app.py

import cv2
import gradio as gr
from ultralytics import YOLO

# Load the YOLO model (update the path to your fire detection model weights)
model = YOLO('path/to/your/best.pt')  # Replace 'path/to/your/best.pt' with the actual path to your model file

def detect_fire(frame):
    # Convert the frame to RGB format (YOLO expects this format)
    img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    
    # Perform fire detection using the YOLO model
    results = model(img)

    # Draw bounding boxes and labels on the detected fire areas
    for bbox in results[0].boxes:
        xyxy = bbox.xyxy[0]  # Bounding box coordinates
        conf = bbox.conf[0]  # Confidence score
        cls = int(bbox.cls[0])  # Class ID
        label = model.names[cls]  # Class name

        if label == "fire":  # Make sure this matches the label in your trained model
            # Draw a rectangle around the detected fire
            cv2.rectangle(img, (int(xyxy[0]), int(xyxy[1])), (int(xyxy[2]), int(xyxy[3])), (255, 0, 0), 2)
            # Put the label text above the rectangle
            cv2.putText(img, f"{label} {conf:.2f}", (int(xyxy[0]), int(xyxy[1]) - 10), 
                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)

    # Convert the image back to BGR format for display
    return cv2.cvtColor(img, cv2.COLOR_RGB2BGR)

# Create a Gradio interface for fire detection
iface = gr.Interface(
    fn=detect_fire,
    inputs=gr.Image(source="webcam", tool="editor", streaming=True),  # Use webcam as the input source
    outputs="image",
    title="Fire Detection using YOLO",
    description="This application detects fire in real-time using a YOLO model."
)

# Launch the Gradio app
iface.launch()