Revise if detection.

app.py

@@ -241,7 +241,7 @@ def compute_gradcam(model_gradcam, img_path, layer_name='bn'):
     # model_gradcam.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),
     #           loss='sparse_categorical_crossentropy')
     # model.load_weights('./pretrained_model.h5')
-    model_gradcam = keras.models.load_model('./gradcam_model.h5')
+    model_gradcam = keras.models.load_model('./model_renamed.h5')
     
     preprocessed_input = load_image(img_path)
     predictions = model_gradcam.predict(preprocessed_input)
@@ -456,40 +456,38 @@ if uploaded_file is not None:
         redirect_button("https://new-ohif-viewer-k7c3gdlxua-et.a.run.app/")
 
     with col2:
-        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)
-                # 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, input_image)
+        image = cv2.imdecode(file_bytes, 1) 
+        if st.button('Auto Detect'):
+            st.write("Processing...")
+            # 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, 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)
     
     with col3:
         if st.button('Generate Grad-CAM'):