app.py

import streamlit as st
import pandas as pd
import geopandas as gpd
import ee
import geemap
import folium
from streamlit_folium import folium_static
import matplotlib.pyplot as plt
import numpy as np
from datetime import datetime, timedelta

# Initialize Earth Engine
service_account = 'esmaeil-kiani1387-gmail-com@ee-esmaeilkiani13877.iam.gserviceaccount.com'
credentials = ee.ServiceAccountCredentials(service_account, 'ee-esmaeilkiani13877-9a054809a4bb.json')
ee.Initialize(credentials)

# Load farm data
@st.cache_data
def load_data():
    df = pd.read_csv('tableConvert.com_wftamx (1).csv')
    return df

# Calculate vegetation indices
def calculate_indices(image):
    ndvi = image.normalizedDifference(['B8', 'B4']).rename('NDVI')
    evi2 = image.expression(
        '2.5 * ((NIR - RED) / (NIR + 2.4 * RED + 1))',
        {'NIR': image.select('B8'), 'RED': image.select('B4')}
    ).rename('EVI2')
    lai = image.expression(
        '3.618 * EVI - 0.118',
        {'EVI': image.select('EVI2')}
    ).rename('LAI')
    return image.addBands([ndvi, evi2, lai])

# Get Sentinel-2 imagery
def get_sentinel_imagery(geometry, start_date, end_date):
    return (ee.ImageCollection('COPERNICUS/S2_SR')
            .filterBounds(geometry)
            .filterDate(start_date, end_date)
            .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20))
            .map(calculate_indices)
            .median())

# Main app
def main():
    st.title('Sugarcane Farm Analysis')

    # Load data
    df = load_data()

    # Sidebar
    st.sidebar.title('Farm Selection')
    selected_farm = st.sidebar.selectbox('Select a farm', df['name'])

    # Main content
    col1, col2 = st.columns([1, 2])

    with col1:
        st.subheader('Farm Information')
        farm_data = df[df['name'] == selected_farm].iloc[0]
        st.write(f"Age: {farm_data['age']}")
        st.write(f"Variety: {farm_data['variety']}")

    with col2:
        st.subheader('Farm Location')
        m = folium.Map(location=[farm_data['latitude'], farm_data['longitude']], zoom_start=12)
        folium.Marker(
            [farm_data['latitude'], farm_data['longitude']],
            popup=farm_data['name']
        ).add_to(m)
        folium_static(m)

    # Vegetation indices analysis
    st.subheader('Vegetation Indices Analysis')
    
    # Date range selection
    today = datetime.now()
    start_date = st.date_input('Start date', today - timedelta(days=30))
    end_date = st.date_input('End date', today)

    if start_date and end_date:
        geometry = ee.Geometry.Point([farm_data['longitude'], farm_data['latitude']])
        image = get_sentinel_imagery(geometry, start_date.strftime('%Y-%m-%d'), end_date.strftime('%Y-%m-%d'))

        # Display NDVI map
        ndvi_map = geemap.Map(center=[farm_data['latitude'], farm_data['longitude']], zoom=14)
        ndvi_map.add_layer(image.select('NDVI'), {'min': 0, 'max': 1, 'palette': ['red', 'yellow', 'green']}, 'NDVI')
        ndvi_map.add_child(folium.LayerControl())
        folium_static(ndvi_map)

        # Plot time series of indices
        indices = ['NDVI', 'EVI2', 'LAI']
        fig, ax = plt.subplots(figsize=(10, 6))
        
        for index in indices:
            values = image.select(index).reduceRegion(
                reducer=ee.Reducer.mean(),
                geometry=geometry,
                scale=10
            ).getInfo()[index]
            ax.plot([start_date, end_date], [values, values], label=index)

        ax.set_xlabel('Date')
        ax.set_ylabel('Index Value')
        ax.legend()
        st.pyplot(fig)

if __name__ == '__main__':
    main()