Delete app.py

app.py

@@ -1,76 +0,0 @@
-import os
-import base64
-import tempfile
-import tensorflow as tf
-from flask import Flask, request, render_template, redirect
-from io import BytesIO
-
-# Initialize the Flask application
-app = Flask(__name__)
-
-# --- Load the Model ---
-# This now points to the directory created by model.export()
-MODEL_PATH = 'waste_classifier_final_5.h5' # IMPORTANT: Ensure this matches your uploaded model's filename
-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()
-
-# --- CRITICAL: Ensure this list EXACTLY matches the output from your training script ---
-# Example: ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash']
-CLASS_NAMES = ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash']
-
-
-def preprocess_image(image_path):
-    """Loads and preprocesses an image for the model."""
-    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)
-    # --- UPDATED: Switched to the correct preprocessing for EfficientNet ---
-    return tf.keras.applications.efficientnet.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 image upload, prediction, and renders the result."""
-    if 'file' not in request.files:
-        return redirect(request.url)
-    file = request.files['file']
-    if file.filename == '':
-        return redirect(request.url)
-
-    if file:
-        with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(file.filename)[1]) as tmp_file:
-            filepath = tmp_file.name
-            file.save(filepath)
-
-        with open(filepath, "rb") as f:
-            image_data = f.read()
-        
-        encoded_image = base64.b64encode(image_data).decode('utf-8')
-        image_to_display = f"data:image/jpeg;base64,{encoded_image}"
-
-        preprocessed_image = preprocess_image(filepath)
-        prediction = model.predict(preprocessed_image)
-        
-        predicted_class_index = tf.argmax(prediction[0]).numpy()
-        predicted_class = CLASS_NAMES[predicted_class_index]
-        confidence = tf.reduce_max(prediction[0]).numpy() * 100
-
-        os.remove(filepath)
-
-        return render_template('index.html',
-                               prediction=f'Prediction: {predicted_class}',
-                               confidence=f'Confidence: {confidence:.2f}%',
-                               uploaded_image=image_to_display)
-    
-    return redirect(request.url)
-
-if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=int(os.environ.get('PORT', 7860)))