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LearnJoy-ML
Commit 4531590e authored by chameera's avatar chameera
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Handwriting model

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{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"6pajsg8Fie5S"},"outputs":[],"source":["import cv2\n","import numpy as np\n","from keras.models import load_model\n","from PIL import Image\n","import requests\n","import matplotlib.pyplot as plt\n","from io import BytesIO\n","from tensorflow.keras.applications.resnet50 import preprocess_input\n","import imutils\n"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":542,"status":"ok","timestamp":1702626602134,"user":{"displayName":"Kaushi Gihan","userId":"11214181140146971518"},"user_tz":-330},"id":"j2u2Ou-HjNoa","colab":{"base_uri":"https://localhost:8080/"},"outputId":"0c6866c8-421d-42ea-c443-02b54fb70e09"},"outputs":[{"output_type":"stream","name":"stdout","text":["Writing Handwriting_recognition.py\n"]}],"source":["#%%writefile Handwriting_recognition.py\n","\n","import cv2\n","import numpy as np\n","from keras.models import load_model\n","from PIL import Image\n","\n","\n","# Load the saved model\n","model = load_model('/content/drive/MyDrive/Work_space/Silverline_IT/Project/Learn_Joy/function2/fun2_model/Handwriting_recognition_1/Handwriting_recognition_1.hdf5')\n","\n","\n","\n","def Handwriting_recognition(BB_image:str=None,WB_image:str=None):\n","\n","\n"," if WB_image is not None:\n","\n"," # Load the test image\n"," image = cv2.imread(WB_image)\n"," # Preprocess the image\n"," if image is not None:\n"," #image = cv2.imread(img_path)\n"," gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n"," thresh = cv2.threshold(gray, 0, 255,cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]\n"," thresh = cv2.resize(thresh, (32, 32), interpolation = cv2.INTER_CUBIC)\n"," thresh = thresh.astype(\"float32\") / 255.0\n"," thresh = np.expand_dims(thresh, axis=-1)\n"," thresh = thresh.reshape(1,32,32,1)\n","\n"," else:\n"," print(\"Invalid or empty image.\")\n","\n","\n"," elif BB_image is not None:\n","\n"," # Load the test image\n"," image = cv2.imread(BB_image)\n","\n"," # Preprocess the image\n"," if image is not None:\n"," #image = cv2.imread(img_path)\n"," image = cv2.resize(cv2.cvtColor(image, cv2.COLOR_BGR2RGB),dsize=(32,32))\n"," image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n"," (thresh, image_binary) = cv2.threshold(image_gray, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)\n"," image_binary = cv2.threshold(image_gray, thresh, 255, cv2.THRESH_BINARY)[1]\n"," thresh = np.array(image_binary)/255.0\n"," thresh = thresh.reshape(-1,32,32,1)\n","\n","\n"," else:\n"," print(\"Invalid or empty image.\")\n","\n"," else:\n"," pass\n","\n"," # Make predictions\n"," predictions = model.predict(thresh)\n"," #print(predictions)\n"," #print(np.argmax(predictions,axis=1))\n","\n"," # Check the shape of predictions\n"," #print(\"Shape of predictions:\", predictions[0].shape)\n","\n"," classes=['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']\n"," lable_nu=list(range(0,len(classes)))\n"," real_lB=dict(zip(lable_nu,classes))\n"," #print(real_lB)\n","\n"," predicted_class = np.vectorize(real_lB.get)(np.argmax(predictions,axis=1))\n"," #print(predicted_class)\n","\n"," return {\"Recognized Character\":predicted_class[0]}\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":984,"status":"ok","timestamp":1702611194909,"user":{"displayName":"Kaushi Gihan","userId":"11214181140146971518"},"user_tz":-330},"id":"mqZIbWYY7nm2","outputId":"0141bae3-3f9f-441c-a449-072e7732dff3"},"outputs":[{"name":"stdout","output_type":"stream","text":["1/1 [==============================] - 0s 81ms/step\n"]},{"data":{"text/plain":["{'Recognized Character': 'A'}"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["Handwriting_recognition(\"/content/drive/MyDrive/Work_space/Data set/Learn_Joy/function2/Predict/2313.jpg\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5059,"status":"ok","timestamp":1702611191208,"user":{"displayName":"Kaushi Gihan","userId":"11214181140146971518"},"user_tz":-330},"id":"nVoTmDYD_cjU","outputId":"355070ce-c738-4a25-db36-12151e9c4dc7"},"outputs":[{"name":"stdout","output_type":"stream","text":["1/1 [==============================] - 0s 190ms/step\n"]},{"data":{"text/plain":["{'Recognized Character': 'T'}"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["Handwriting_recognition(WB_image=\"/content/drive/MyDrive/Work_space/Data set/Learn_Joy/function2/Predict/img030-047.png\")"]},{"cell_type":"markdown","metadata":{"id":"4ZD0Z_PMb_gK"},"source":["##Predict with bounding box & white bachground"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"t_wTnH1o1Rbq"},"outputs":[],"source":["def sort_contours(cnts, method=\"left-to-right\"):\n"," reverse = False\n"," i = 0\n"," if method == \"right-to-left\" or method == \"bottom-to-top\":\n"," reverse = True\n"," if method == \"top-to-bottom\" or method == \"bottom-to-top\":\n"," i = 1\n"," boundingBoxes = [cv2.boundingRect(c) for c in cnts]\n"," (cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),\n"," key=lambda b:b[1][i], reverse=reverse))\n","\n"," return (cnts, boundingBoxes)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"UXQDSxifuqgs"},"outputs":[],"source":["#image = cv2.imread()\n","\n","model = load_model('/content/drive/MyDrive/Work_space/Silverline_IT/Project/Learn_Joy/function2/fun2_model/Handwriting_recognition_1/Handwriting_recognition_1.hdf5')\n","\n","def get_letters(img):\n"," letters = []\n"," total_bbox_cordi = []\n"," image = cv2.imread(img)\n"," gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n"," ret,thresh1 = cv2.threshold(gray ,127,255,cv2.THRESH_BINARY_INV)\n"," dilated = cv2.dilate(thresh1, None, iterations=2)\n","\n"," cnts = cv2.findContours(dilated.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)\n"," cnts = imutils.grab_contours(cnts)\n"," cnts = sort_contours(cnts, method=\"left-to-right\")[0]\n","\n"," for c in cnts:\n"," if cv2.contourArea(c) > 10:\n"," (x, y, w, h) = cv2.boundingRect(c)\n"," print([x,y,w,h])\n"," single_bbox_cordi = [x,y,w,h]\n"," total_bbox_cordi.append(single_bbox_cordi)\n"," cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)\n"," roi = gray[y:y + h, x:x + w]\n"," thresh = cv2.threshold(roi, 0, 255,cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]\n"," thresh = cv2.resize(thresh, (32, 32), interpolation = cv2.INTER_CUBIC)\n"," thresh = thresh.astype(\"float32\") / 255.0\n"," thresh = np.expand_dims(thresh, axis=-1)\n"," thresh = thresh.reshape(1,32,32,1)\n"," ypred = model.predict(thresh)\n"," #ypred = LB.inverse_transform(ypred)\n"," [x] = ypred\n"," letters.append(x)\n"," return ypred, image, total_bbox_cordi"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":483,"status":"ok","timestamp":1702566070631,"user":{"displayName":"Kaushi Gihan","userId":"11214181140146971518"},"user_tz":-330},"id":"W8g-434_0_VN","outputId":"ca905f9a-8e8c-457d-bdf3-5e612da69858"},"outputs":[{"name":"stdout","output_type":"stream","text":["[460, 261, 422, 375]\n","1/1 [==============================] - 0s 23ms/step\n"]}],"source":["letter,image,total_bbox_cordi = get_letters('/content/drive/MyDrive/Work_space/Data set/Learn_Joy/function2/Predict/img027-034.png')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":16,"status":"ok","timestamp":1702566072608,"user":{"displayName":"Kaushi Gihan","userId":"11214181140146971518"},"user_tz":-330},"id":"2L294BJ91ytw","outputId":"8ad0a604-58cf-419f-fc26-7ee4880aef31"},"outputs":[{"name":"stdout","output_type":"stream","text":["[[6.5416185e-05 1.6269371e-09 5.9041672e-04 1.1789890e-08 1.6744872e-07\n"," 1.2843116e-08 4.3512742e-07 8.7157115e-10 9.3695093e-08 1.7395679e-07\n"," 1.3048541e-03 5.8992364e-06 1.0420042e-06 1.1702650e-03 9.9977512e-08\n"," 1.6490534e-07 3.8444973e-04 1.2546275e-08 7.7148997e-08 1.8157302e-06\n"," 9.0535551e-11 2.1660206e-08 6.8394826e-08 2.4115893e-06 2.4366493e-06\n"," 9.9644524e-01 5.0177903e-07 2.9452164e-07 8.7145813e-10 1.6614571e-06\n"," 1.5226144e-08 2.1720891e-05 2.1708865e-10 5.4405479e-08 1.8828146e-07]]\n"]}],"source":["print(letter)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":487},"executionInfo":{"elapsed":2367,"status":"ok","timestamp":1702566154598,"user":{"displayName":"Kaushi Gihan","userId":"11214181140146971518"},"user_tz":-330},"id":"Bp2tzAA1128l","outputId":"9b1ba58b-138e-40c6-e203-4c155f49ccd4"},"outputs":[{"name":"stdout","output_type":"stream","text":["[25]\n","[[460, 261, 422, 375]]\n"]},{"data":{"text/plain":["<matplotlib.image.AxesImage at 0x7de7d293c3d0>"]},"execution_count":63,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["print(np.argmax(letter,axis=1))\n","print(total_bbox_cordi)\n","plt.imshow(image)"]},{"cell_type":"markdown","metadata":{"id":"TRKxPN7e_7gh"},"source":["##Fast API"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":35668,"status":"ok","timestamp":1702611234404,"user":{"displayName":"Kaushi Gihan","userId":"11214181140146971518"},"user_tz":-330},"id":"qG1kwa3_CvGR","outputId":"9ddaaf93-5abd-4764-fc6b-65739937cc64"},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting python-multipart\n"," Downloading python_multipart-0.0.6-py3-none-any.whl (45 kB)\n","\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/45.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.7/45.7 kB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: python-multipart\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","lida 0.0.10 requires fastapi, which is not installed.\n","lida 0.0.10 requires kaleido, which is not installed.\n","lida 0.0.10 requires uvicorn, which is not installed.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed python-multipart-0.0.6\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m93.1/93.1 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.0/67.0 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","lida 0.0.10 requires kaleido, which is not installed.\n","lida 0.0.10 requires uvicorn, which is not installed.\n","tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m59.7/59.7 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","lida 0.0.10 requires kaleido, which is not installed.\u001b[0m\u001b[31m\n","\u001b[0mRequirement already satisfied: nest-asyncio in /usr/local/lib/python3.10/dist-packages (1.5.8)\n"]}],"source":["!pip install python-multipart\n","!pip install fastapi -q\n","!pip install pyngrok -q\n","!pip install uvicorn -q\n","!pip install pydantic -q\n","!pip install nest-asyncio"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iohU7kEWEd70"},"outputs":[],"source":["from fastapi import FastAPI\n","from pydantic import BaseModel\n","import uvicorn\n","from pyngrok import ngrok\n","import nest_asyncio"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":507,"status":"ok","timestamp":1702626629090,"user":{"displayName":"Kaushi Gihan","userId":"11214181140146971518"},"user_tz":-330},"id":"s_KwQ1_t__TY","colab":{"base_uri":"https://localhost:8080/"},"outputId":"335c81d6-d9e2-4a3b-db4f-0bf341d952af"},"outputs":[{"output_type":"stream","name":"stdout","text":["Writing app02.py\n"]}],"source":["#%%writefile app02.py\n","from fastapi import FastAPI\n","from fastapi import File, UploadFile\n","from typing import List\n","from pydantic import BaseModel\n","import uvicorn\n","\n","\n","import cv2\n","import tempfile\n","import os\n","import shutil\n","\n","app=FastAPI(title=\"Learn Joy\",\n"," description=\"FastAPI for Learn_Joy\",\n"," version=\"0.104.1\")\n","\n","\n","\n","@app.get(\"/\")\n","async def root():\n"," return{\"Fast API\":\"Hello World\"}\n","\n","\n","@app.post(\"/function02/recognition_BB\")\n","async def function2_recognition(BB_image:UploadFile =File(...)):\n"," out_put={}\n"," temp_dir = tempfile.mkdtemp()\n","\n"," # Create a temporary file path foe doner file\n"," BB_image_path = os.path.join(temp_dir,BB_image.filename)\n","\n"," # Write the uploaded file content to the temporary file\n"," with open(BB_image_path, \"wb\") as temp_file1:\n"," shutil.copyfileobj(BB_image.file, temp_file1)\n","\n"," out_put= Handwriting_recognition(BB_image=BB_image_path,WB_image=None)\n","\n","\n"," # Clean up: Remove the temporary directory and its contents\n"," shutil.rmtree(temp_dir)\n","\n"," return out_put\n","\n","@app.post(\"/function02/recognition_WB\")\n","async def function2_recognition(WB_image:UploadFile =File(...)):\n"," out_put={}\n"," # Create a temporary directory\n"," temp_dir = tempfile.mkdtemp()\n"," WB_image_path = os.path.join(temp_dir,WB_image.filename)\n","\n"," # Write the uploaded file content to the temporary file\n"," with open(WB_image_path, \"wb\") as temp_file2:\n"," shutil.copyfileobj(WB_image.file, temp_file2)\n","\n","\n"," out_put= Handwriting_recognition(BB_image=None,WB_image=WB_image_path)\n","\n"," # Clean up: Remove the temporary directory and its contents\n"," shutil.rmtree(temp_dir)\n","\n","\n"," return out_put"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zFoJ5KojCrn3"},"outputs":[],"source":["ngrok.set_auth_token(\"2YX5IJustLB5zcqU9rbzA9iZLVf_86pZKjHT2kXNELQgLRkyi\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"w2y_Zbs9Ctlh","executionInfo":{"status":"ok","timestamp":1702625055784,"user_tz":-330,"elapsed":31613,"user":{"displayName":"Kaushi Gihan","userId":"11214181140146971518"}},"outputId":"e329e6ff-039b-4833-abe8-a787c63cf7e5"},"outputs":[{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["Public URL: https://9c75-34-16-185-214.ngrok-free.app\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["INFO: Started server process [2017]\n","INFO: Waiting for application startup.\n","INFO: Application startup complete.\n","INFO: Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"GET / HTTP/1.1\" 200 OK\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"GET / HTTP/1.1\" 200 OK\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"GET /favicon.ico HTTP/1.1\" 404 Not Found\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"GET /docs HTTP/1.1\" 200 OK\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"GET /openapi.json HTTP/1.1\" 200 OK\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"GET /docs HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 58ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_WB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 16ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_WB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 16ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_WB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 17ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_WB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 20ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_WB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 17ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 15ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 21ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 16ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 17ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 15ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 17ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 16ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 18ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 15ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 15ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 15ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 16ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 16ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 15ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 15ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 16ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 15ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 16ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 17ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 19ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 14ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 15ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 18ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 15ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 15ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 20ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 16ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 18ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 17ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 15ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 16ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 17ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 14ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 16ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 16ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 20ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 17ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 17ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 20ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 15ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 16ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 16ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 14ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 17ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 16ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 19ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n","1/1 [==============================] - 0s 16ms/step\n","INFO: 2402:4000:21c3:1fa7:18b1:f959:46a6:6c1d:0 - \"POST /function02/recognition_BB HTTP/1.1\" 200 OK\n"]},{"output_type":"stream","name":"stderr","text":["INFO: Shutting down\n","INFO: Waiting for application shutdown.\n","INFO: Application shutdown complete.\n","INFO: Finished server process [2017]\n"]}],"source":["ngrok_tunnel = ngrok.connect(8000)\n","print('Public URL:', ngrok_tunnel.public_url)\n","nest_asyncio.apply()\n","uvicorn.run(app, port=8000)"]}],"metadata":{"colab":{"collapsed_sections":["4ZD0Z_PMb_gK"],"provenance":[],"mount_file_id":"1BpT_sd4w1-FwnK-TfsP5vTkDaM5_DE62","authorship_tag":"ABX9TyPh9OwNrIbVLI5ImSKsM1N8"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
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