Remove big center image from detection.

app.py

@@ -82,39 +82,11 @@ model = load_model()
 
 # uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
 
-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, 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)
+
+    # st.image(image, caption='Uploaded Image.', use_column_width=True)
     
 
 # Utility Functions