Update app.py

app.py

@@ -85,41 +85,41 @@ def draw_bbox(image, bbox):
 
 model_detection = load_model_detection()
 
-uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png", "dcm"])
-
-if uploaded_file is not None:
-    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)
-
-    if st.button('Detect'):
-        st.write("Processing...")
-        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',
-            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)
+# uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png", "dcm"])
+
+# if uploaded_file is not None:
+    # 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)
+
+    # if st.button('Detect'):
+    #     st.write("Processing...")
+    #     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',
+    #         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)
 
 @st.cache_resource
 def load_gradcam_model():
@@ -509,9 +509,44 @@ if uploaded_file is not None:
         redirect_button("https://new-ohif-viewer-k7c3gdlxua-et.a.run.app/")
 
     with col2:
+        model_detection = load_model_detection()
+        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)
+    
+        if st.button('Detect'):
+            st.write("Processing...")
+            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',
+                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)
+
+
         # file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
         # image = cv2.imdecode(file_bytes, 1)
-        st.button('Auto Detect')
+        # st.button('Auto Detect')
             # st.write("Processing...")
             # input_image = preprocess_image(image)
             # pred_bbox, pred_label, pred_label_confidence = predict(model_detection, input_image)