import gradio as gr
import cv2
import numpy as np
from detect_people import detect_people_from_image
from table_occupancy import is_table_occupied
from face_utils import recognize_face
from db import get_table_status, log_customer_visit, get_alerts

# Gradio interface function to analyze uploaded image
def analyze_image(image):
    try:
        # Ensure image is in correct format (NumPy array, BGR for OpenCV)
        if not isinstance(image, np.ndarray):
            image = np.array(image)
        if image.shape[-1] == 4:  # Convert RGBA to BGR if needed
            image = cv2.cvtColor(image, cv2.COLOR_RGBA2BGR)
        elif image.shape[-1] == 3:  # Ensure RGB is converted to BGR
            image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
        
        # Run analysis functions
        people = detect_people_from_image(image)
        seated = is_table_occupied(image)
        role = recognize_face(image)
        
        # Log a visit to the database (adjust timestamp as needed)
        import datetime
        log_customer_visit(role, datetime.datetime.now(), table_id=1)
        
        # Get table status and alerts
        table_status = get_table_status()
        alerts = get_alerts()
        
        # Format output
        output = (
            f"People detected: {people}\n"
            f"Table occupied: {seated}\n"
            f"Face match: {role}\n"
            f"Table status: {table_status}\n"
            f"Active alerts: {alerts}"
        )
        return output
    except Exception as e:
        return f"Error processing image: {e}"

# Define Gradio interface
demo = gr.Interface(
    fn=analyze_image,
    inputs=gr.Image(type="numpy"),  # Expect NumPy array input
    outputs="text",
    title="Table Occupancy and Face Detection",
    description="Upload an image to detect people, table occupancy, and recognize faces."
)

# Start the Gradio app
if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860, share=False)