import cv2
import numpy as np
import torch
import streamlit as st
import streamlit.components.v1 as components
from ultralytics import YOLO
from camera_input_live import camera_input_live
# Load YOLO fire detection model
model_path = "last.pt"
device = "cuda" if torch.cuda.is_available() else "cpu"
model = YOLO(model_path)
model.to(device)
# Streamlit app title
st.title("🔥 Live Fire Detection with Alarm 🔥")
st.subheader("Hold the camera towards potential fire sources to detect in real-time.")
# Load alarm sound from a public URL
alarm_url = "https://soundcloud.com/trending-music-in/sets/soul?utm_source=clipboard&utm_medium=text&utm_campaign=social_sharing" # Replace with your hosted alarm
# JavaScript to control alarm playback
js_code = f"""
"""
# Display the JavaScript once
components.html(js_code, height=0)
# Capture live camera input
image = camera_input_live()
if image is not None:
# Convert the image to OpenCV format
bytes_data = image.getvalue()
cv2_img = cv2.imdecode(np.frombuffer(bytes_data, np.uint8), cv2.IMREAD_COLOR)
# Perform fire detection
results = model(cv2_img)
fire_present = False # Flag for fire detection
# Draw bounding boxes for detected fires
for result in results:
boxes = result.boxes
for box in boxes:
b = box.xyxy[0].cpu().numpy().astype(int)
label = f'Fire {box.conf[0]:.2f}'
cv2.rectangle(cv2_img, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 3)
cv2.putText(cv2_img, label, (b[0], b[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
fire_present = True
# Display the annotated image
st.image(cv2_img, channels="BGR", caption="Detected Fire", use_container_width=True)
# Display logs & trigger alarm using JavaScript
if fire_present:
st.error("🔥 Fire Detected! 🔥")
components.html("", height=0)
else:
st.success("✅ No Fire Detected")
components.html("", height=0)