vinuka/leafsecurehost

1from flask import Flask, request, jsonify2from tensorflow.keras.models import load_model3from tensorflow.keras.preprocessing import image4import numpy as np5from PIL import Image6import io7import os8import base649from flask_cors import CORS10 11 12 13app = Flask(__name__)14CORS(app)15 16#dataset clasess17class_name = {18    0: 'Blister Blight',19    1: 'Brown Blight',20    2: 'Gray Blight',21    3: 'Healthy',22    4: 'White Spot'23}24 25#load saved model26model_dir = './CNN_TEA_MODEL.h5'27model = load_model(model_dir)28 29 30    31        32 33@app.route("/predict", methods=["POST"])34def predictTest():35    36    if 'file' not in request.files:37        return jsonify({'error': 'No file part'}), 40038    file = request.files['file']39    if file.filename == '':40        return jsonify({'error': 'No selected file'}), 40041    if file:42        # Convert the file storage to PIL Image and ensure it's in RGB43        img = Image.open(io.BytesIO(file.read())).convert('RGB')  # Added .convert('RGB')44        img = img.resize((256, 256))45        img_array = np.array(img)46        img_array = np.expand_dims(img_array, axis=0)47        img_array = img_array / 255.0  # Normalize48        49        predictions = model.predict(img_array)50        #get the class with the highest probability51        predicted_class = np.argmax(predictions, axis=1)52        predicted_class_name = class_name[predicted_class[0]]53        54        result = {"class": predicted_class_name}55        print("Prediction: ", result)56        print(predictions)57        58        return jsonify({'prediction': result})59 60 61 62 63 64 65if __name__ == "__main__":66    app.run(debug=True)67 68 69