Update app.py

app.py

@@ -1,75 +1,75 @@
-import os
-import tensorflow as tf
-from flask import Flask, request, render_template, redirect, url_for
-from werkzeug.utils import secure_filename
-
-# Initialize the Flask application
-app = Flask(__name__)
-
-# --- Load the Clean, Compatible .h5 Model ---
-# This model was saved with save_format='h5' for maximum compatibility.
-MODEL_PATH = 'waste_classifier_v2_clean.h5'
-try:
-    model = tf.keras.models.load_model(MODEL_PATH)
-    print("Image classification model loaded successfully!")
-except Exception as e:
-    print(f"Error loading image model: {e}")
-    exit()
-
-# Define the class names in the correct order for the model's output
-CLASS_NAMES = ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash']
-
-def preprocess_image(image_path):
-    """
-    Loads an image from a file path and preprocesses it for the model.
-    This function ensures the input image matches the format used during training.
-    """
-    img = tf.keras.preprocessing.image.load_img(image_path, target_size=(224, 224))
-    img_array = tf.keras.preprocessing.image.img_to_array(img)
-    img_array = tf.expand_dims(img_array, 0) # Create a batch of one
-    # Apply the MobileNetV2-specific preprocessing
-    return tf.keras.applications.mobilenet_v2.preprocess_input(img_array)
-
-@app.route('/', methods=['GET'])
-def index():
-    """Renders the main upload page."""
-    return render_template('index.html')
-
-@app.route('/predict', methods=['POST'])
-def predict():
-    """Handles the image upload, prediction, and renders the result."""
-    # Check if a file was uploaded
-    if 'file' not in request.files:
-        return redirect(request.url)
-    file = request.files['file']
-    if file.filename == '':
-        return redirect(request.url)
-
-    if file:
-        # Save the file securely
-        filename = secure_filename(file.filename)
-        filepath = os.path.join('static/uploads', filename)
-        file.save(filepath)
-
-        # Preprocess the image and get a prediction
-        preprocessed_image = preprocess_image(filepath)
-        prediction = model.predict(preprocessed_image)
-        
-        # Decode the prediction
-        predicted_class_index = tf.argmax(prediction[0]).numpy()
-        predicted_class = CLASS_NAMES[predicted_class_index]
-        confidence = tf.reduce_max(prediction[0]).numpy() * 100
-
-        # Pass the results to the HTML template
-        return render_template('index.html',
-                               prediction=f'Prediction: {predicted_class}',
-                               confidence=f'Confidence: {confidence:.2f}%',
-                               uploaded_image=filepath)
-    return redirect(request.url)
-
-if __name__ == '__main__':
-    # Ensure the upload folder exists
-    os.makedirs('static/uploads', exist_ok=True)
-    # This host and port configuration is important for deployment services like Hugging Face
-    app.run(host='0.0.0.0', port=int(os.environ.get('PORT', 7860)))
-
+import os
+import tensorflow as tf
+from flask import Flask, request, render_template, redirect, url_for
+from werkzeug.utils import secure_filename
+
+# Initialize the Flask application
+app = Flask(__name__)
+
+# --- Load the Clean, Compatible .h5 Model ---
+# This model was saved with save_format='h5' for maximum compatibility.
+MODEL_PATH = 'waste_classifier_final_5.h5'
+try:
+    model = tf.keras.models.load_model(MODEL_PATH)
+    print("Image classification model loaded successfully!")
+except Exception as e:
+    print(f"Error loading image model: {e}")
+    exit()
+
+# Define the class names in the correct order for the model's output
+CLASS_NAMES = ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash']
+
+def preprocess_image(image_path):
+    """
+    Loads an image from a file path and preprocesses it for the model.
+    This function ensures the input image matches the format used during training.
+    """
+    img = tf.keras.preprocessing.image.load_img(image_path, target_size=(224, 224))
+    img_array = tf.keras.preprocessing.image.img_to_array(img)
+    img_array = tf.expand_dims(img_array, 0) # Create a batch of one
+    # Apply the MobileNetV2-specific preprocessing
+    return tf.keras.applications.mobilenet_v2.preprocess_input(img_array)
+
+@app.route('/', methods=['GET'])
+def index():
+    """Renders the main upload page."""
+    return render_template('index.html')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    """Handles the image upload, prediction, and renders the result."""
+    # Check if a file was uploaded
+    if 'file' not in request.files:
+        return redirect(request.url)
+    file = request.files['file']
+    if file.filename == '':
+        return redirect(request.url)
+
+    if file:
+        # Save the file securely
+        filename = secure_filename(file.filename)
+        filepath = os.path.join('static/uploads', filename)
+        file.save(filepath)
+
+        # Preprocess the image and get a prediction
+        preprocessed_image = preprocess_image(filepath)
+        prediction = model.predict(preprocessed_image)
+        
+        # Decode the prediction
+        predicted_class_index = tf.argmax(prediction[0]).numpy()
+        predicted_class = CLASS_NAMES[predicted_class_index]
+        confidence = tf.reduce_max(prediction[0]).numpy() * 100
+
+        # Pass the results to the HTML template
+        return render_template('index.html',
+                               prediction=f'Prediction: {predicted_class}',
+                               confidence=f'Confidence: {confidence:.2f}%',
+                               uploaded_image=filepath)
+    return redirect(request.url)
+
+if __name__ == '__main__':
+    # Ensure the upload folder exists
+    os.makedirs('static/uploads', exist_ok=True)
+    # This host and port configuration is important for deployment services like Hugging Face
+    app.run(host='0.0.0.0', port=int(os.environ.get('PORT', 7860)))
+