app.py

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)))