Comment most of lines for debugging

app.py

@@ -38,48 +38,48 @@ with st.spinner("Wait for loading a model..."):
     predictor = get_predictor(model, device=device, **predictor_params)
 
 # Create a canvas component.
-image = None
-if image_path:
-    image = Image.open(image_path)
-canvas_height, canvas_width = 600, 600
-pos_color, neg_color = "#3498DB", "#C70039"
-st.title("Canvas:")
-canvas_result = st_canvas(
-        fill_color="rgba(255, 165, 0, 0.3)",  # Fixed fill color with some opacity
-        stroke_width=3,
-        stroke_color=pos_color if marking_type == "positive" else neg_color,
-        background_color="#eee",
-        background_image=image,
-        update_streamlit=True,
-        drawing_mode="point",
-        point_display_radius=3,
-        key="canvas",
-        width=canvas_width,
-        height=canvas_height,
-)
+#image = None
+#if image_path:
+#    image = Image.open(image_path)
+#canvas_height, canvas_width = 600, 600
+#pos_color, neg_color = "#3498DB", "#C70039"
+#st.title("Canvas:")
+#canvas_result = st_canvas(
+#        fill_color="rgba(255, 165, 0, 0.3)",  # Fixed fill color with some opacity
+#        stroke_width=3,
+#        stroke_color=pos_color if marking_type == "positive" else neg_color,
+#        background_color="#eee",
+#        background_image=image,
+#        update_streamlit=True,
+#        drawing_mode="point",
+#        point_display_radius=3,
+#        key="canvas",
+#        width=canvas_width,
+#        height=canvas_height,
+#)
 
 # Check the user inputs ans execute predictions.
-st.title("Prediction:")
-if canvas_result.json_data and canvas_result.json_data["objects"] and image:
-    objects = canvas_result.json_data["objects"]
-    image_width, image_height = image.size
-    ratio_h, ratio_w = image_height / canvas_height, image_width / canvas_width
-
-    err_x, err_y = 5.5, 1.0
-    pos_clicks, neg_clicks = [], []
-    for click in objects:
-        x, y = (click["left"] + err_x) * ratio_w, (click["top"] + err_y) * ratio_h
-        x, y = min(image_width, max(0, x)), min(image_height, max(0, y))
-
-        is_positive = click["stroke"] == pos_color
-        click = ck.Click(is_positive=is_positive, coords=(y, x))
-        clicker.add_click(click)
-
-    # prediction.
-    pred = None
-    predictor.set_input_image(np.array(image))
-    with st.spinner("Wait for prediction..."):
-        pred = predictor.get_prediction(clicker, prev_mask=None)
-    pred = cv2.resize(pred, dsize=(canvas_height, canvas_width), interpolation=cv2.INTER_CUBIC)
-    pred = np.where(pred > threshold, 1.0, 0)
-    st.image(pred, caption="")
+#st.title("Prediction:")
+#if canvas_result.json_data and canvas_result.json_data["objects"] and image:
+#    objects = canvas_result.json_data["objects"]
+#    image_width, image_height = image.size
+#    ratio_h, ratio_w = image_height / canvas_height, image_width / canvas_width
+#
+#    err_x, err_y = 5.5, 1.0
+#    pos_clicks, neg_clicks = [], []
+#    for click in objects:
+#        x, y = (click["left"] + err_x) * ratio_w, (click["top"] + err_y) * ratio_h
+#        x, y = min(image_width, max(0, x)), min(image_height, max(0, y))
+#
+#        is_positive = click["stroke"] == pos_color
+#        click = ck.Click(is_positive=is_positive, coords=(y, x))
+#        clicker.add_click(click)
+#
+#    # prediction.
+#    pred = None
+#    predictor.set_input_image(np.array(image))
+#    with st.spinner("Wait for prediction..."):
+#        pred = predictor.get_prediction(clicker, prev_mask=None)
+#    pred = cv2.resize(pred, dsize=(canvas_height, canvas_width), interpolation=cv2.INTER_CUBIC)
+#    pred = np.where(pred > threshold, 1.0, 0)
+#    st.image(pred, caption="")