Upload app.py

app.py

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+import os
+import base64
+import tempfile
+import tensorflow as tf
+from flask import Flask, request, render_template, redirect
+from io import BytesIO
+
+# Initialize application
+app = Flask(__name__)
+
+# Load the Model
+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()
+
+CLASS_NAMES = ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash']
+
+
+def preprocess_image(image_path):
+    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)
+    
+    return tf.keras.applications.efficientnet.preprocess_input(img_array)
+
+@app.route('/', methods=['GET'])
+def index():
+    return render_template('index.html')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    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)))