Update app.py

app.py

@@ -1,76 +1,78 @@
 import streamlit as st
-import pandas as pd
 import ee
 import geemap
+import datetime
 
-# Earth Engine authentication
+# 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 the CSV file
-@st.cache_data
-def load_data():
-    return pd.read_csv('Farm_Details_Export.csv')
+# Define the indices and their corresponding functions
+INDICES = {
+    'NDVI': lambda img: img.normalizedDifference(['B8', 'B4']).rename('NDVI'),
+    'EVI': lambda img: img.expression(
+        '2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1))',
+        {
+            'NIR': img.select('B8'),
+            'RED': img.select('B4'),
+            'BLUE': img.select('B2')
+        }
+    ).rename('EVI'),
+    'SAVI': lambda img: img.expression(
+        '((NIR - RED) / (NIR + RED + 0.5)) * (1.5)',
+        {
+            'NIR': img.select('B8'),
+            'RED': img.select('B4')
+        }
+    ).rename('SAVI'),
+    'NDMI': lambda img: img.normalizedDifference(['B8', 'B11']).rename('NDMI')
+}
 
-df = load_data()
+# Define color palettes for each index
+COLOR_PALETTES = {
+    'NDVI': ['FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718', '74A901', '66A000', '529400', '3E8601', '207401', '056201', '004C00', '023B01', '012E01', '011D01', '011301'],
+    'EVI': ['FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718', '74A901', '66A000', '529400', '3E8601', '207401', '056201', '004C00', '023B01', '012E01', '011D01', '011301'],
+    'SAVI': ['#D73027', '#F46D43', '#FDAE61', '#FEE08B', '#FFFFBF', '#D9EF8B', '#A6D96A', '#66BD63', '#1A9850'],
+    'NDMI': ['#D73027', '#F46D43', '#FDAE61', '#FEE08B', '#FFFFBF', '#D9EF8B', '#A6D96A', '#66BD63', '#1A9850']
+}
 
-st.title('Farm Finder and Sentinel-2 Imagery Viewer')
+# Streamlit app
+st.title('Google Earth Engine Indices Viewer')
 
-# Farm search functionality
-farm_name = st.text_input('Enter farm name:')
-if farm_name:
-    farm = df[df['farm_name'].str.contains(farm_name, case=False)]
-    if not farm.empty:
-        st.write(farm)
-    else:
-        st.write('Farm not found')
+# User inputs
+selected_indices = st.multiselect('Select two indices', list(INDICES.keys()), max_selections=2)
+start_date = st.date_input('Start date', datetime.date(2022, 1, 1))
+end_date = st.date_input('End date', datetime.date(2022, 12, 31))
 
-# Sentinel-2 index selection
-indices = {'NDVI': 'NDVI', 'EVI': 'EVI', 'NDWI': '(B3 - B8) / (B3 + B8)'}
-selected_index = st.selectbox('Select Sentinel-2 index:', list(indices.keys()))
+if len(selected_indices) == 2 and start_date < end_date:
+    # Define the area of interest
+    aoi = ee.Geometry.Point([48.7312815, 31.5200749]).buffer(5000)
 
-# Date range selection
-start_date = st.date_input('Start date')
-end_date = st.date_input('End date')
+    # Get Sentinel-2 imagery
+    s2 = ee.ImageCollection('COPERNICUS/S2_SR') \
+        .filterBounds(aoi) \
+        .filterDate(start_date, end_date) \
+        .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20)) \
+        .sort('CLOUDY_PIXEL_PERCENTAGE')
 
-if st.button('Show Map'):
-    # Create a map centered on the mean coordinates of all farms
-    center_lat = df['latitude'].mean()
-    center_lon = df['longitude'].mean()
-    m = geemap.Map(center=[center_lat, center_lon], zoom=10)
-    
-    # Add Sentinel-2 imagery with selected index
-    s2_collection = (ee.ImageCollection('COPERNICUS/S2_SR')
-                     .filterDate(start_date, end_date)
-                     .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20)))
-    
-    if selected_index == 'NDVI':
-        vis_params = {'min': 0, 'max': 1, 'palette': ['red', 'yellow', 'green']}
-    elif selected_index == 'EVI':
-        vis_params = {'min': -1, 'max': 1, 'palette': ['blue', 'white', 'green']}
-    else:  # NDWI
-        vis_params = {'min': -1, 'max': 1, 'palette': ['red', 'white', 'blue']}
-    
-    def add_index(image):
-        index = image.expression(indices[selected_index], {
-            'B3': image.select('B3'),
-            'B4': image.select('B4'),
-            'B8': image.select('B8'),
-        }).rename(selected_index)
-        return image.addBands(index)
-    
-    s2_with_index = s2_collection.map(add_index)
-    median_index = s2_with_index.select(selected_index).median()
-    m.addLayer(median_index, vis_params, selected_index)
-    
-    # Add farm locations to the map
-    for idx, row in df.iterrows():
-        info = f"Farm: {row['farm_name']}<br>Age: {row['age']}<br>Variety: {row['variety']}"
-        m.add_marker(location=[row['latitude'], row['longitude']], popup=info)
-    
-    # Display the map
+    # Calculate indices
+    for index in selected_indices:
+        s2 = s2.map(lambda img: img.addBands(INDICES[index](img)))
+
+    # Reduce the collection to a single image (median)
+    image = s2.select(selected_indices).median()
+
+    # Create a map
+    m = geemap.Map()
+    m.centerObject(aoi, 12)
+
+    # Add layers to the map
+    for index in selected_indices:
+        m.addLayer(image.select(index), {'min': 0, 'max': 1, 'palette': COLOR_PALETTES[index]}, index)
+
+    # Add the map to the Streamlit app
     m.to_streamlit(height=600)
 
-if __name__ == '__main__':
-    st.write('App is running')
+else:
+    st.warning('Please select two indices and ensure the start date is before the end date.')