Update app.py

app.py

@@ -458,34 +458,6 @@ if uploaded_file is not None:
             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)
 
             col1, col2 = st.columns(2)
             # Check the number of dimensions of the image