Update app.py

app.py

@@ -148,99 +148,3 @@ interface = gr.Interface(
 if __name__ == "__main__":
     interface.launch()
 
-"""""
-import gradio as gr
-import cv2
-import numpy as np
-import os
-import torch
-from ultralytics import YOLO
-import spaces  # Import ZeroGPU for Hugging Face Spaces
-
-@spaces.GPU  # Ensures GPU is allocated during execution
-def process_video(video_path):
-    """Process video using YOLOv8 for crowd detection."""
-    
-    # Check if CUDA is available
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    print(f"🔍 Using device: {device}")
-
-    # Load YOLOv8 model on GPU
-    model = YOLO("yolov8n.pt").to(device)
-
-    # Read input video
-    cap = cv2.VideoCapture(video_path)
-    if not cap.isOpened():
-        raise ValueError(f"❌ Failed to open video: {video_path}")
-
-    fps = int(cap.get(cv2.CAP_PROP_FPS))
-    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-
-    print(f"🎥 Video details - FPS: {fps}, Width: {width}, Height: {height}")
-
-    # Define output video path
-    output_path = "output_crowd.mp4"
-    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
-    out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
-
-    CROWD_THRESHOLD = 10  # Define crowd limit for alerts
-    frame_count = 0
-
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if not ret:
-            break  # End of video
-        
-        frame_count += 1
-
-        # Run YOLO inference on the frame
-        results = model(frame)
-
-        # Count detected persons
-        person_count = 0
-        for result in results:
-            boxes = result.boxes.xyxy.cpu().numpy()
-            classes = result.boxes.cls.cpu().numpy()
-
-            for box, cls in zip(boxes, classes):
-                if int(cls) == 0:  # YOLO class ID 0 = "person"
-                    person_count += 1
-                    x1, y1, x2, y2 = map(int, box)
-
-                    # Draw bounding box for persons
-                    cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
-                    cv2.putText(frame, "Person", (x1, y1 - 10),
-                                cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
-
-        # Display count on frame
-        alert_text = "Crowd Alert!" if person_count > CROWD_THRESHOLD else f"People: {person_count}"
-        cv2.putText(frame, alert_text, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1,
-                    (0, 0, 255) if person_count > CROWD_THRESHOLD else (0, 255, 0), 2)
-
-        out.write(frame)  # Save frame to output video
-
-    cap.release()
-    out.release()
-
-    if frame_count == 0:
-        raise ValueError("❌ No frames were processed!")
-
-    if not os.path.exists(output_path):
-        raise FileNotFoundError(f"❌ Output video not found: {output_path}")
-
-    print(f"✅ Processed video saved at: {output_path}")
-    return output_path
-
-# Gradio Interface
-interface = gr.Interface(
-    fn=process_video,
-    inputs=gr.Video(label="Upload Video"),
-    outputs=gr.Video(label="Processed Video"),
-    title="Crowd Detection with YOLOv8",
-    description="Upload a video, and YOLOv8 will detect and count people. If the crowd exceeds 10 people, a warning will be displayed."
-)
-
-if __name__ == "__main__":
-    interface.launch()
-""""