import streamlit as st
import pickle

st.title("Predict flood probability :water_polo:") 
model=pickle.load(open("flood.pkl","rb"))
MonsoonIntensity=st.number_input("MonsoonIntensity",0,16)
TopographyDrainage=st.number_input("TopographyDrainage",0,18)
RiverManagement=st.number_input("RiverManagement",0,16)
Deforestation=st.number_input("Deforestation",0,17)
Urbanization=st.number_input("Urbanization",0,17)
ClimateChange=st.number_input("ClimateChange",0,17)
DamsQuality=st.number_input("DamsQuality",0,16)
Siltation=st.number_input("Siltation",0,16)
AgriculturalPractices=st.number_input("AgriculturalPractices",0,16)
Encroachments=st.number_input("Encroachments",0,18)
IneffectiveDisasterPreparedness=st.number_input("IneffectiveDisasterPreparedness",0,16)
DrainageSystems=st.number_input("DrainageSystems",0,17)
CoastalVulnerability=st.number_input("CoastalVulnerability",0,17)
Landslides=st.number_input("Landslides",0,16)
Watersheds=st.number_input("Watersheds",0,16)
DeterioratingInfrastructure=st.number_input("DeterioratingInfrastructure",0,17)
PopulationScore =st.number_input("PopulationScore ",0,18)
WetlandLoss=st.number_input("WetlandLoss",0,19)
InadequatePlanning =st.number_input("InadequatePlanning ",0,16)
PoliticalFactors=st.number_input("PoliticalFactors",0,16)
if st.button("Tahmin et"):
    tahmin=model.predict([[MonsoonIntensity,TopographyDrainage,RiverManagement,Deforestation,Urbanization,ClimateChange,
                           DamsQuality,Siltation,AgriculturalPractices,Encroachments,IneffectiveDisasterPreparedness,DrainageSystems,
                           CoastalVulnerability,Landslides,Watersheds,DeterioratingInfrastructure,PopulationScore,WetlandLoss,
                           InadequatePlanning,PoliticalFactors
                           ]])
    # tahmin=round(tahmin[0][0],2)
    st.success(f"Flood Prediction:{tahmin}")