Update app.py

app.py

@@ -81,45 +81,45 @@ def draw_bbox(image, bbox):
 
 # st.title("Chest X-ray Disease Detection")
 
-# st.write("Upload a chest X-ray image and click on 'Detect' to find out if there's any disease.")
+st.write("Upload a chest X-ray image and click on 'Detect' to find out if there's any disease.")
 
 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=False, width=320, height=320)
+
+    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=False, width=320, height=320)
 
 @st.cache_resource
 def load_gradcam_model():
@@ -509,39 +509,39 @@ 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)
+        # 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)
+        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'
-            }
+        #     # 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}")
+        #     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)
+        #         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)