app.py

import streamlit as st
import pandas as pd
import plotly.express as px
import folium
from streamlit_folium import folium_static
import ee
import datetime
import os

# Set up Google Earth Engine
service_account = 'dehkhodamap-e9f0da4ce9f6514021@ee-esmaeilkiani13877.iam.gserviceaccount.com'
credentials = ee.ServiceAccountCredentials(service_account, 'ee-esmaeilkiani13877-cfdea6eaf411.json')
ee.Initialize(credentials)

# Load farm data
@st.cache_data
def load_farm_data():
    return pd.read_csv('farm_coordinates.csv')

farms = load_farm_data()

# Streamlit app
st.title('Sugarcane Farm Dashboard')

# Sidebar
st.sidebar.title('Navigation')
page = st.sidebar.radio('Go to', ['Map', 'Comparative Charts', 'Weekly Reports'])

if page == 'Map':
    st.header('Sugarcane Farm Map')
    
    # Create map
    m = folium.Map(location=[farms['latitude'].mean(), farms['longitude'].mean()], zoom_start=10)
    
    # Add markers for each farm
    for idx, row in farms.iterrows():
        folium.Marker(
            location=[row['latitude'], row['longitude']],
            popup=f"Farm: {row['name']}, Age: {row['age']}, Variety: {row['variety']}",
            icon=folium.Icon(color='green', icon='leaf')
        ).add_to(m)
    
    # Display map
    folium_static(m)
    
    # Farm search
    search_farm = st.text_input('Search for a farm:')
    if search_farm:
        farm = farms[farms['name'].str.contains(search_farm, case=False)]
        if not farm.empty:
            st.write(f"Farm found: {farm['name'].values[0]}")
            st.map(farm)
        else:
            st.write("Farm not found.")

elif page == 'Comparative Charts':
    st.header('Comparative Charts')
    
    # Function to get NDVI data
    def get_ndvi_data(geometry, start_date, end_date):
        collection = ee.ImageCollection('COPERNICUS/S2_SR') \
            .filterBounds(geometry) \
            .filterDate(start_date, end_date) \
            .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20))
        
        def addNDVI(image):
            ndvi = image.normalizedDifference(['B8', 'B4']).rename('NDVI')
            return image.addBands(ndvi)
        
        ndvi_collection = collection.map(addNDVI)
        ndvi_values = ndvi_collection.select('NDVI').mean().reduceRegion(
            reducer=ee.Reducer.mean(),
            geometry=geometry,
            scale=10
        ).get('NDVI')
        
        return ndvi_values.getInfo()
    
    # Function to get LAI data
    def get_lai_data(geometry, start_date, end_date):
        collection = ee.ImageCollection('MODIS/006/MOD15A2H') \
            .filterBounds(geometry) \
            .filterDate(start_date, end_date)
        
        lai_values = collection.select('Lai_500m').mean().reduceRegion(
            reducer=ee.Reducer.mean(),
            geometry=geometry,
            scale=500
        ).get('Lai_500m')
        
        return lai_values.getInfo()
    
    # Date range selection
    start_date = st.date_input('Start date', datetime.date(2023, 1, 1))
    end_date = st.date_input('End date', datetime.date.today())
    
    if start_date < end_date:
        # Get NDVI and LAI data for each farm
        ndvi_data = []
        lai_data = []
        for idx, row in farms.iterrows():
            geometry = ee.Geometry.Point([row['longitude'], row['latitude']])
            ndvi = get_ndvi_data(geometry, start_date.isoformat(), end_date.isoformat())
            lai = get_lai_data(geometry, start_date.isoformat(), end_date.isoformat())
            ndvi_data.append({'Farm': row['name'], 'NDVI': ndvi})
            lai_data.append({'Farm': row['name'], 'LAI': lai})
        
        ndvi_df = pd.DataFrame(ndvi_data)
        lai_df = pd.DataFrame(lai_data)
        
        # NDVI chart
        fig_ndvi = px.bar(ndvi_df, x='Farm', y='NDVI', title='NDVI Comparison')
        st.plotly_chart(fig_ndvi)
        
        # LAI chart
        fig_lai = px.bar(lai_df, x='Farm', y='LAI', title='LAI Comparison')
        st.plotly_chart(fig_lai)
    else:
        st.error('Error: End date must fall after start date.')

elif page == 'Weekly Reports':
    st.header('Weekly Reports')
    
    # Farm selection
    selected_farm = st.selectbox('Select a farm:', farms['name'])
    
    # Week selection
    week = st.slider('Select week:', 1, 52, 1)
    
    # Simulate weekly data (replace this with actual data processing)
    def get_weekly_data(farm, week):
        return {
            'NDVI': 0.5 + 0.1 * week,  # Simulated increasing NDVI
            'LAI': 2 + 0.2 * week,  # Simulated increasing LAI
        }
    
    data = get_weekly_data(selected_farm, week)
    
    # Display weekly report
    st.subheader(f'Weekly Report for {selected_farm} - Week {week}')
    
    col1, col2 = st.columns(2)
    with col1:
        st.metric(label="NDVI 🌱", value=f"{data['NDVI']:.2f}")
    with col2:
        st.metric(label="LAI 🌡", value=f"{data['LAI']:.2f}")
    
    # Trend chart
    weeks = list(range(1, week + 1))
    trend_data = [get_weekly_data(selected_farm, w) for w in weeks]
    trend_df = pd.DataFrame(trend_data)
    trend_df['Week'] = weeks
    
    fig = px.line(trend_df, x='Week', y=['NDVI', 'LAI'], title=f'Trend for {selected_farm}')
    st.plotly_chart(fig)

# Footer
st.sidebar.markdown('---')
st.sidebar.text('Dashboard created with Streamlit')