Revise file_bytes for detection.

app.py

@@ -86,7 +86,7 @@ model = load_model()
 #     file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
 #     image = cv2.imdecode(file_bytes, 1)
 
-    # st.image(image, caption='Uploaded Image.', use_column_width=True)
+#     st.image(image, caption='Uploaded Image.', use_column_width=True)
     
 
 # Utility Functions
@@ -440,33 +440,36 @@ if uploaded_file is not None:
         redirect_button("https://new-ohif-viewer-k7c3gdlxua-et.a.run.app/")
 
     with col2:
-        if st.button('Auto Detect'):
-            st.write("Processing...")
-            input_image = preprocess_image(image)
-            pred_bbox, pred_label, pred_label_confidence = predict(model, 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)
+        if uploaded_file is not None:
+            file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+            image = cv2.imdecode(file_bytes, 1) 
+            if st.button('Auto Detect'):
+                st.write("Processing...")
+                input_image = preprocess_image(image)
+                pred_bbox, pred_label, pred_label_confidence = predict(model, 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'):