Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,211 +1,2 @@
 import streamlit as st
-import ee
-import geemap.foliumap as geemap
-import base64
-import json
-import tempfile
-import os
-import datetime
-import pandas as pd  # Added for data manipulation and plotting
-import altair as alt  # Added for custom chart coloring
-
-# --- Configuration ---
-st.set_page_config(layout="wide")
-st.title("🇪🇺 European Capitals Satellite Viewer")
-
-# Define a list of major European capitals and their coordinates (Lon, Lat)
-EUROPEAN_CAPITALS = {
-    "Rome, Italy": (12.4964, 41.9028),
-    "Stockholm, Sweden": (18.0656, 59.3327),
-    "Paris, France": (2.3522, 48.8566),
-    "Berlin, Germany": (13.4050, 52.5200),
-    "London, UK": (-0.1278, 51.5074),
-    "Madrid, Spain": (-3.7038, 40.4168),
-    "Vienna, Austria": (16.3738, 48.2082),
-    "Athens, Greece": (23.7275, 37.9838),
-    "Warsaw, Poland": (21.0118, 52.2297),
-    "Amsterdam, Netherlands": (4.8952, 52.3702),
-    "Oslo, Norway": (10.7522, 59.9139),
-    "Lisbon, Portugal": (-9.1393, 38.7223),
-}
-
-# --- Initialize EE (using the temporary file method) ---
-
-
-@st.cache_resource
-def initialize_ee_session():
-    """Initializes the Earth Engine session and caches the result."""
-    try:
-        # Ensure secrets are available
-        SERVICE_ACCOUNT = st.secrets["service_account"]
-        PRIVATE_KEY_B64 = st.secrets["private_key"]
-
-        # Decode the private key and write it to a temporary file for ee.Initialize
-        decoded = base64.b64decode(PRIVATE_KEY_B64).decode("utf-8")
-        with tempfile.NamedTemporaryFile(mode="w+", suffix=".json", delete=False) as f:
-            f.write(decoded)
-            temp_path = f.name
-
-        credentials = ee.ServiceAccountCredentials(SERVICE_ACCOUNT, temp_path)
-        ee.Initialize(credentials)
-        os.remove(temp_path)
-
-        return True
-    except Exception as e:
-        st.error(f"❌ Error initializing Earth Engine. Check your Streamlit secrets configuration. Error: {e}")
-        return False
-
-
-# Run the initialization only once
-if initialize_ee_session():
-    st.success(f"✅ Earth Engine initialized successfully (Cached).")
-else:
-    st.stop()
-
-
-# --- User Inputs ---
-st.sidebar.image("image/logo.png")
-
-st.sidebar.header("Controls")
-
-selected_city = st.sidebar.selectbox(
-    "1. Select a European Capital:",
-    options=list(EUROPEAN_CAPITALS.keys())
-)
-
-col1, col2 = st.sidebar.columns(2)
-with col1:
-    # Fixed: Use datetime.date for Streamlit compatibility
-    start_date = st.date_input("2. Start Date:", value=datetime.date(2023, 9, 1))
-with col2:
-    # Fixed: Use datetime.date for Streamlit compatibility
-    end_date = st.date_input("3. End Date:", value=datetime.date(2024, 3, 1))
-
-cloud_filter = st.sidebar.slider(
-    "4. Max Cloud Filter (%):",
-    min_value=1,
-    max_value=100,
-    value=15
-)
-
-
-# --- Processing Logic ---
-
-# Get selected city coordinates
-lon, lat = EUROPEAN_CAPITALS[selected_city]
-# Define a buffer around the city point (e.g., 25km radius)
-city_point = ee.Geometry.Point([lon, lat])
-aoi = city_point.buffer(25000)
-
-# 1. Collection filtered ONLY by date and bounds (used for comprehensive plotting)
-s2_unfiltered_collection = ee.ImageCollection("COPERNICUS/S2_HARMONIZED") \
-    .filterDate(start_date.isoformat(), end_date.isoformat()) \
-    .filterBounds(aoi)
-
-# 2. Collection filtered by date, bounds, AND cloud percentage (used for composite)
-# This ensures only images under the cloud_filter threshold are used for the median composite.
-s2_composite_collection = s2_unfiltered_collection \
-    .filterMetadata('CLOUDY_PIXEL_PERCENTAGE', 'less_than', cloud_filter)
-
-# Calculate the size of the *composite* collection (blocking call)
-collection_size = s2_composite_collection.size().getInfo()
-unfiltered_collection_size = s2_unfiltered_collection.size().getInfo()
-
-try:
-    if collection_size == 0:
-        st.warning(
-            f"⚠️ No Sentinel-2 images found for **{selected_city}** that meet the **{cloud_filter}%** max cloudiness filter. Try expanding the date range or increasing the cloud filter.")
-        # Create a map centered on the city even if no image is found
-        Map = geemap.Map(center=[lat, lon], zoom=11, plugin_Draw=False)
-        Map.to_streamlit(width=800, height=500)
-        st.stop()
-    else:
-        # Calculate the median composite image
-        s2_composite = s2_composite_collection.median()
-
-        # --- Data Extraction for Plotting (Using UNFILTERED Collection) ---
-        # Get list of properties for each image in the UNFILTERED collection
-        feature_list = s2_unfiltered_collection.toList(unfiltered_collection_size).getInfo()
-        data_for_df = []
-        for feature in feature_list:
-            props = feature['properties']
-            data_for_df.append({
-                'Acquisition Date': props['system:time_start'],
-                'Cloudiness (%)': props['CLOUDY_PIXEL_PERCENTAGE']
-            })
-
-        # Convert to Pandas DataFrame and format the date
-        df = pd.DataFrame(data_for_df)
-        # Convert Earth Engine Unix timestamp (milliseconds) to datetime objects
-        df['Acquisition Date'] = pd.to_datetime(df['Acquisition Date'], unit='ms')
-        df = df.set_index('Acquisition Date').sort_index()
-
-        # Add color column based on the user's filter threshold (Blue <= threshold, Red > threshold)
-        df['Color'] = df['Cloudiness (%)'].apply(
-            lambda x: 'blue' if x <= cloud_filter else 'red'
-        )
-
-        # Calculate the average cloudiness of the source images (from the UNFILTERED set for proper reporting)
-        mean_cloud_percentage = s2_unfiltered_collection.aggregate_mean('CLOUDY_PIXEL_PERCENTAGE').getInfo()
-
-        # Display analysis results
-        st.subheader(f"Data Analysis for {selected_city}")
-        st.info(f"📸 Total available images (date/bounds filtered): **{unfiltered_collection_size}**")
-        st.info(f"✅ Images used for composite (under {cloud_filter}% cloudiness): **{collection_size}**")
-        st.info(f"☁️ Average Cloudiness of all available images: **{mean_cloud_percentage:.2f}%**")
-
-        # --- PLOT CLOUDINESS OVER TIME with Conditional Colors using Altair ---
-        st.subheader("Cloudiness Over Time vs. Filter Threshold")
-        st.markdown(
-            f"Bars are colored **blue** if cloudiness is below the **{cloud_filter}%** threshold (used for composite) and **red** if above.")
-
-        # Define custom color scale to ensure blue and red are used
-        color_scale = alt.Scale(domain=['blue', 'red'], range=['blue', 'red'])
-
-        # Create the Altair chart
-        chart = alt.Chart(df.reset_index()).mark_bar().encode(
-            x=alt.X('Acquisition Date', title='Acquisition Date'),
-            y=alt.Y('Cloudiness (%)', title='Cloudiness (%)'),
-            color=alt.Color('Color', scale=color_scale),  # Use the pre-calculated color column
-            tooltip=['Acquisition Date', 'Cloudiness (%)']
-        ).properties(
-            height=300
-        ).interactive()  # Make the chart zoomable/pannable
-
-        # Add a horizontal line to represent the user's cloud filter threshold
-        rule = alt.Chart(pd.DataFrame({'y': [cloud_filter]})).mark_rule(color='green', strokeDash=[5, 5]).encode(
-            y='y'
-        )
-
-        st.altair_chart(chart + rule, use_container_width=True)
-        # --- END PLOT ---
-
-        # Visualization parameters (Natural Color RGB)
-        vis_params = {
-            "bands": ["B4", "B3", "B2"],
-            "min": 0,
-            "max": 3000,
-            "gamma": 1.4
-        }
-
-        # Create a map centered on the selected city
-        Map = geemap.Map(center=[lat, lon], zoom=10)
-
-        # Add the composite layer to the map
-        Map.addLayer(s2_composite, vis_params, f"Sentinel-2 Composite: {selected_city}")
-
-        # Add a marker for the capital city center
-        Map.add_marker([lat, lon], tooltip=selected_city)
-        #Map.add_ee_layer(aoi.bounds(), {'color': 'red'}, 'Area of Interest')
-
-        # Display the map in Streamlit
-        st.subheader("Satellite Composite Visualization")
-        Map.to_streamlit(width=900, height=600)
-
-except Exception as e:
-    st.error(f"An Earth Engine error occurred during processing: {e}")
-
-st.markdown("""
----
-*Data Source: ESA Copernicus Sentinel-2 Level 2A data via Google Earth Engine.*
-""")
+st.write(st.secrets)