Update app.py

app.py

@@ -17,15 +17,16 @@ def predict_regression(image):
     image = image.resize((150, 150))
     # If model expects RGB, convert to RGB
     image = image.convert('RGB')  # Ensure image is in RGB format
-    image = np.array(image, dtype=np.float32)  # Convert image to float32
-    image /= 255.0  # Normalize image data to 0-1 range
 
-    print(image.shape)  # Debugging: Check the shape to ensure it's correct
+
+    image = np.array(image)
+    print(image.shape)
     # Predict
-    prediction = model.predict(image[None, ...])  # Adjusted to include batch dimension properly
+    prediction = model.predict(image[None, ...])  # Assuming single regression value
     confidences = {labels[i]: np.round(float(prediction[0][i]), 2) for i in range(len(labels))}
     return confidences
 
+
 # Create Gradio interface
 input_image = gr.Image()
 output_text = gr.Textbox(label="Predicted Value")