Update app.py

app.py

@@ -1062,21 +1062,88 @@ import streamlit as st
 import geemap.foliumap as geemap
 import ee
 import matplotlib.pyplot as plt
-from datetime import datetime
-import tempfile
+from datetime import datetime, timedelta
 from streamlit_folium import st_folium
-import folium
 
 # =====================================================
-# Map snapshot export
+# EE Init Helper
+# =====================================================
+def initialize_ee():
+    EARTHENGINE_TOKEN = os.getenv("EARTHENGINE_TOKEN")
+    SERVICE_ACCOUNT = os.getenv("SERVICE_ACCOUNT")
+
+    if "ee_initialized" in st.session_state and st.session_state["ee_initialized"]:
+        return True
+
+    if EARTHENGINE_TOKEN and SERVICE_ACCOUNT:
+        try:
+            creds = ee.ServiceAccountCredentials(email=SERVICE_ACCOUNT, key_data=EARTHENGINE_TOKEN)
+            ee.Initialize(creds)
+            st.session_state["ee_initialized"] = True
+            return True
+        except Exception as e:
+            st.warning(f"Service account init failed: {e}, falling back...")
+
+    try:
+        ee.Initialize()
+        st.session_state["ee_initialized"] = True
+        return True
+    except Exception:
+        try:
+            ee.Authenticate()
+            ee.Initialize()
+            st.session_state["ee_initialized"] = True
+            return True
+        except Exception as e:
+            st.error(f"Earth Engine auth failed: {e}")
+            return False
+
+# =====================================================
+# Safe reducers
+# =====================================================
+def safe_get_reduce(region, image, band, scale=1000, default=None, max_pixels=int(1e7)):
+    try:
+        rr = image.reduceRegion(ee.Reducer.mean(), region, scale=scale, maxPixels=max_pixels)
+        val = rr.get(band)
+        return float(val.getInfo()) if val else default
+    except Exception:
+        return default
+
+def safe_reduce_histogram(region, image, band, scale=1000, max_pixels=int(1e7)):
+    try:
+        rr = image.reduceRegion(ee.Reducer.frequencyHistogram(), region, scale=scale, maxPixels=max_pixels)
+        hist = rr.get(band)
+        return hist.getInfo() if hist else {}
+    except Exception:
+        return {}
+
+def safe_time_series(region, collection, band, start, end, reducer=ee.Reducer.mean(), scale=1000, max_pixels=int(1e7)):
+    try:
+        def per_image(img):
+            date = img.date().format("YYYY-MM-dd")
+            val = img.reduceRegion(reducer, region, scale=scale, maxPixels=max_pixels).get(band)
+            return ee.Feature(None, {"date": date, "val": val})
+        feats = collection.filterDate(start, end).map(per_image).filter(ee.Filter.notNull(["val"])).getInfo()
+        pts = []
+        for f in feats.get("features", []):
+            p = f.get("properties", {})
+            if p.get("val") is not None:
+                pts.append((p.get("date"), float(p.get("val"))))
+        return pts
+    except Exception:
+        return []
+
+# =====================================================
+# Map snapshot (in-memory, no disk bloat)
 # =====================================================
 def export_map_snapshot(m, width=800, height=600):
-    """Export geemap Map object to PNG snapshot (returns bytes)."""
+    """Return PNG snapshot bytes of geemap Map."""
     try:
-        tmpfile = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
-        m.screenshot(filename=tmpfile.name, region=None, dimensions=(width, height))
-        with open(tmpfile.name, "rb") as f:
-            return f.read()
+        from io import BytesIO
+        buf = BytesIO()
+        m.screenshot(filename=None, region=None, dimensions=(width, height), out_file=buf)
+        buf.seek(0)
+        return buf.read()
     except Exception as e:
         st.warning(f"Map snapshot failed: {e}")
         return None
@@ -1088,85 +1155,19 @@ def locator_page():
     st.title("🌍 GeoMate Interactive Earth Explorer")
     st.markdown(
         "Draw a polygon (or rectangle) on the map using the drawing tool. "
-        "Then press **Compute Summaries** to compute soil clay, elevation, seismic, flood occurrence, landcover, and NDVI."
+        "Then press **Compute Summaries** to compute soil, elevation, seismic, flood, landcover, NDVI, and atmospheric data."
     )
 
-    # ----------------------------
-    # EE Auth
-    # ----------------------------
-    EARTHENGINE_TOKEN = os.getenv("EARTHENGINE_TOKEN")
-    SERVICE_ACCOUNT = os.getenv("SERVICE_ACCOUNT")
-
-    def initialize_ee():
-        if "ee_initialized" in st.session_state and st.session_state["ee_initialized"]:
-            return True
-        if EARTHENGINE_TOKEN and SERVICE_ACCOUNT:
-            try:
-                creds = ee.ServiceAccountCredentials(email=SERVICE_ACCOUNT, key_data=EARTHENGINE_TOKEN)
-                ee.Initialize(creds)
-                st.session_state["ee_initialized"] = True
-                return True
-            except Exception as e:
-                st.warning(f"Service account init failed: {e}, falling back...")
-        try:
-            ee.Initialize()
-            st.session_state["ee_initialized"] = True
-            return True
-        except Exception:
-            try:
-                ee.Authenticate()
-                ee.Initialize()
-                st.session_state["ee_initialized"] = True
-                return True
-            except Exception as e:
-                st.error(f"Earth Engine auth failed: {e}")
-                return False
-
+    # --- Auth
     if not initialize_ee():
         st.stop()
 
-    # ----------------------------
-    # Safe reducers
-    # ----------------------------
-    def safe_get_reduce(region, image, band, scale=1000, default=None, max_pixels=int(1e7)):
-        try:
-            rr = image.reduceRegion(ee.Reducer.mean(), region, scale=scale, maxPixels=max_pixels)
-            val = rr.get(band)
-            return float(val.getInfo()) if val else default
-        except Exception:
-            return default
-
-    def safe_reduce_histogram(region, image, band, scale=1000, max_pixels=int(1e7)):
-        try:
-            rr = image.reduceRegion(ee.Reducer.frequencyHistogram(), region, scale=scale, maxPixels=max_pixels)
-            hist = rr.get(band)
-            return hist.getInfo() if hist else {}
-        except Exception:
-            return {}
-
-    def safe_time_series(region, collection, band, start, end, reducer=ee.Reducer.mean(), scale=1000, max_pixels=int(1e7)):
-        try:
-            def per_image(img):
-                date = img.date().format("YYYY-MM-dd")
-                val = img.reduceRegion(reducer, region, scale=scale, maxPixels=max_pixels).get(band)
-                return ee.Feature(None, {"date": date, "val": val})
-            feats = collection.filterDate(start, end).map(per_image).filter(ee.Filter.notNull(["val"])).getInfo()
-            pts = []
-            for f in feats.get("features", []):
-                p = f.get("properties", {})
-                if p.get("val") is not None:
-                    pts.append((p.get("date"), float(p.get("val"))))
-            return pts
-        except Exception:
-            return []
-
-    # ----------------------------
-    # Map setup
-    # ----------------------------
+    # --- Map setup
     m = geemap.Map(center=[28.0, 72.0], zoom=5, plugin_Draw=True, draw_export=True, locate_control=True)
 
-    # Restore ROI (if available) as polygon on the map
+    # Restore ROI (if available)
     if "roi_geojson" in st.session_state:
+        import folium
         try:
             saved = st.session_state["roi_geojson"]
             folium.GeoJson(saved, name="Saved ROI",
@@ -1174,61 +1175,59 @@ def locator_page():
         except Exception as e:
             st.warning(f"Could not re-add saved ROI: {e}")
 
-    # ----------------------------
-    # Datasets (DEM, Soil, Seismic, Flood, Landcover, NDVI)
-    # ----------------------------
-    # DEM
+    # --- Datasets
     try:
         dem = ee.Image("NASA/NASADEM_HGT/001"); dem_band_name = "elevation"
     except Exception:
-        try:
-            dem = ee.Image("USGS/SRTMGL1_003"); dem_band_name = "elevation"
-        except Exception:
-            dem = None; dem_band_name = None
+        dem, dem_band_name = None, None
 
-    # Soil
-    soil_img = None; chosen_soil_band = None
+    soil_img, chosen_soil_band = None, None
     try:
         soil_img = ee.Image("OpenLandMap/SOL/SOL_CLAY-WFRACTION_USDA-3A1A1A_M/v02")
         bands = soil_img.bandNames().getInfo()
-        chosen_soil_band = st.selectbox("Select soil depth / clay band", options=bands, index=bands.index("b200") if "b200" in bands else 0)
+        chosen_soil_band = st.selectbox("Select soil clay band", options=bands, index=bands.index("b200") if "b200" in bands else 0)
     except Exception:
-        try:
-            soil_img = ee.Image("projects/soilgrids-isric/clay_mean")
-            bands = soil_img.bandNames().getInfo()
-            chosen_soil_band = st.selectbox("Select soil depth (SoilGrids)", options=bands, index=0)
-        except Exception:
-            soil_img = None; chosen_soil_band = None
+        soil_img, chosen_soil_band = None, None
 
-    # Seismic
     try:
         seismic_img = ee.Image("SEDAC/GSHAPSeismicHazard"); seismic_band = "gshap"
     except Exception:
-        seismic_img = None; seismic_band = None
+        seismic_img, seismic_band = None, None
 
-    # Flood
     try:
         water = ee.Image("JRC/GSW1_4/GlobalSurfaceWater"); water_band = "occurrence"
     except Exception:
-        water = None; water_band = None
+        water, water_band = None, None
 
-    # Landcover
     try:
         landcover = ee.Image("ESA/WorldCover/v200"); lc_band = "Map"
     except Exception:
-        landcover = None; lc_band = None
+        landcover, lc_band = None, None
 
-    # NDVI
     try:
         ndvi_col = ee.ImageCollection("MODIS/061/MOD13A2").select("NDVI")
     except Exception:
         ndvi_col = None
 
-    # ----------------------------
-    # Render map + capture draw ROI (only once with st_folium)
-    # ----------------------------
+    # Atmospheric datasets
+    try:
+        precip_col = ee.ImageCollection("UCSB-CHG/CHIRPS/DAILY").select("precipitation")
+    except Exception:
+        precip_col = None
+
+    try:
+        temp_col = ee.ImageCollection("MODIS/061/MOD11A2").select("LST_Day_1km")
+    except Exception:
+        temp_col = None
+
+    try:
+        pm25_img = ee.ImageCollection("COPERNICUS/S5P/OFFL/L3_AER_AI").select("absorbing_aerosol_index").mean()
+    except Exception:
+        pm25_img = None
+
+    # --- Render & capture ROI
     result = st_folium(m, width=800, height=600, returned_objects=["last_active_drawing"])
-    roi, coords, flat_coords = None, None, None
+    roi, flat_coords = None, None
 
     if result and "last_active_drawing" in result and result["last_active_drawing"]:
         feat = result["last_active_drawing"]
@@ -1251,9 +1250,8 @@ def locator_page():
                 st.error(f"Failed to convert geometry: {e}")
 
     if roi is None and "roi_geojson" in st.session_state:
-        saved = st.session_state["roi_geojson"]
         try:
-            geom = saved.get("geometry")
+            geom = st.session_state["roi_geojson"].get("geometry")
             if geom:
                 roi = ee.Geometry(geom)
                 coords = geom.get("coordinates", None)
@@ -1274,9 +1272,7 @@ def locator_page():
         st.markdown("### 📍 ROI Coordinates (Lat, Lon)")
         st.write(st.session_state["roi_coords"])
 
-    # ----------------------------
-    # Compute summaries
-    # ----------------------------
+    # --- Compute summaries
     if st.button("Compute Summaries"):
         if roi is None:
             st.error("⚠️ No ROI found. Please draw first.")
@@ -1291,32 +1287,47 @@ def locator_page():
             ndvi_ts = []
             if ndvi_col:
                 end = datetime.utcnow().strftime("%Y-%m-%d")
-                start = (datetime.utcnow().replace(year=datetime.utcnow().year-2)).strftime("%Y-%m-%d")
+                start = (datetime.utcnow() - timedelta(days=365*2)).strftime("%Y-%m-%d")
                 ndvi_ts = safe_time_series(roi, ndvi_col, "NDVI", start, end)
 
-            # Save results
+            precip_ts, temp_ts, pm25_val = [], [], None
+            if precip_col:
+                end = datetime.utcnow().strftime("%Y-%m-%d")
+                start = (datetime.utcnow() - timedelta(days=365)).strftime("%Y-%m-%d")
+                precip_ts = safe_time_series(roi, precip_col, "precipitation", start, end, scale=5000)
+            if temp_col:
+                end = datetime.utcnow().strftime("%Y-%m-%d")
+                start = (datetime.utcnow() - timedelta(days=365)).strftime("%Y-%m-%d")
+                temp_ts = safe_time_series(roi, temp_col, "LST_Day_1km", start, end, scale=1000)
+            if pm25_img:
+                pm25_val = safe_get_reduce(roi, pm25_img, "absorbing_aerosol_index", 10000)
+
+            # Save to site
             active = st.session_state.get("active_site", 0)
             if "sites" in st.session_state:
                 site = st.session_state["sites"][active]
-                if roi:
-                    try:
-                        site["ROI"] = roi.getInfo()
-                    except Exception:
-                        site["ROI"] = "Not available"
+                try:
+                    site["ROI"] = roi.getInfo()
+                except Exception:
+                    site["ROI"] = "Not available"
                 site["Soil Profile"] = f"{soil_val} ({chosen_soil_band})" if soil_val else "N/A"
                 site["Topo Data"] = f"{elev_val} m" if elev_val else "N/A"
                 site["Seismic Data"] = seismic_val if seismic_val else "N/A"
                 site["Flood Data"] = flood_val if flood_val else "N/A"
                 site["Environmental Data"] = {"Landcover": lc_stats, "NDVI": ndvi_ts}
+                site["Atmospheric Data"] = {"Precipitation": precip_ts, "Temperature": temp_ts, "PM2.5": pm25_val}
 
             st.session_state["soil_json"] = {
                 "Soil": soil_val, "Soil Band": chosen_soil_band,
                 "Elevation": elev_val, "Seismic": seismic_val,
                 "Flood": flood_val, "Landcover Stats": lc_stats,
-                "NDVI TS": ndvi_ts
+                "NDVI TS": ndvi_ts,
+                "Precipitation TS": precip_ts,
+                "Temperature TS": temp_ts,
+                "PM2.5": pm25_val
             }
 
-            # Map snapshot
+            # Snapshot
             map_bytes = export_map_snapshot(m)
             if map_bytes:
                 st.session_state["last_map_snapshot"] = map_bytes
@@ -1330,13 +1341,28 @@ def locator_page():
             st.write(f"**Elevation:** {elev_val}")
             st.write(f"**Seismic:** {seismic_val}")
             st.write(f"**Flood:** {flood_val}")
+            st.write(f"**PM2.5 Index:** {pm25_val}")
             st.json(lc_stats)
+
             if ndvi_ts:
                 d, v = zip(*ndvi_ts)
                 fig, ax = plt.subplots()
                 ax.plot(d, v, marker="o"); ax.set_title("NDVI"); ax.set_xlabel("Date")
                 st.pyplot(fig)
 
+            if precip_ts:
+                d, v = zip(*precip_ts)
+                fig, ax = plt.subplots()
+                ax.plot(d, v, marker="o", color="blue"); ax.set_title("Precipitation (mm)"); ax.set_xlabel("Date")
+                st.pyplot(fig)
+
+            if temp_ts:
+                d, v = zip(*temp_ts)
+                fig, ax = plt.subplots()
+                ax.plot(d, v, marker="o", color="red"); ax.set_title("LST Temperature (K)"); ax.set_xlabel("Date")
+                st.pyplot(fig)
+
+
 # GeoMate Ask (RAG) — simple chat with memory per site and auto-extract numeric values
 import re, json, pickle
 import streamlit as st