README.md

metadata

title: Wild Fire Tracker
emoji: 🔥
colorFrom: yellow
colorTo: purple
sdk: gradio
sdk_version: 5.33.1
app_file: app.py
pinned: false
license: mit
short_description: A wildfire monitor that alerts authorities in event of fire
tag: mcp-server-track

🔥 Wild Fire Tracker

Real-time fire and smoke detection system using computer vision and AI. Available as both a web application and MCP (Model Context Protocol) server.

Features

• 🔥 Fire Detection: AI-powered fire detection with confidence scores
• 💨 Smoke Detection: Advanced smoke detection capabilities
• 📹 Multiple Sources: Webcam, RTSP streams, and video file uploads
• ⏱️ Real-time Analysis: Frame analysis every 10 seconds
• 🎨 Visual Feedback: Color-coded status overlays on video
• 📊 Confidence Scores: Shows detection confidence percentages

Installation

pip install gradio opencv-python-headless torch transformers pillow numpy

Usage

1. Gradio Web Application

Run the web interface:

python app.py

Then open http://localhost:7860 in your browser.

Features:

• Upload video files (MP4, AVI, MOV)
• Connect to webcam or RTSP streams
• Real-time video display with status overlay
• Detection status with timestamps

2. Standalone MCP Server

For integration with MCP clients:

# Install MCP dependency
pip install mcp

# Run MCP server
python mcp_server_standalone.py

MCP Tools:

• analyze_image: Analyze base64 encoded images for fire/smoke

Example MCP Client Usage:

import base64
import json

# Encode image to base64
with open("image.jpg", "rb") as f:
    image_b64 = base64.b64encode(f.read()).decode()

# Call MCP tool
result = await client.call_tool("analyze_image", {
    "image_base64": image_b64
})

Sample Test Videos

• Fire Test Video
• Smoke Test Video

Status Legend

• 🟢 Green: All clear (no fire/smoke detected)
• 🟠 Orange: Fire detected
• ⚫ Gray: Smoke detected
• 🔴 Red: Error or both fire & smoke detected

License

MIT License - see LICENSE file for details.