Update app.py

app.py

@@ -6,30 +6,30 @@ from PIL import Image
 # Load model
 model = tf.keras.models.load_model("model.h5")
 
-# Correct class names in training order
+# Classes in training order
 class_names = ['batteries', 'clothes', 'e-waste', 'glass', 'light blubs', 'metal', 'organic', 'paper', 'plastic']
-
-# Match training input size
 IMG_SIZE = (224, 224)
 
 def preprocess(image):
     image = image.resize(IMG_SIZE)
     image_array = tf.keras.preprocessing.image.img_to_array(image)
-    image_array = image_array / 255.0  # Normalize if this was done in training
+    image_array = image_array / 255.0  # Normalize
     return np.expand_dims(image_array, axis=0)
 
 def predict(image):
     input_tensor = preprocess(image)
     predictions = model.predict(input_tensor)[0]
-    top_class = class_names[np.argmax(predictions)]
-    confidence = np.max(predictions) * 100
-    return f"{top_class} ({confidence:.2f}%)"
+    top_idx = np.argmax(predictions)
+    top_class = class_names[top_idx]
+    confidence = predictions[top_idx] * 100
+    return f"Predicted: {top_class} ({confidence:.2f}%)"
 
 demo = gr.Interface(
     fn=predict,
-    inputs=gr.Image(type="pil", label="Upload an image of a recyclable item to classify it."),
-    outputs=gr.Textbox(label="Output"),
-    title="Waste Image Classifier"
+    inputs=gr.Image(type="pil", label="Upload an image of a recyclable item"),
+    outputs=gr.Textbox(label="Prediction"),
+    title="♻️ Waste Image Classifier",
+    description="Classify recyclable waste images into 9 categories using a MobileNetV2-based model."
 )
 
 if __name__ == "__main__":