https://github.com/barbaraeguche/pyrocast
🚒 a proactive wildfire prediction & analysis built with react & flask.
https://github.com/barbaraeguche/pyrocast
ai flask ml pandas react scikit-learn vite
Last synced: 3 months ago
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🚒 a proactive wildfire prediction & analysis built with react & flask.
- Host: GitHub
- URL: https://github.com/barbaraeguche/pyrocast
- Owner: barbaraeguche
- License: mit
- Created: 2025-02-02T10:04:01.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-10-17T00:59:55.000Z (8 months ago)
- Last Synced: 2025-10-27T22:47:00.464Z (8 months ago)
- Topics: ai, flask, ml, pandas, react, scikit-learn, vite
- Language: TypeScript
- Homepage: https://pyrocast.vercel.app
- Size: 117 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
- License: license
Awesome Lists containing this project
README
# pyrocast 🚒
a real-time wildfire analysis and prediction dashboard that helps monitor, analyze, and predict wildfire incidents in
quebec using machine learning built for [**conuhacks-ix**](https://drive.google.com/file/d/1THw9CLQd4ynGZGwxXhtVPb1ZpVTQxTZ8/view) as part of the [**sap**](https://www.sap.com/canada/index.html) task.
## tech stack ✨
- **frontend:** vite + react with tailwind css and framer motion for smooth animations
- **backend:** flask with scikit-learn for ml predictions
- **data processing:** pandas for data manipulation and analysis
- **deployment:** frontend hosted on vercel; backend deployed on render.
## features 👾
- **smart file upload:** handles multiple csv files with validation and progress tracking
- **resource management:** tracks firefighting resources and optimizes their allocation
- **incident analysis:** comprehensive reporting of fire incidents with severity levels
- **risk prediction:** machine learning-based prediction of future fire risks using environmental data
- **geospatial visualization:** displays incident locations and resources on interactive maps using latitude and longitude coordinates
- **cost analysis:** detailed breakdown of operational and damage costs
- **interactive dashboard:** real-time visualization of fire incident data and predictions
## 💭 what I learned
- **ml implementation:** building and deploying a random forest classifier for fire risk prediction
- **resource optimization:** developing algorithms for efficient firefighting resource allocation
- **data processing:** handling and analyzing complex environmental and incident data
- **google maps integration:** implementing the google maps api to create interactive location-based features
## limitations 🚨
- **data dependencies:** requires specific csv file formats for proper functionality
- **resource constraints:** fixed resource allocation limits for firefighting units
- **model scope:** predictions limited to available environmental parameters
## improvements 🌱
- **real-time updates:** implement websocket for live data updates
- **advanced ml models:** explore deep learning models for improved predictions
- **resource scheduling:** add dynamic resource allocation optimization
- **historical analysis:** include trend analysis and seasonal pattern detection
## .env file 📄
this project requires `.env` files for both the server and client, located in their respective folders. rename the
`.env.example` file in each folder to `.env`, and update it with the necessary values. ensure these files are configured
properly and not committed to version control.
## running the project 🏁
to get the project up and running on your local machine, follow these steps:
- **ensure [python](https://www.python.org/downloads/) and [node.js](https://nodejs.org/en) are installed.**
1. **clone the repository:**
```bash
git clone https://github.com/barbaraeguche/pyrocast.git
```
2. **navigate to the project directory:**
```bash
cd pyrocast
```
3. **run the backend:**
1. **navigate to server directory:**
```bash
cd server
```
2. **install and activate virtual environment:**
```bash
python3 -m venv venv
source ./venv/bin/activate
pip install -r requirements.txt
```
3. **run the flask app:**
```bash
python3 app.py
```
4. open [http://127.0.0.1:5000](http://127.0.0.1:5000) with your browser.
4. **run the frontend:**
1. **navigate to client directory:**
```bash
cd client
```
2. **install dependencies:**
```bash
pnpm install
```
3. **start the development server:**
```bash
pnpm run dev
```
4. open [http://localhost:5173/](http://localhost:5173/) with your browser.
## preview 📸
https://github.com/user-attachments/assets/46a4d3d0-05e3-4b80-a85d-f1a3e45a0eca