main.py

import streamlit as st
from ultralytics import YOLO
import cv2
import numpy as np
from PIL import Image

# Load the YOLO model (replace with your model path)
model = YOLO("D:\\streamlit\\my_streamlit_app\\fire\\best.pt")
   # Use your YOLO model file here

st.title("Fire Detection in Forest")

# Sidebar for input options
input_option = st.sidebar.selectbox("Select Input Method", ["Upload Image", "Use Webcam"])

if input_option == "Upload Image":
    # Upload Image
    uploaded_file = st.file_uploader("Choose an Image", type=["jpg", "jpeg", "png"])

    if uploaded_file is not None:
        img = Image.open(uploaded_file)
        st.image(img, caption='User Image')
        st.write("Classifying...")

        # Convert image to numpy array
        img_np = np.array(img)

        # Make predictions
        results = model.predict(source=img_np, conf=0.5)

        # Draw bounding boxes on the image
        for result in results:
            boxes = result.boxes.xyxy
            for box in boxes:
                x1, y1, x2, y2 = box[:4].astype(int)
                img_np = cv2.rectangle(img_np, (x1, y1), (x2, y2), (0, 255, 0), 2)
        
        # Show the resulting image
        st.image(img_np, caption='Detected Fire', use_column_width=True)

elif input_option == "Use Webcam":
    st.write("Starting webcam for live detection...")
    
    # Start video capture
    camera = cv2.VideoCapture(0)  # 0 is the default camera

    # Create a placeholder for the video feed
    video_placeholder = st.empty()

    # Main loop for live detection
    while True:
        ret, frame = camera.read()
        if not ret:
            st.write("Failed to capture image")
            break

        # Make predictions
        results = model.predict(source=frame, conf=0.5)

        # Draw bounding boxes on the frame
        for result in results:
            boxes = result.boxes.xyxy
            for box in boxes:
                x1, y1, x2, y2 = box[:4].astype(int)
                frame = cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)

        # Convert frame to RGB
        rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

        # Display the frame in the Streamlit app
        video_placeholder.image(rgb_frame, channels="RGB", use_column_width=True)

        # Break loop on user command
        if st.button("Stop Detection"):
            break

    # Release the camera
    camera.release()