app.py

import streamlit as st
import json
import ee
import geemap
import os

st.set_page_config(layout="wide")
m = st.markdown(
    """
<style>
div.stButton > button:first-child {
    background-color: #006400;
    color:#ffffff;
}
</style>""",
    unsafe_allow_html=True,
)

# Authenticate and initialize Earth Engine
earthengine_credentials = os.environ.get("EE_Authentication")

# Initialize Earth Engine with the secret credentials
os.makedirs(os.path.expanduser("~/.config/earthengine/"), exist_ok=True)
with open(os.path.expanduser("~/.config/earthengine/credentials"), "w") as f:
    f.write(earthengine_credentials)

ee.Initialize(project='ee-yashsacisro24')

# Load JSON data for Sentinel dataset options
with open("sentinel_datasets.json") as f:
    data = json.load(f)

# Define index calculation functions
def calculate_ndvi(image):
    """Calculate NDVI."""
    return image.normalizedDifference(['B5', 'B4']).rename('NDVI')

def calculate_ndwi(image):
    """Calculate NDWI."""
    return image.normalizedDifference(['B3', 'B5']).rename('NDWI')

def calculate_avg_no2(image):
    """Calculate average NO2."""
    return image.select('NO2').reduceRegion(
        reducer=ee.Reducer.mean(),
        geometry=image.geometry(),
        scale=1000
    ).get('NO2')

def calculate_custom_formula(image, formula):
    """Apply a custom formula to the image."""
    return image.expression(formula).rename('Custom Index')

# Streamlit UI - Title
st.title("Sentinel Dataset Selector and Earth Engine Index Calculator")

# Sentinel Dataset Selector Section
st.header("Sentinel Dataset Selector")

# Step 1: Select main dataset category
main_selection = st.selectbox("Select Sentinel Dataset Category", list(data.keys()))

# Step 2: Display sub-options based on main selection
sub_selection = None
if main_selection:
    sub_options = data[main_selection]["sub_options"]
    sub_selection = st.selectbox("Select Specific Dataset ID", list(sub_options.keys()))

#     # Step 3: Display band selection based on sub-selection with max 3 selections
#     selected_bands = []
#     if sub_selection:
#         bands = sub_options[sub_selection]
#         st.write("Select up to 3 bands:")
        
#         # Display bands in a side-by-side layout with three columns
#         cols = st.columns(3)
#         for i, band in enumerate(bands):
#             with cols[i % 3]:
#                 if st.checkbox(band, key=band):
#                     selected_bands.append(band)
#                 # Limit selection to a maximum of 3 bands
#                 if len(selected_bands) == 3:
#                     break

#         # Show selected bands
#         st.write("Selected Bands:", selected_bands)

# Earth Engine Index Calculator Section
st.header("Earth Engine Index Calculator")

# Step 4: Select Index Calculation Option
index_choice = st.selectbox("Select an Index or Enter Custom Formula", ['NDVI', 'NDWI', 'Average NO₂', 'Custom Formula'])

# Step 5: Custom Formula Input
custom_formula = ""
if index_choice == 'Custom Formula':
    custom_formula = st.text_input("Enter Custom Formula (e.g., 'B5 - B4 / B5 + B4')")

# Step 6: Calculate and Display Results based on selected index or formula
if sub_selection and st.button("Calculate Index"):
    # Use the selected dataset from the main and sub-selection as an ImageCollection
    collection = ee.ImageCollection(sub_selection).filterDate('2020-01-01', '2020-1-31')
    image = collection.first()  # Using the first image in the collection as an example

    result_image = None
    if index_choice == 'NDVI':
        result_image = calculate_ndvi(image)
    elif index_choice == 'NDWI':
        result_image = calculate_ndwi(image)
    elif index_choice == 'Average NO₂':
        result = calculate_avg_no2(image)
        st.write("Average NO₂ Concentration:", result.getInfo())
    elif index_choice == 'Custom Formula' and custom_formula:
        result_image = calculate_custom_formula(image, custom_formula)

    # If an index image is generated, add it to the map
    if result_image:
        Map = geemap.Map()
        Map.centerObject(image, 10)
        Map.addLayer(result_image, {}, index_choice)
        Map.to_streamlit(height=600)