src/streamlit_app.py

import streamlit as st
import joblib
import numpy as np

with open('src/forest_fire_model', 'rb') as file:
    model = joblib.load(file)
with open('src/forest_fire_scaler', 'rb') as file:
    scaler = joblib.load(file)
with open('src/forest_fire_encoder', 'rb') as file:
    encoder = joblib.load(file)


st.title('Forest Fire Prediction :fire:')

Temperature =st.slider("Temperature : ", 22, 42, step=1)
RH = st.number_input("Relative Humidity : ", 21, 90, step=1)
Rain = st.number_input("Rain(mm) : ", 0.0, 16.80, step=1.0)
FFMC = st.number_input("Fine Fuel Moisture Code : ",28.6, 96.0, step=1.0)
DMC = st.number_input("Duff Moisture Code : ",0.7, 65.9, step=1.0)
DC = st.number_input("DC",6.9, 220.4, step=1.0)
ISI = st.number_input("ISI", 0.0, 100.0, step=1.0)
BUI = st.number_input("BUI", 0.0, 100.0, step=1.0)
FWI = st.number_input("FWI", 0.0, 100.0, step=1.0)

if st.button("Submit"):
    model_input = np.array([[Temperature, RH, Rain, FFMC, DMC, DC, ISI, BUI, FWI]])
    model_input = scaler.transform(model_input)
    output = model.predict(model_input)
    output = encoder.inverse_transform(output)
    rephrased = ["forest fire" if output[0]=='fire' else "no forest fire"]
    st.write(f"There will be {rephrased[0]} in the next 24 hours")