SaranHiruthikM/algerian-ff-lr

Simple ,but Interactive Flask web app for predicting Fire Weather Index (FWI) for Algerian Forest Fire using Machine Learning. # FWI Prediction Web App A modern, responsive web application for predicting the Fire Weather Index (FWI) using machine learning. ## Features - **Clean UI:** Stylish, user-friendly form with a soft gradient background and subtle shadows. - **Input Fields:** Enter weather and fire indices such as Temperature, RH, Ws, Rain, FFMC, DMC, ISI, Classes, and Region. - **Instant Prediction:** Submit your data to receive an FWI prediction instantly. - **Responsive Design:** Looks great on all devices. ## Screenshots ## Usage 1. **Clone the repository:** ```bash git clone github.com cd algerian-ff-lr ``` 2. **Run the Flask server:** ```bash python app.py ``` 3. **Open your browser:** Visit `localhost` to access the app. 4. **Enter the required data:** Fill in all fields and click **Predict** to get the FWI prediction. 5. **Before all this, please run this:** ```bash pip install -r requirements.txt ``` ## Input Fields | Field | Description | |--------------|---------------------------| | Temperature | Ambient temperature (°C) | | RH | Relative Humidity (%) | | Ws | Wind Speed (km/h) | | Rain | Rainfall (mm) | | FFMC | Fine Fuel Moisture Code | | DMC | Duff Moisture Code | | ISI | Initial Spread Index | | Classes | Fire class label | | Region | Region code or name | ## Example ```text Temperature: 25 RH: 40 Ws: 15 Rain: 0 FFMC: 85 DMC: 120 ISI: 10 Classes: 1 Region: 2 ``` ## Technologies Used - **HTML5 & CSS3:** For the frontend UI. - **Flask:** Backend server for handling predictions. - **Python:** Machine learning model integration. ## License This project is licensed under the MIT License. --- **FWI Prediction** – Predicting fire risk, beautifully.