Open input_image again.

app.py

@@ -467,11 +467,12 @@ if uploaded_file is not None:
         image = cv2.imdecode(file_bytes, 1) 
         if st.button('Auto Detect'):
             st.write("Processing...")
-            # input_image = preprocess_image(image)
+            input_image = preprocess_image(image)
             # input_image = enhancement_type
             input_image = cv2.resize(enhanced_image, (W, H))  # Resize the enhanced image to the required input size
             input_image = (input_image - 127.5) / 127.5  # Normalize to [-1, +1]
             input_image = np.expand_dims(input_image, axis=0).astype(np.float32)  # Expand dimensions and convert to float32
+            
             pred_bbox, pred_label, pred_label_confidence = predict(model_detection, input_image)
         
             # Updated label mapping based on the dataset