Update app.py

app.py

@@ -469,14 +469,9 @@ if uploaded_file is not None:
         image = cv2.imdecode(file_bytes, 1) 
         if st.button('Auto Detect'):
             st.write("Processing...")
-            input_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
-            
+            input_image = preprocess_image(image)
             pred_bbox, pred_label, pred_label_confidence = predict(model_detection, input_image)
-        
+    
             # Updated label mapping based on the dataset
             label_mapping = {
                 0: 'Atelectasis',
@@ -488,7 +483,7 @@ if uploaded_file is not None:
                 6: 'Pneumonia',
                 7: 'Pneumothorax'
             }
-        
+    
             if pred_label_confidence < 0.2:
                 st.write("May not detect a disease.")
             else:
@@ -496,9 +491,43 @@ if uploaded_file is not None:
                 st.write(f"Prediction Label: {pred_label_name}")
                 st.write(f"Prediction Bounding Box: {pred_bbox}")
                 st.write(f"Prediction Confidence: {pred_label_confidence:.2f}")
-        
+    
                 output_image = draw_bbox(image.copy(), pred_bbox)
                 st.image(output_image, caption='Detected Image.', use_column_width=True)
+
+
+        # if st.button('Auto Detect'):
+        #     st.write("Processing...")
+        #     input_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
+        #     label_mapping = {
+        #         0: 'Atelectasis',
+        #         1: 'Cardiomegaly',
+        #         2: 'Effusion',
+        #         3: 'Infiltrate',
+        #         4: 'Mass',
+        #         5: 'Nodule',
+        #         6: 'Pneumonia',
+        #         7: 'Pneumothorax'
+        #     }
+        
+        #     if pred_label_confidence < 0.2:
+        #         st.write("May not detect a disease.")
+        #     else:
+        #         pred_label_name = label_mapping[pred_label]
+        #         st.write(f"Prediction Label: {pred_label_name}")
+        #         st.write(f"Prediction Bounding Box: {pred_bbox}")
+        #         st.write(f"Prediction Confidence: {pred_label_confidence:.2f}")
+        
+        #         output_image = draw_bbox(image.copy(), pred_bbox)
+        #         st.image(output_image, caption='Detected Image.', use_column_width=True)
     
     with col3:
         if st.button('Generate Grad-CAM'):