activation) (None, 128) 0 \n",
" \n",
" dropout (Dropout) (None, 128) 0 \n",
" \n",
" dense_2 (Dense) (None, 64) 8256 \n",
" \n",
" batch_normalization_1 (Batc (None, 64) 256 \n",
" hNormalization) \n",
" \n",
" activation_1 (Activation) (None, 64) 0 \n",
" \n",
" dropout_1 (Dropout) (None, 64) 0 \n",
" \n",
" dense_3 (Dense) (None, 32) 2080 \n",
" \n",
" batch_normalization_2 (Batc (None, 32) 128 \n",
" hNormalization) \n",
" \n",
" activation_2 (Activation) (None, 32) 0 \n",
" \n",
" dropout_2 (Dropout) (None, 32) 0 \n",
" \n",
" detection (Dense) (None, 1) 33 \n",
" \n",
"=================================================================\n",
"Total params: 175,489\n",
"Trainable params: 175,041\n",
"Non-trainable params: 448\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model = build_model()\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/100\n",
"4/4 [==============================] - 1s 8ms/step - loss: 0.8720 - accuracy: 0.4062 - precision: 0.3846 - recall: 0.3125 - auc: 0.3613\n",
"Epoch 2/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.6819 - accuracy: 0.5625 - precision: 0.6250 - recall: 0.3125 - auc: 0.6250\n",
"Epoch 3/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.5912 - accuracy: 0.6875 - precision: 0.8000 - recall: 0.5000 - auc: 0.7383 \n",
"Epoch 4/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.4495 - accuracy: 0.8438 - precision: 0.8667 - recall: 0.8125 - auc: 0.8945\n",
"Epoch 5/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.3916 - accuracy: 0.8438 - precision: 0.9231 - recall: 0.7500 - auc: 0.9297\n",
"Epoch 6/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.2661 - accuracy: 0.9062 - precision: 0.9333 - recall: 0.8750 - auc: 0.9883\n",
"Epoch 7/100\n",
"4/4 [==============================] - 0s 3ms/step - loss: 0.3791 - accuracy: 0.8750 - precision: 0.8750 - recall: 0.8750 - auc: 0.9355\n",
"Epoch 8/100\n",
"4/4 [==============================] - 0s 3ms/step - loss: 0.3156 - accuracy: 0.9062 - precision: 0.9333 - recall: 0.8750 - auc: 0.9922\n",
"Epoch 9/100\n",
"4/4 [==============================] - 0s 3ms/step - loss: 0.2801 - accuracy: 0.9375 - precision: 0.9375 - recall: 0.9375 - auc: 0.9805\n",
"Epoch 10/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.2198 - accuracy: 0.9688 - precision: 1.0000 - recall: 0.9375 - auc: 1.0000\n",
"Epoch 11/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.2803 - accuracy: 0.9062 - precision: 1.0000 - recall: 0.8125 - auc: 0.9922\n",
"Epoch 12/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.2149 - accuracy: 0.9688 - precision: 1.0000 - recall: 0.9375 - auc: 1.0000\n",
"Epoch 13/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.1541 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 14/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.1529 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 15/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.2901 - accuracy: 0.8750 - precision: 0.8750 - recall: 0.8750 - auc: 0.9648\n",
"Epoch 16/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.1161 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 17/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.1315 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 18/100\n",
"4/4 [==============================] - 0s 3ms/step - loss: 0.3717 - accuracy: 0.9062 - precision: 1.0000 - recall: 0.8125 - auc: 0.9043\n",
"Epoch 19/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.1533 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 20/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.1370 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 21/100\n",
"4/4 [==============================] - 0s 3ms/step - loss: 0.1268 - accuracy: 0.9688 - precision: 0.9412 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 22/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.1185 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 23/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.1002 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 24/100\n",
"4/4 [==============================] - 0s 5ms/step - loss: 0.1243 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 25/100\n",
"4/4 [==============================] - 0s 5ms/step - loss: 0.1121 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 26/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.1200 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 27/100\n",
"4/4 [==============================] - 0s 3ms/step - loss: 0.1230 - accuracy: 0.9688 - precision: 1.0000 - recall: 0.9375 - auc: 1.0000\n",
"Epoch 28/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.0778 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 29/100\n",
"4/4 [==============================] - 0s 3ms/step - loss: 0.0838 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 30/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.0756 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 31/100\n",
"4/4 [==============================] - 0s 3ms/step - loss: 0.0807 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 32/100\n",
"4/4 [==============================] - 0s 3ms/step - loss: 0.0835 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 33/100\n",
"4/4 [==============================] - 0s 3ms/step - loss: 0.1426 - accuracy: 0.9375 - precision: 0.9375 - recall: 0.9375 - auc: 0.9961\n",
"Epoch 34/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.0730 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 35/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.0690 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 36/100\n",
"4/4 [==============================] - 0s 3ms/step - loss: 0.0762 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 37/100\n",
"4/4 [==============================] - 0s 3ms/step - loss: 0.0935 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 38/100\n",
"4/4 [==============================] - 0s 3ms/step - loss: 0.1438 - accuracy: 0.9688 - precision: 1.0000 - recall: 0.9375 - auc: 1.0000\n",
"Epoch 39/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.0632 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 40/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.0515 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 41/100\n",
"4/4 [==============================] - 0s 3ms/step - loss: 0.0540 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 42/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.0509 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 43/100\n",
"4/4 [==============================] - 0s 3ms/step - loss: 0.0636 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 44/100\n",
"4/4 [==============================] - 0s 3ms/step - loss: 0.0900 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 45/100\n",
"4/4 [==============================] - 0s 3ms/step - loss: 0.0758 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 46/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.0447 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 47/100\n",
"4/4 [==============================] - 0s 3ms/step - loss: 0.1070 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 48/100\n",
"4/4 [==============================] - 0s 3ms/step - loss: 0.1172 - accuracy: 0.9688 - precision: 0.9412 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 49/100\n",
"4/4 [==============================] - 0s 3ms/step - loss: 0.0479 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 50/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.0435 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 51/100\n",
"4/4 [==============================] - 0s 3ms/step - loss: 0.1728 - accuracy: 0.9375 - precision: 1.0000 - recall: 0.8750 - auc: 0.9922\n",
"Epoch 52/100\n",
"4/4 [==============================] - 0s 3ms/step - loss: 0.1023 - accuracy: 0.9688 - precision: 1.0000 - recall: 0.9375 - auc: 1.0000\n",
"Epoch 53/100\n",
"4/4 [==============================] - 0s 3ms/step - loss: 0.0716 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 54/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.0358 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 55/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.0267 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 56/100\n",
"4/4 [==============================] - 0s 3ms/step - loss: 0.0797 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 57/100\n",
"4/4 [==============================] - 0s 3ms/step - loss: 0.0783 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 58/100\n",
"4/4 [==============================] - 0s 5ms/step - loss: 0.0528 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 59/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.0318 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 60/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.0753 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 61/100\n",
"4/4 [==============================] - 0s 3ms/step - loss: 0.3589 - accuracy: 0.8438 - precision: 0.7895 - recall: 0.9375 - auc: 0.9258 \n",
"Epoch 62/100\n",
"4/4 [==============================] - 0s 4ms/step - loss: 0.0808 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 63/100\n",
"4/4 [==============================] - 0s 3ms/step - loss: 0.0684 - accuracy: 0.9688 - precision: 0.9412 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 64/100\n",
"4/4 [==============================] - 0s 3ms/step - loss: 0.1127 - accuracy: 0.9375 - precision: 0.8889 - recall: 1.0000 - auc: 1.0000\n",
"Epoch 65/100\n",
"4/4 [==============================] - 0s 7ms/step - loss: 0.1123 - accuracy: 0.9375 - precision: 0.8889 - recall: 1.0000 - auc: 1.0000\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x16a176b08b0>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(\n",
" embeddings,\n",
" labels,\n",
" epochs=100,\n",
" batch_size=8,\n",
" callbacks=[\n",
" tf.keras.callbacks.EarlyStopping(\n",
" monitor='loss',\n",
" patience=10,\n",
" restore_best_weights=True\n",
" )\n",
" ] \n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"model.save('models/abnomility-sentiment.h5')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"class_dict_rev = {v: k for k, v in class_dict.items()} "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def inference_abnomility_sentiment(audio_file):\n",
" embedding = embedding_inference(audio_file)\n",
" embedding = np.expand_dims(embedding, axis=0)\n",
" sentiment = model.predict(embedding)\n",
" sentiment = sentiment.squeeze()\n",
" sentiment = np.round(sentiment)\n",
" sentiment = int(sentiment)\n",
" return class_dict_rev[sentiment]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1/1 [==============================] - 0s 153ms/step\n"
]
},
{
"data": {
"text/plain": [
"'autism'"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"response = inference_abnomility_sentiment('data/abnormality/autism/Child 1 - 16/child16_8-තාත්ති එක්ක ආවෙ.wav')\n",
"response"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "tf210",
"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": "3.9.13"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
Frontend/lib/pages/Comp1/Comp1Intro.dart 0 → 100644
View file @ 15c30659
import 'package:Autism/widgets/Button.dart';
import 'package:Autism/widgets/Header.dart';
import 'package:flutter/material.dart';
import 'package:Autism/MyStyles.dart' as MyStyles;
class Comp1Intro extends StatefulWidget {
const Comp1Intro({super.key});
@override
State<Comp1Intro> createState() => _Comp1IntroState();
}
class _Comp1IntroState extends State<Comp1Intro> {
@override
Widget build(BuildContext context) {
void nextPage(String route) {
Navigator.pushNamedAndRemoveUntil(context, route,(r) => false, arguments: {});
}
return Column(
children: [
SizedBox(height: 20,),
Header(title: 'කැමති ක්‍රියාකාරකම තෝරන්න'),
SizedBox(height: 30,),
Button(route: '/Comp1Step1First', title: 'පින්තුරයක් බලා කතන්දර කියවීම', bg: MyStyles.cbtnPrimary),
SizedBox(height: 30,),
Button(route: '/Comp1Step2', title: 'කැමති කතාවක් කීම', bg: MyStyles.cbtnPrimary),
SizedBox(width: 180,child:
Image.asset('assets/images/Component 1 - img 02.png')
),
],
);
}
}
Frontend/lib/pages/Comp1/Comp1Welcome.dart 0 → 100644
View file @ 15c30659
import 'package:Autism/widgets/ButtonXl.dart';
import 'package:flutter/material.dart';
import 'package:Autism/MyStyles.dart' as MyStyles;
class Comp1Welcome extends StatefulWidget {
const Comp1Welcome({super.key});
@override
State<Comp1Welcome> createState() => _Comp1WelcomeState();
}
class _Comp1WelcomeState extends State<Comp1Welcome> {
@override
Widget build(BuildContext context) {
void nextPage(String route) {
Navigator.pushNamedAndRemoveUntil(context, route,(r) => false, arguments: {});
}
return Column(
children: [
SizedBox(width: 180,child:
Image.asset('assets/images/Component 1 - img 01.png')
),
SizedBox(height: 30,),
ButtonXL(route: '/Comp1Intro', title: 'ආරම්භ කරන්න', bg: MyStyles.cbtnPrimary),
],
);
}
}
Frontend/lib/pages/Comp1/Step1/Comp1Step1First.dart 0 → 100644
View file @ 15c30659
import 'package:Autism/widgets/Header.dart';
import 'package:Autism/widgets/ImageCard.dart';
import 'package:flutter/material.dart';
class Comp1Step1First extends StatefulWidget {
const Comp1Step1First({super.key});
@override
State<Comp1Step1First> createState() => _Comp1Step1FirstState();
}
class _Comp1Step1FirstState extends State<Comp1Step1First> {
void nextPage(String route,String image) {
Navigator.pushNamed(context, route, arguments: {
'image':image
});
}
@override
Widget build(BuildContext context) {
return Column(
children: [
SizedBox(height: 20,),
Header(title: 'කැමති ක්‍රියාකාරකම තෝරන්න'),
SizedBox(height: 30,),
InkWell(
onTap: ()=>nextPage('/Comp1Step1Second','assets/images/Component 1 - img 03.png'),
child: ImageCard(image: 'assets/images/Component 1 - img 03.png')
),
SizedBox(height: 30,),
InkWell(
onTap: ()=>nextPage('/Comp1Step1Second','assets/images/Component 1 - img 04.png'),
child: ImageCard(image: 'assets/images/Component 1 - img 04.png')
),
],
);
}
}
Frontend/lib/pages/Comp1/Step1/Comp1Step1Second.dart 0 → 100644
View file @ 15c30659
import 'package:Autism/widgets/ButtonXl.dart';
import 'package:Autism/widgets/Instructions.dart';
import 'package:flutter/material.dart';
import 'package:Autism/MyStyles.dart' as MyStyles;
class Comp1Step1Second extends StatefulWidget {
const Comp1Step1Second({super.key});
@override
State<Comp1Step1Second> createState() => _Comp1Step1SecondState();
}
class _Comp1Step1SecondState extends State<Comp1Step1Second> {
String image = '';
void nextPage(String route) {
Navigator.pushNamed(context, route, arguments: {
'image':image
});
}
@override
Widget build(BuildContext context) {
final arg = ModalRoute.of(context)!.settings.arguments as Map;
image = arg['image'];
return Column(
children: [
SizedBox(height: 20,),
Instructions(title: 'උපදෙස්',body: 'මීළඟ පියවරෙන් පසු, දරුවාට විනාඩි දෙකක් කතා කිරීමට සලස්වන්න',),
SizedBox(height: 50,),
ButtonXL(route: '/Comp1Step1Third',arguments:{'image':image}, title: 'ඊළඟ පියවර', bg: MyStyles.cbtnPrimary),
],
);
}
}
Frontend/lib/pages/Comp1/Step1/Comp1Step1Third.dart 0 → 100644
View file @ 15c30659
import 'dart:io';
import 'package:Autism/widgets/AudioInput.dart';
import 'package:Autism/widgets/ButtonIcon.dart';
import 'package:Autism/widgets/ImageCard.dart';
import 'package:Autism/widgets/Instructions.dart';
import 'package:flutter/material.dart';
import 'package:Autism/MyStyles.dart' as MyStyles;
import 'package:Autism/Api.dart' as Api;
import 'package:dio/dio.dart';
class Comp1Step1Third extends StatefulWidget {
const Comp1Step1Third({super.key});
@override
State<Comp1Step1Third> createState() => _Comp1Step1ThirdState();
}
class _Comp1Step1ThirdState extends State<Comp1Step1Third> {
String image = '';
File? recordedFile;
String color = '';
Future sendRequest() async {
try {
Response response;
var dio = Dio();
FormData formData = FormData.fromMap({
'audio': await MultipartFile.fromFile(
recordedFile!.path,
//filename: 'image.jpg'
),
});
response = await dio.post(
Api.Comp1Api,
data: formData,
onSendProgress: (int sent, int total) {
//print((100 * sent) / total);
},
);
if (response.statusCode == 200) {
if (response.data["abnomility-sentiment"] == "autism") {
setState(() {
color = "රතු පාට";
});
// var color = "Red";
} else {
// color = "Green";
setState(() {
color = "කොළ පාට";
});
}
print(response.data);
print(response.data["abnomility-sentiment"]);
print(color);
nextPage('/Results');
}
} catch (e) {
print(e);
}
}
void nextPage(String route) {
Navigator.pushNamed(context, route, arguments: {'color': color});
}
@override
Widget build(BuildContext context) {
final arg = ModalRoute.of(context)!.settings.arguments as Map;
image = arg['image'];
return Column(
children: [
ImageCard(image: image),
SizedBox(
height: 10,
),
Instructions(
title: 'උපදෙස්',
body: 'පින්තූරය විස්තර කිරීමට දරුවාට කියන්න',
),
SizedBox(
height: 10,
),
Row(
mainAxisAlignment: MainAxisAlignment.spaceEvenly,
children: [
AudioInput(
audio: 'audio',
rtn: (reco) {
setState(() {
recordedFile = reco;
});
print('recorded');
}),
recordedFile != null
? ButtonIcon(
click: () => sendRequest(),
icon: Icons.arrow_forward_ios,
bg: MyStyles.cbtnPrimary,
)
: SizedBox(),
],
)
],
);
}
}
Frontend/lib/pages/Comp1/Step2/Comp1Step2.dart 0 → 100644
View file @ 15c30659
import 'dart:io';
import 'package:Autism/widgets/AudioInput.dart';
import 'package:Autism/widgets/ButtonIcon.dart';
import 'package:Autism/widgets/Instructions.dart';
import 'package:flutter/material.dart';
import 'package:Autism/MyStyles.dart' as MyStyles;
import 'package:Autism/Api.dart' as Api;
import 'package:dio/dio.dart';
class Comp1Step2 extends StatefulWidget {
const Comp1Step2({super.key});
@override
State<Comp1Step2> createState() => _Comp1Step2State();
}
class _Comp1Step2State extends State<Comp1Step2> {
File? recordedFile;
String color = '';
Future sendRequest() async {
try {
Response response;
var dio = Dio();
FormData formData = FormData.fromMap({
'audio': await MultipartFile.fromFile(
recordedFile!.path,
//filename: 'image.jpg'
),
});
response = await dio.post(
Api.Comp1Api,
data: formData,
// onSendProgress: (int sent, int total) {
// print((100 * sent) / total);
// },
);
if (response.statusCode == 200) {
if (response.data["abnomility-sentiment"] == "autism") {
setState(() {
color = "රතු පාට";
});
// var color = "Red";
} else {
// color = "Green";
setState(() {
color = "කොළ පාට";
});
}
print(response.data);
print(response.data["abnomility-sentiment"]);
print(color);
nextPage('/Results');
}
} catch (e) {
print(e);
}
}
void nextPage(String route) {
Navigator.pushNamed(context, route, arguments: {'color': color});
}
@override
Widget build(BuildContext context) {
return Column(
children: [
Instructions(
title: 'උපදෙස්',
body: 'ඔබේ දරුවාට කතන්දරය කීම ආරම්ඹ කරන්න යැයි පවසන්න',
),
SizedBox(
height: 50,
),
Row(
mainAxisAlignment: MainAxisAlignment.spaceEvenly,
children: [
AudioInput(
audio: 'audio',
rtn: (reco) {
setState(() {
recordedFile = reco;
});
print('recorded');
}),
recordedFile != null
? ButtonIcon(
click: () => sendRequest(),
icon: Icons.arrow_forward_ios,
bg: MyStyles.cbtnPrimary,
)
: SizedBox(),
],
)
],
);
}
}
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment