Update app.py

app.py

@@ -1,50 +1,49 @@
-# from flask import Flask, request, jsonify, render_template
-# from tensorflow.keras.models import load_model
-# from tensorflow.keras.preprocessing import image
-# from efficientnet.tfkeras import preprocess_input
-# import numpy as np
-
-# app = Flask(__name__)
+from flask import Flask, request, jsonify, render_template
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+from efficientnet.tfkeras import preprocess_input
+import numpy as np
 
-# model = load_model('EfficientNet_ModelWeights.keras')
+app = Flask(__name__)
+model = load_model('EfficientNet_ModelWeights.keras')
 
-# def preprocess_and_predict(model, img_path, target_size=(224, 224)):
-#     # Load and preprocess the image
-#     img = image.load_img(img_path, target_size=target_size)
-#     img_array = image.img_to_array(img)
-#     img_array = np.expand_dims(img_array, axis=0)
-#     img_array = preprocess_input(img_array)
+def preprocess_and_predict(model, img_path, target_size=(224, 224)):
+    #Load and preprocess the image
+     img = image.load_img(img_path, target_size=target_size)
+     img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+     img_array = preprocess_input(img_array)
 
-#     # Make prediction
-#     prediction = model.predict(img_array)
-#     predicted_class = np.argmax(prediction) 
+     # Make prediction
+     prediction = model.predict(img_array)
+     predicted_class = np.argmax(prediction) 
 
-#     # Return the predicted class
-#     return predicted_class
+     # Return the predicted class
+   return predicted_class
 
-# @app.route('/')
-# def home():
-#     return render_template('index.html')
+ @app.route('/')
+ def home():
+     return render_template('index.html')
 
-# @app.route('/predict', methods=['POST'])
-# def predict():
-#     if 'file' not in request.files:
-#         return jsonify({'error': 'No file part'})
+@app.route('/predict', methods=['POST'])
+ def predict():
+     if 'file' not in request.files:
+         return jsonify({'error': 'No file part'})
 
-#     file = request.files['file']
+     file = request.files['file']
 
-#     # Save the uploaded file temporarily
-#     file_path = 'temp_image.jpg'
-#     file.save(file_path)
+     # Save the uploaded file temporarily
+    file_path = 'temp_image.jpg'
+     file.save(file_path)
 
-#     # Make prediction
-#     predicted_class = preprocess_and_predict(model, file_path)
+    # Make prediction
+    predicted_class = preprocess_and_predict(model, file_path)
 
-#     # Return the predicted class as a response
-#     return render_template('index.html', prediction=predicted_class)
+    # Return the predicted class as a response
+    return render_template('index.html', prediction=predicted_class)
 
-# if __name__ == '__main__':
-#     app.run(debug=True)
+if __name__ == '__main__':
+    app.run(debug=True)
 
 import streamlit as st
 from tensorflow.keras.models import load_model