Uncomment all lines

app.py

@@ -1,6 +1,6 @@
 import streamlit as st
 import torch
-# import numpy as np
+import numpy as np
 import cv2
 import wget
 import os
@@ -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="")