app.py

import streamlit as st  
import pandas as pd  
import joblib  
import ee  
import geemap  

# Authenticate Earth Engine
service_account = 'earth-engine-service-account@ee-esmaeilkiani1387.iam.gserviceaccount.com'
credentials = ee.ServiceAccountCredentials(service_account, 'ee-esmaeilkiani1387-1b2c5e812a1d.json')
ee.Initialize(credentials) 

# Load model and farm data
model = joblib.load('updated_model.pkl')  
farm_data = pd.read_csv('Farm_NDRE_TimeSeries.csv')  
farm_names = farm_data['Farm'].tolist()  

# Function to calculate NDRE
def calculate_ndre(coordinates, start_date, end_date):  
    try:  
        # Convert start_date and end_date to strings
        start_date_str = start_date.strftime('%Y-%m-%d')
        end_date_str = end_date.strftime('%Y-%m-%d')

        roi = ee.Geometry.Point(coordinates)  
        imageCollection = ee.ImageCollection('COPERNICUS/S2_SR') \
            .filterBounds(roi) \
            .filterDate(start_date_str, end_date_str) \
            .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20))  
            
        def ndre(image):  
            red_edge = image.select('B8A')  
            red = image.select('B4')  
            return image.addBands(red_edge.subtract(red).divide(red_edge.add(red)).rename('NDRE'))  

        ndre_image = imageCollection.map(ndre).median().select('NDRE')  

        ndre_value = ndre_image.reduceRegion(  
            reducer=ee.Reducer.first(),  
            geometry=roi,  
            scale=10  
        ).getInfo()  

        return ndre_value.get('NDRE') if ndre_value else None  

    except Exception as e:  
        st.error(f"Error calculating NDRE: {e}")  
        return None  

# Streamlit User Interface
st.title("Farm Parameter Prediction App")  
selected_farm = st.selectbox("Select Farm", farm_names)  
farm_age = st.number_input("Farm Age (years)", min_value=0)  
farm_variety = st.text_input("Farm Variety")  
start_date = st.date_input("Start Date")  
end_date = st.date_input("End Date")  

# Handling Farm Data Selection and NDRE Calculation
selected_farm_data = farm_data[farm_data['Farm'] == selected_farm]  
coordinates = (selected_farm_data['longitude'].iloc[0], selected_farm_data['latitude'].iloc[0])  

if st.button('نمایش نقشه NDRE'):
    NDRE = calculate_ndre(coordinates, start_date, end_date)
    if NDRE is not None:
        st.session_state.ndre_value = NDRE  # Store NDRE in session state
        st.write(f'شاخص NDRE: {NDRE}')  
        
        Map = geemap.Map()  
        Map.centerObject(ee.Geometry.Point(coordinates), 12)  

        vis_params = {'min': 0, 'max': 1, 'palette': ['blue', 'green', 'yellow', 'red']}  
        Map.addLayer(ee.Image(NDRE), vis_params, 'NDRE')  
        Map.to_streamlit(height=500)  
    else:
        st.error("Unable to calculate NDRE.")

# Making Predictions Using the Loaded Model
if st.button("Predict"):  
    ndre_value = st.session_state.get('ndre_value', 0)  
    
    user_input = pd.DataFrame({  
        'Age': [farm_age],  
        'Variety': [farm_variety],  
        'NDRE': [ndre_value]  
    })
    
    if start_date:
        day_of_year = start_date.timetuple().tm_yday
        month = start_date.month
        user_input['DayOfYear'] = [day_of_year]
        user_input['Month'] = [month]

    user_input = user_input[['Age', 'DayOfYear', 'Month', 'Variety', 'NDRE']]

    prediction = model.predict(user_input)  

    st.write("Predictions:")  
    st.write(f"Brix: {prediction[0][0]}")  
    st.write(f"Pol: {prediction[0][1]}")  
    st.write(f"Purity: {prediction[0][2]}")  
    st.write(f"RS: {prediction[0][3]}")