src/streamlit_app.py

# Streamlit uygulaması

import streamlit as st
import pandas as pd
import pickle
from catboost import CatBoostClassifier

st.set_page_config(
    page_title="Irrigation Need Prediction",
    page_icon="🌱",
    layout="centered"
)

st.title("🌱 Irrigation Need Prediction")
st.write(
    "This application predicts the irrigation need level of a field "
    "based on soil, crop, weather, and irrigation-related inputs."
)

model = CatBoostClassifier()
model.load_model("src/catboost_irrigation_model.cbm")

with open("src/feature_columns.pkl", "rb") as f:
    feature_columns = pickle.load(f)

soil_type = st.selectbox("Soil Type", ["Sandy", "Clay", "Loamy", "Silt"])
soil_ph = st.number_input("Soil pH", min_value=4.0, max_value=9.0, value=6.5)
soil_moisture = st.number_input("Soil Moisture", min_value=0.0, max_value=100.0, value=35.0)
organic_carbon = st.number_input("Organic Carbon", min_value=0.0, max_value=3.0, value=0.9)
electrical_conductivity = st.number_input("Electrical Conductivity", min_value=0.0, max_value=5.0, value=1.5)
temperature_c = st.number_input("Temperature (°C)", min_value=0.0, max_value=50.0, value=28.0)
humidity = st.number_input("Humidity", min_value=0.0, max_value=100.0, value=60.0)
rainfall_mm = st.number_input("Rainfall (mm)", min_value=0.0, max_value=3000.0, value=1200.0)
sunlight_hours = st.number_input("Sunlight Hours", min_value=0.0, max_value=15.0, value=7.5)
wind_speed_kmh = st.number_input("Wind Speed (km/h)", min_value=0.0, max_value=30.0, value=10.0)

crop_type = st.selectbox("Crop Type", ["Sugarcane", "Rice", "Cotton", "Maize", "Wheat", "Potato"])
crop_growth_stage = st.selectbox("Crop Growth Stage", ["Sowing", "Vegetative", "Flowering", "Harvest"])
season = st.selectbox("Season", ["Kharif", "Rabi", "Zaid"])
irrigation_type = st.selectbox("Irrigation Type", ["Canal", "Sprinkler", "Rainfed", "Drip"])
water_source = st.selectbox("Water Source", ["Reservoir", "River", "Groundwater", "Rainwater"])
field_area_hectare = st.number_input("Field Area (hectare)", min_value=0.0, max_value=20.0, value=7.5)
mulching_used = st.selectbox("Mulching Used", ["Yes", "No"])
previous_irrigation_mm = st.number_input("Previous Irrigation (mm)", min_value=0.0, max_value=150.0, value=60.0)
region = st.selectbox("Region", ["North", "South", "East", "West", "Central"])

input_df = pd.DataFrame({
    "Soil_Type": [soil_type],
    "Soil_pH": [soil_ph],
    "Soil_Moisture": [soil_moisture],
    "Organic_Carbon": [organic_carbon],
    "Electrical_Conductivity": [electrical_conductivity],
    "Temperature_C": [temperature_c],
    "Humidity": [humidity],
    "Rainfall_mm": [rainfall_mm],
    "Sunlight_Hours": [sunlight_hours],
    "Wind_Speed_kmh": [wind_speed_kmh],
    "Crop_Type": [crop_type],
    "Crop_Growth_Stage": [crop_growth_stage],
    "Season": [season],
    "Irrigation_Type": [irrigation_type],
    "Water_Source": [water_source],
    "Field_Area_hectare": [field_area_hectare],
    "Mulching_Used": [mulching_used],
    "Previous_Irrigation_mm": [previous_irrigation_mm],
    "Region": [region]
})

input_df = input_df[feature_columns]

if st.button("Predict Irrigation Need / Sulama İhtiyacını Tahmin Et"):

    prediction = model.predict(input_df)[0][0]

    turkish_labels = {
        "Low": "Düşük",
        "Medium": "Orta",
        "High": "Yüksek"
    }

    st.success(
        f"Predicted Irrigation Need: {prediction}\n\n"
        f"Sulama İhtiyacı: {turkish_labels[prediction]}"
    )