app.py

import cv2
import gradio as gr
from transformers import pipeline

# Load Hugging Face models
pose_detection = pipeline("object-detection", model="./yolov8-pose")  # Local YOLOv8 pose model
suspicious_activity_detection = pipeline("text-classification", model="./suspicious_activity_model")  # Local LSTM model

# Function to process a single frame and predict suspicious activity
def process_frame(frame):
    """
    Detect persons in the frame and determine if they exhibit suspicious behavior.
    """
    results = pose_detection(frame)

    for person in results:
        if person['label'] == 'person' and 'box' in person:
            x1, y1, x2, y2 = map(int, person['box'].values())

            if 'keypoints' in person:
                keypoints = person['keypoints']
                keypoints_input = " ".join(map(str, [kp for point in keypoints for kp in point[:2]]))

                # Predict suspicious activity
                prediction = suspicious_activity_detection(keypoints_input)[0]['label']
                color = (0, 0, 255) if prediction == "Suspicious" else (0, 255, 0)
                label = 'Suspicious' if prediction == "Suspicious" else 'Normal'

                # Annotate frame
                cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
                cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
            else:
                print("No keypoints found for detected person.")
    return frame

# Gradio interface
def live_detection(frame):
    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)  # Convert BGR to RGB for Gradio
    processed_frame = process_frame(frame)
    return cv2.cvtColor(processed_frame, cv2.COLOR_RGB2BGR)  # Convert back to BGR for display

# Gradio UI
interface = gr.Interface(
    fn=live_detection,
    inputs=gr.Image(source="webcam", tool="editor", type="numpy"),
    outputs=gr.Image(type="numpy", label="Processed Video Stream"),
    live=True,
    description="Real-time Suspicious Activity Detection"
)

if __name__ == "__main__":
    interface.launch(server_name="0.0.0.0", server_port=7860)