this uplode code

app.py

@@ -4,9 +4,10 @@ import numpy as np
 import gradio as gr
 import tempfile
 
-# Load model
+# Path to hand landmark model file (make sure it's in your repo!)
 MODEL_PATH = "hand_landmarker.task"
 
+# MediaPipe setup
 BaseOptions = mp.tasks.BaseOptions
 HandLandmarker = mp.tasks.vision.HandLandmarker
 HandLandmarkerOptions = mp.tasks.vision.HandLandmarkerOptions
@@ -14,7 +15,7 @@ VisionRunningMode = mp.tasks.vision.RunningMode
 mp_image = mp.Image
 mp_format = mp.ImageFormat
 
-# Finger connections and colors
+# Define hand connections and colors for visualization
 HAND_CONNECTIONS = [
     (0, 1), (1, 2), (2, 3), (3, 4),
     (0, 5), (5, 6), (6, 7), (7, 8),
@@ -46,18 +47,28 @@ def get_finger_color(start_idx):
     else:
         return FINGER_COLORS['palm']
 
-def process_video(video_path):
+def process_video(video_file):
+    # Gradio may send a dict or path string depending on how input is passed
+    if isinstance(video_file, dict):
+        video_path = video_file["name"]
+    else:
+        video_path = video_file
+
     cap = cv2.VideoCapture(video_path)
+    if not cap.isOpened():
+        raise ValueError("Could not open video.")
+
+    fps = cap.get(cv2.CAP_PROP_FPS) or 24
+    w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
 
+    # Prepare output video path
     fourcc = cv2.VideoWriter_fourcc(*'mp4v')
     tmp_out = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
     out_path = tmp_out.name
-
-    fps = cap.get(cv2.CAP_PROP_FPS)
-    w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-    h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
     out = cv2.VideoWriter(out_path, fourcc, fps, (w, h))
 
+    # Load hand detection model
     options = HandLandmarkerOptions(
         base_options=BaseOptions(model_asset_path=MODEL_PATH),
         running_mode=VisionRunningMode.IMAGE,
@@ -80,12 +91,10 @@ def process_video(video_path):
             if results.hand_landmarks:
                 for hand_landmarks in results.hand_landmarks:
                     points = [(int(lm.x * w), int(lm.y * h)) for lm in hand_landmarks]
-
                     for start, end in HAND_CONNECTIONS:
                         color = get_finger_color(start)
                         cv2.line(frame, points[start], points[end], color, 2)
-
-                    for i, (x, y) in enumerate(points):
+                    for x, y in points:
                         cv2.circle(frame, (x, y), 4, (0, 255, 255), -1)
 
             out.write(frame)
@@ -94,13 +103,14 @@ def process_video(video_path):
     out.release()
     return out_path
 
-# Gradio interface
+# Gradio app interface
 demo = gr.Interface(
     fn=process_video,
-    inputs=gr.Video(label="Upload Video or Use Webcam"),
+    inputs=gr.Video(label="Upload Video or Record via Webcam"),
     outputs=gr.Video(label="Hand Landmark Annotated Video"),
-    title="Hand Detection ",
-    description="Upload a video or use webcam to detect hands."
+    title="🖐️ Hand Detection using MediaPipe",
+    description="Upload a video or record from webcam. The system will detect hands and annotate keypoints using MediaPipe HandLandmarker."
 )
 
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()