Create app.py

app.py

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+import streamlit as st
+import ee
+import geemap
+import pandas as pd
+from datetime import datetime, timedelta
+import matplotlib.pyplot as plt
+import folium
+import os
+
+# Constants
+NDRE_HARVEST_THRESHOLD = 0.3
+NDMI_HARVEST_THRESHOLD = 0.4
+CSV_URL = "https://huggingface.co/datasets/your-username/sugarcane-farms/raw/main/farms.csv"
+PRIVATE_KEY_PATH = os.environ.get("GEE_PRIVATE_KEY_PATH")
+
+# Page configuration
+st.set_page_config(page_title="Sugarcane Field Monitoring", layout="wide")
+
+# GEE Authentication
+def authenticate_gee():
+    try:
+        credentials = ee.ServiceAccountCredentials(None, PRIVATE_KEY_PATH)
+        ee.Initialize(credentials)
+        return True
+    except Exception as e:
+        st.error(f"Failed to authenticate with Google Earth Engine: {str(e)}")
+        return False
+
+# Load farm data
+@st.cache_data
+def load_farm_data():
+    try:
+        df = pd.read_csv(CSV_URL)
+        if not all(col in df.columns for col in ["farm_name", "latitude", "longitude"]):
+            raise ValueError("CSV file is missing required columns")
+        return df
+    except Exception as e:
+        st.error(f"Error loading farm data: {str(e)}")
+        return None
+
+# Get indices data from GEE
+def get_indices_data(farm_coords, start_date, end_date):
+    point = ee.Geometry.Point(farm_coords)
+    s2 = ee.ImageCollection("COPERNICUS/S2_SR")
+    
+    filtered = s2.filterBounds(point).filterDate(start_date, end_date)
+    
+    def add_indices(image):
+        ndre = image.normalizedDifference(['B8', 'B5']).rename('NDRE')
+        ndmi = image.normalizedDifference(['B8A', 'B11']).rename('NDMI')
+        return image.addBands([ndre, ndmi])
+    
+    with_indices = filtered.map(add_indices)
+    
+    time_series = with_indices.select(['NDRE', 'NDMI']).getRegion(point, 500)
+    
+    return time_series
+
+# Extract index data from GEE results
+def extract_index_data(data, index_name):
+    try:
+        dates = [datetime.utcfromtimestamp(d[3] / 1000) for d in data[1:]]
+        values = [float(d[4]) if d[4] is not None else None for d in data[1:]]
+        return pd.DataFrame({
+            'date': dates,
+            index_name: values
+        }).dropna().set_index('date')
+    except Exception as e:
+        st.error(f"Error extracting {index_name} data: {str(e)}")
+        return pd.DataFrame()
+
+# Main application
+def main():
+    if not authenticate_gee():
+        return
+
+    st.title("Sugarcane Field Monitoring")
+
+    farm_data = load_farm_data()
+    if farm_data is None:
+        return
+
+    # Sidebar
+    st.sidebar.header("Settings")
+    farm_name = st.sidebar.selectbox("Select Farm", farm_data['farm_name'].tolist())
+    start_date = st.sidebar.date_input("Start Date", datetime.now() - timedelta(days=30))
+    end_date = st.sidebar.date_input("End Date", datetime.now())
+    
+    if end_date > datetime.now().date():
+        st.sidebar.error("End date cannot be in the future.")
+        return
+    
+    indices = st.sidebar.multiselect("Select Indices", ["NDRE", "NDMI"], default=["NDRE", "NDMI"])
+    
+    if st.sidebar.button("Submit"):
+        farm_row = farm_data[farm_data['farm_name'] == farm_name].iloc[0]
+        farm_coords = [farm_row['longitude'], farm_row['latitude']]
+        
+        with st.spinner("Fetching data..."):
+            gee_data = get_indices_data(farm_coords, start_date, end_date)
+            
+            if gee_data:
+                ndre_data = extract_index_data(gee_data.getInfo(), "NDRE")
+                ndmi_data = extract_index_data(gee_data.getInfo(), "NDMI")
+                
+                # Plotting
+                fig, ax = plt.subplots(figsize=(12, 6))
+                
+                if "NDRE" in indices and not ndre_data.empty:
+                    ax.plot(ndre_data.index, ndre_data["NDRE"], label="NDRE")
+                    ax.axhline(y=NDRE_HARVEST_THRESHOLD, color='r', linestyle='--', label="NDRE Harvest Threshold")
+                
+                if "NDMI" in indices and not ndmi_data.empty:
+                    ax.plot(ndmi_data.index, ndmi_data["NDMI"], label="NDMI")
+                    ax.axhline(y=NDMI_HARVEST_THRESHOLD, color='g', linestyle='--', label="NDMI Harvest Threshold")
+                
+                ax.set_xlabel("Date")
+                ax.set_ylabel("Index Value")
+                ax.set_title(f"Time Series for {farm_name}")
+                ax.legend()
+                
+                st.pyplot(fig)
+                
+                # Harvest Analysis
+                if "NDRE" in indices and not ndre_data.empty:
+                    if ndre_data["NDRE"].iloc[-1] > NDRE_HARVEST_THRESHOLD:
+                        st.success("NDRE indicates the field may be ready for harvest.")
+                    else:
+                        st.info("NDRE suggests the field is not yet ready for harvest.")
+                
+                if "NDMI" in indices and not ndmi_data.empty:
+                    if ndmi_data["NDMI"].iloc[-1] > NDMI_HARVEST_THRESHOLD:
+                        st.success("NDMI indicates good moisture content for harvest.")
+                    else:
+                        st.info("NDMI suggests suboptimal moisture content for harvest.")
+                
+                # Map
+                m = geemap.Map(center=farm_coords, zoom=12)
+                m.add_basemap("OpenStreetMap")
+                
+                if "NDRE" in indices:
+                    ndre_layer = geemap.ee_tile_layer(
+                        with_indices.select('NDRE').mean(),
+                        {'min': 0, 'max': 1, 'palette': ['red', 'yellow', 'green']},
+                        'NDRE'
+                    )
+                    m.add_layer(ndre_layer)
+                
+                if "NDMI" in indices:
+                    ndmi_layer = geemap.ee_tile_layer(
+                        with_indices.select('NDMI').mean(),
+                        {'min': -1, 'max': 1, 'palette': ['red', 'white', 'blue']},
+                        'NDMI'
+                    )
+                    m.add_layer(ndmi_layer)
+                
+                marker = folium.Marker(
+                    location=farm_coords[::-1],
+                    popup=f"Farm: {farm_name}<br>Lat: {farm_coords[1]}<br>Lon: {farm_coords[0]}"
+                )
+                marker.add_to(m)
+                
+                m.to_streamlit(height=600)
+                
+                # Map Download
+                map_html = m.to_html()
+                st.download_button(
+                    label="Download Map",
+                    data=map_html,
+                    file_name="sugarcane_field_map.html",
+                    mime="text/html"
+                )
+            else:
+                st.error("Failed to retrieve data from Google Earth Engine.")
+
+if __name__ == "__main__":
+    main()