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import streamlit as st
import pandas as pd
import numpy as np
import pickle
import json
# Load All Files
with open('prepmod_dt.pkl', 'rb') as file_1:
prepmod_dt = pickle.load(file_1)
with open('Drop_Columns.txt', 'r') as file_2:
Drop_Columns = json.load(file_2)
def run():
with st.form(key='form_forest_fire'):
day = st.slider('Enter Date',min_value=1,max_value=31,value=26)
month = st.slider('Enter Month',min_value=1, max_value=12,value=7)
year = st.number_input('Enter Year',min_value=2012,max_value=2012,value=2012)
st.markdown('---')
Temperature = st.number_input('Enter Temperature in degree Celsius',min_value=22,max_value=42,value=36)
RH = st.number_input('Enter RH (Relative Humidity) in %',min_value=21,max_value=90,value=53)
Ws = st.number_input('Enter Wind speed in km/h',min_value=6,max_value=29,value=19)
Rain = st.number_input('Enter Rainfall in mm',step=0.01,format="%.2f",min_value=0.00,max_value=16.80,value=0.00)
st.markdown('---')
FFMC = st.number_input('Fine Fuel Moisture Code (FFMC)',step=0.1,format="%.2f",min_value=28.60,max_value=92.50,value=89.20)
DMC = st.number_input('Duff Moisture Code (DMC)',step=0.1,format="%.2f",min_value=1.10,max_value=65.90,value=17.10)
DC = st.number_input('Drought Code (DC)',step=0.1,format="%.2f",min_value=7.00,max_value=220.40,value=98.60)
ISI = st.number_input('Initial Spread Index (ISI)',step=0.1,format="%.2f",min_value=0.00,max_value=18.50,value=10.00)
BUI = st.number_input('Buildup Index (BUI)',step=0.1,format="%.2f",min_value=1.10,max_value=68.00,value=23.90)
FWI = st.number_input('Fire Weather Index (FWI)',step=0.1,format="%.2f",min_value=0.00,max_value=31.10,value=15.30)
submitted = st.form_submit_button('Is there a forest fire?')
df_inf = {
'day': day,
'month': month,
'year': year,
'Temperature': Temperature,
'RH': RH,
'Ws': Ws,
'Rain': Rain,
'FFMC': FFMC,
'DMC': DMC,
'DC': DC,
'ISI': ISI,
'BUI':BUI,
'FWI':FWI
}
df_inf = pd.DataFrame([df_inf])
# Data Inference
df_inf_copy = df_inf.copy()
# Removing unnecessary features
df_inf_final = df_inf_copy.drop(Drop_Columns,axis=1).sort_index()
st.dataframe(df_inf_final)
if submitted:
# Predict using DecisionTree
y_pred_inf = prepmod_dt.predict(df_inf_final)
st.write('# Is there a forest fire?')
if y_pred_inf == 0:
st.subheader('There is No Forest Fire')
else:
st.subheader('There is a Forest Fire')
if __name__ == '__main__':
run() |