{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyOcwN4hTrwFblLIGBmjUo1/"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cHhaLANYlk12","executionInfo":{"status":"ok","timestamp":1698233246284,"user_tz":-330,"elapsed":29915,"user":{"displayName":"Piumi Rathnayaka","userId":"02254013807970830484"}},"outputId":"4ae76d70-b1eb-4085-e538-744845f48ac7"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oJIjvLhOlA2J","executionInfo":{"status":"ok","timestamp":1698233362578,"user_tz":-330,"elapsed":116298,"user":{"displayName":"Piumi Rathnayaka","userId":"02254013807970830484"}},"outputId":"a99a8ad0-3f5c-43aa-874c-820ae1a12752"},"outputs":[{"output_type":"stream","name":"stdout","text":[" * Serving Flask app '__main__'\n"," * Debug mode: on\n"]},{"output_type":"stream","name":"stderr","text":["INFO:werkzeug:\u001b[31m\u001b[1mWARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.\u001b[0m\n"," * Running on http://127.0.0.1:5000\n","INFO:werkzeug:\u001b[33mPress CTRL+C to quit\u001b[0m\n","INFO:werkzeug: * Restarting with stat\n"]}],"source":["from flask import Flask, request, jsonify\n","import tensorflow as tf\n","import numpy as np\n","\n","app = Flask(__name__)\n","\n","# Load the Keras model from the .h5 file\n","model = tf.keras.models.load_model('/content/drive/MyDrive/Research_Test/Handwritten-Character-Recognition/best_model.h5')\n","\n","@app.route('/predict', methods=['POST'])\n","def predict():\n"," try:\n"," data = request.json # Assuming you are sending JSON data\n"," # Preprocess the input data if needed\n"," input_data = np.array(data['input_data']) # Adjust this based on your input format\n"," # Make predictions using the loaded Keras model\n"," predictions = model.predict(input_data)\n"," # You can post-process the predictions if needed\n"," response = {'predictions': predictions.tolist()}\n"," return jsonify(response)\n"," except Exception as e:\n"," return jsonify({'error': str(e)})\n","\n","if __name__ == '__main__':\n"," app.run(debug=True)\n","\n","# Make sure to replace 'your_model.h5' with the actual path to your .h5 model file. Also, adjust the code to preprocess and format the input data and post-process the predictions as needed for your specific use case.\n","\n","# Running the API: Save this code in a Python script, and run the script. Your Flask API should start and listen on the default address and port (http://127.0.0.1:5000/). You can access it by making POST requests to the /predict endpoint with the input data.\n","\n","# **Making Predict\n","\n","\n"]},{"cell_type":"code","source":[],"metadata":{"id":"iwvKO3cUlRGO"},"execution_count":null,"outputs":[]}]}
