Create app.py

app.py

@@ -0,0 +1,94 @@
+import gradio as gr
+import cv2
+import numpy as np
+from ultralytics import YOLO
+from huggingface_hub import hf_hub_download
+import tempfile
+import os
+
+# --- 1. SET UP MODELS ---
+# Downloading specialized volleyball models from Davidsv/volley-ref-ai
+try:
+    court_model_path = hf_hub_download(repo_id="Davidsv/volley-ref-ai", filename="yolo_court_keypoints.pt")
+    ball_model_path = hf_hub_download(repo_id="Davidsv/volley-ref-ai", filename="yolo_volleyball_ball.pt")
+    
+    court_model = YOLO(court_model_path)
+    ball_model = YOLO(ball_model_path)
+    pose_model = YOLO("yolo11n-pose.pt")  # General human pose model
+except Exception as e:
+    print(f"Error loading models: {e}")
+
+def process_volleyball_video(video_path):
+    if not video_path:
+        return None
+    
+    cap = cv2.VideoCapture(video_path)
+    width  = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    fps    = int(cap.get(cv2.CAP_PROP_FPS))
+    
+    # Create a temporary file to save the processed video
+    temp_output = tempfile.NamedTemporaryFile(delete=False, suffix='.mp4')
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    out = cv2.VideoWriter(temp_output.name, fourcc, fps, (width, height))
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        # Run Detections
+        court_res = court_model(frame, verbose=False)[0]
+        pose_res = pose_model(frame, verbose=False)[0]
+        ball_res = ball_model(frame, verbose=False)[0]
+
+        annotated_frame = frame.copy()
+
+        # Logic: Find the Net height (using court keypoints)
+        # Usually keypoints 6 and 7 in volleyball court models represent the net top
+        net_y = height // 2 # Default fallback
+        if court_res.keypoints is not None and len(court_res.keypoints.xy[0]) > 7:
+            net_y = int(court_res.keypoints.xy[0][6][1]) # Y-coord of net top
+
+        # Process Players
+        if pose_res.keypoints is not None:
+            for i, person in enumerate(pose_res.keypoints.xy):
+                if len(person) < 11: continue
+                
+                # Get key joints (indices: 5=L_Shoulder, 6=R_Shoulder, 9=L_Wrist, 10=R_Wrist)
+                l_shoulder, r_shoulder = person[5], person[6]
+                l_wrist, r_wrist = person[9], person[10]
+
+                # ANALYSIS 1: Detection of a "Spike" (Hand above shoulder)
+                if (l_wrist[1] < l_shoulder[1] or r_wrist[1] < r_shoulder[1]) and l_wrist[1] > 0:
+                    cv2.putText(annotated_frame, "SPIKE ATTACK", (int(l_shoulder[0]), int(l_shoulder[1]-20)),
+                                cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2)
+
+                # ANALYSIS 2: Net Touch Mistake
+                # If wrist is near the net y-coordinate and moving forward
+                if abs(l_wrist[1] - net_y) < 10 or abs(r_wrist[1] - net_y) < 10:
+                    cv2.putText(annotated_frame, "WARNING: NET TOUCH", (50, 50 + (i*30)),
+                                cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 3)
+
+        # Draw detections
+        annotated_frame = pose_res.plot(img=annotated_frame)
+        annotated_frame = court_res.plot(img=annotated_frame)
+        
+        out.write(annotated_frame)
+
+    cap.release()
+    out.release()
+    return temp_output.name
+
+# --- 3. GRADIO INTERFACE ---
+interface = gr.Interface(
+    fn=process_volleyball_video,
+    inputs=gr.Video(label="Upload Volleyball Match"),
+    outputs=gr.Video(label="AI Analysis (Detections & Mistakes)"),
+    title="🏐 AI Volleyball Performance Lab",
+    description="This app uses YOLOv11 and specialized Volleyball-Ref-AI models to detect court lines, ball movement, and player form to identify mistakes.",
+    theme="soft"
+)
+
+if __name__ == "__main__":
+    interface.launch()