Update src/streamlit_app.py

src/streamlit_app.py

@@ -151,38 +151,38 @@ if uploaded is not None:
 
     pts = to_xy(pred)
 
-st.subheader('Prediction (keypoints on original image)')
-
-w, h = pil_img.size  # original size
-scale_x = w / 96.0
-scale_y = h / 96.0
-
-pts_scaled = [(x * scale_x, y * scale_y) for (x, y) in pts]
-
-overlay = draw_keypoints(pil_img, pts_scaled)
-st.image(overlay, use_container_width=True)
-
-st.subheader('Prediction (keypoints on 96×96)')
-img96 = pil_img.resize((96, 96)).convert('RGB')
-overlay96 = draw_keypoints(img96, pts)
-st.image(overlay96, use_container_width=False)
-
-st.subheader('Keypoints table (x, y)')
-# Build a nice table using target_cols order
-# target_cols is typically like: ['left_eye_center_x', 'left_eye_center_y', ...]
-rows = []
-for i in range(0, len(target_cols), 2):
-    name_x = target_cols[i]
-    name_y = target_cols[i + 1]
-    rows.append({
-        'keypoint': name_x.replace('_x', ''),
-        'x_name': name_x,
-        'y_name': name_y,
-        'x': float(pred[i]),
-        'y': float(pred[i + 1]),
-    })
-
-st.dataframe(rows, use_container_width=True)
+    st.subheader('Prediction (keypoints on original image)')
+
+    w, h = pil_img.size  # original size
+    scale_x = w / 96.0
+    scale_y = h / 96.0
+
+    pts_scaled = [(x * scale_x, y * scale_y) for (x, y) in pts]
+
+    overlay = draw_keypoints(pil_img, pts_scaled)
+    st.image(overlay, use_container_width=True)
+
+    st.subheader('Prediction (keypoints on 96×96)')
+    img96 = pil_img.resize((96, 96)).convert('RGB')
+    overlay96 = draw_keypoints(img96, pts)
+    st.image(overlay96, use_container_width=False)
+
+    st.subheader('Keypoints table (x, y)')
+    # Build a nice table using target_cols order
+    # target_cols is typically like: ['left_eye_center_x', 'left_eye_center_y', ...]
+    rows = []
+    for i in range(0, len(target_cols), 2):
+        name_x = target_cols[i]
+        name_y = target_cols[i + 1]
+        rows.append({
+            'keypoint': name_x.replace('_x', ''),
+            'x_name': name_x,
+            'y_name': name_y,
+            'x': float(pred[i]),
+            'y': float(pred[i + 1]),
+        })
+
+    st.dataframe(rows, use_container_width=True)
 
 else:
     st.info('Upload an image to get predictions.')