Create app.py

app.py

@@ -0,0 +1,55 @@
+!pip install gradio ultralytics opencv-python
+
+import gradio as gr
+from ultralytics import YOLO
+import cv2
+import numpy as np
+
+# Load the model (do this outside the function for efficiency)
+try:
+    model = YOLO("yolov8n.pt")  # Or your model path
+except Exception as e:
+    print(f"Error loading model: {e}")
+    exit()  # Exit if the model fails to load
+
+def detect_pedestrians(video):
+    try:
+        cap = cv2.VideoCapture(video)
+        frames = []
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+
+            results = model(frame)
+
+            for result in results:
+                boxes = result.boxes
+                for box in boxes:
+                    if result.names[int(box.cls)] == 'person':
+                        x1, y1, x2, y2 = map(int, box.xyxy[0])
+                        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)
+            frames.append(frame)
+        cap.release()
+
+        if not frames: #if no frame return a blank image
+            return np.zeros((480, 640, 3), dtype=np.uint8)
+
+        # Combine frames into a video (optional - if you want to return a video)
+        # For simplicity, we'll return the last frame for now.
+        return frames[-1] #return the last frame
+    except Exception as e:
+        print(f"Error in detection: {e}")
+        return f"Error: {e}" # Return the error message as a string
+
+iface = gr.Interface(
+    fn=detect_pedestrians,
+    inputs=gr.Video(source="upload"),  # Use gr.Video for video input
+    outputs=gr.Image(),  # Use gr.Image for image output
+    title="Pedestrian Detection",
+    description="Upload a video to detect pedestrians.",
+    allow_flagging="never", #removes flag button
+)
+
+iface.launch()