diff --git a/.Jules/palette.md b/.Jules/palette.md
new file mode 100644
index 0000000..44dc5a3
--- /dev/null
+++ b/.Jules/palette.md
@@ -0,0 +1,5 @@
+# Palette's Journal
+
+## 2026-01-30 - Hidden Labels for Accessibility
+**Learning:** Streamlit's `st.text_input` with an empty string label creates an accessibility issue where screen readers cannot identify the input's purpose. Using `label="Description"` with `label_visibility="collapsed"` provides the semantic information without affecting the visual design.
+**Action:** Check for empty string labels in Streamlit components and replace them with descriptive labels + `label_visibility="collapsed"`.
diff --git a/app.py b/app.py
index 0f60354..e9f4aa0 100644
--- a/app.py
+++ b/app.py
@@ -283,7 +283,7 @@
 """
 st.markdown(css, unsafe_allow_html=True)
 
-# Add topography decoration 
+# Add topography decoration
 st.markdown("""
 <div style="position: absolute; top: 0; right: 0; width: 45%; min-width: 400px; height: 300px; z-index: 1; overflow: visible; pointer-events: none;">
     <img src="data:image/png;base64,{topo_base64}" style="width: 100%; height: 100%; object-fit: cover; object-position: top right;">
@@ -487,10 +487,18 @@
 if st.session_state.show_homepage:
     # Show satellite homepage
     map_data = satellite_homepage.create_satellite_homepage()
-    
-    # Prominent Launch CTA button styled over the blurred preview
+
+    # Prominent Launch CTA button positioned in the CENTER of the blurred preview
     st.markdown("""
     <style>
+    .launch-button-container {
+        display: flex;
+        justify-content: center;
+        margin-top: -280px;
+        margin-bottom: 240px;
+        position: relative;
+        z-index: 100;
+    }
     div[data-testid="stButton"] > button[kind="primary"] {
         background: linear-gradient(135deg, #1E90FF, #9370DB) !important;
         color: white !important;
@@ -515,7 +523,7 @@
         if st.button("Launch Climate Copilot", type="primary", use_container_width=True):
             st.session_state.show_homepage = False
             st.rerun()
-    
+
     # Stop execution here for homepage
     st.stop()
 
@@ -536,7 +544,7 @@
 <div style="display: flex; justify-content: center; align-items: center; margin-top: 20px; margin-bottom: 30px;">
 """, unsafe_allow_html=True)
 
-# Center the title area with a single centered container 
+# Center the title area with a single centered container
 st.markdown('<div style="padding: 10px 0;"></div>', unsafe_allow_html=True)  # Reduced padding at top
 
 # Using direct HTML with base64 encoding to ensure image display
@@ -587,21 +595,21 @@
     # Use session state to remember dark/light mode preference
     if 'globe_dark_mode' not in st.session_state:
         st.session_state.globe_dark_mode = True
-        
+
     # Determine which visualization to show based on map_style preference
     if 'map_style' not in st.session_state:
         st.session_state.map_style = "earth_nullschool"  # Set the earth nullschool style as default
-    
+
     # Show map type selection toggle
     map_styles = ["Earth Nullschool", "Classic Globe"]
     cols = st.columns([7, 3])
     with cols[1]:
-        map_style_idx = st.radio("Map Style", options=range(len(map_styles)), 
+        map_style_idx = st.radio("Map Style", options=range(len(map_styles)),
                                  format_func=lambda idx: map_styles[idx],
                                  horizontal=True, label_visibility="collapsed")
         selected_map_style = map_styles[map_style_idx].lower().replace(" ", "_")
         st.session_state.map_style = selected_map_style
-    
+
     # Display the appropriate visualization based on selection
     if st.session_state.map_style == "earth_nullschool":
         # Display the official Earth Nullschool map in an iframe
@@ -609,7 +617,7 @@
     else:
         # Display the classic globe map
         globe_map.display_globe_map(dark_mode=st.session_state.globe_dark_mode)
-        
+
 except Exception as e:
     st.error(f"Unable to load interactive globe map: {str(e)}")
     # Fallback padding if map fails to load
@@ -638,7 +646,7 @@
             c0.4,0.6,1,0.9,1.7,0.9h9.5c0.6,0,1.2-0.3,1.6-0.8l8.8-10.2c1.6-1.9,3.8-3.3,6.2-4l5.8-1.6c0.7-0.2,1.2-0.8,1.2-1.6v-0.7
             C62.2,33,61.7,32.3,61,32.1z M25.5,32c0-1.9,1.6-3.5,3.5-3.5s3.5,1.6,3.5,3.5s-1.6,3.5-3.5,3.5S25.5,33.9,25.5,32z"/>
     </svg>""",
-    
+
     "agriculture": """<svg viewBox="0 0 64 64" xmlns="http://www.w3.org/2000/svg">
         <linearGradient id="grad2" x1="0%" y1="0%" x2="100%" y2="100%">
             <stop offset="0%" style="stop-color:#1E90FF;stop-opacity:1" />
@@ -649,7 +657,7 @@
         <path fill="url(#grad2)" d="M29.4,46l2.3-4.9c0.4-0.8,1.3-1.1,2.1-0.8l5.8,2.1c0.8,0.3,1.2,1.2,0.9,2L37,53.3l-7.6-7.3z"/>
         <path fill="url(#grad2)" d="M26,50c0,0-2,6-2,9c0,2.2,1.8,4,4,4s4-1.8,4-4c0-3-2-9-2-9H26z"/>
     </svg>""",
-    
+
     "energy": """<svg viewBox="0 0 64 64" xmlns="http://www.w3.org/2000/svg">
         <linearGradient id="grad3" x1="0%" y1="0%" x2="100%" y2="100%">
             <stop offset="0%" style="stop-color:#1E90FF;stop-opacity:1" />
@@ -658,7 +666,7 @@
         <path fill="url(#grad3)" d="M36,3c0,0-17,24-17,35c0,3.3,2.7,6,6,6s6-2.7,6-6c0-11,17-35,17-35H36z"/>
         <path fill="url(#grad3)" d="M19,39L9,59h16l-6,9h2l19-22H29l6-13L19,39z"/>
     </svg>""",
-    
+
     "insurance": """<svg viewBox="0 0 64 64" xmlns="http://www.w3.org/2000/svg">
         <linearGradient id="grad4" x1="0%" y1="0%" x2="100%" y2="100%">
             <stop offset="0%" style="stop-color:#1E90FF;stop-opacity:1" />
@@ -669,7 +677,7 @@
         <path fill="url(#grad4)" d="M32,24c-5,0-9,4-9,9s4,9,9,9s9-4,9-9S37,24,32,24z M32,36c-1.7,0-3-1.3-3-3s1.3-3,3-3s3,1.3,3,3
         S33.7,36,32,36z"/>
     </svg>""",
-    
+
     "forestry": """<svg viewBox="0 0 64 64" xmlns="http://www.w3.org/2000/svg">
         <linearGradient id="grad5" x1="0%" y1="0%" x2="100%" y2="100%">
             <stop offset="0%" style="stop-color:#1E90FF;stop-opacity:1" />
@@ -679,7 +687,7 @@
         <path fill="url(#grad5)" d="M44,43c0-2.8-2.5-5-5.7-5H34v23h4c1.1,0,2-0.9,2-2V43z"/>
         <path fill="url(#grad5)" d="M32,38c2,0,14-19.1,14-24.5C46,8.4,40.6,3,34,3h-4c-6.6,0-12,5.4-12,10.5C18,18.9,30,38,32,38z"/>
     </svg>""",
-    
+
     "catastrophes": """<svg viewBox="0 0 64 64" xmlns="http://www.w3.org/2000/svg">
         <linearGradient id="grad6" x1="0%" y1="0%" x2="100%" y2="100%">
             <stop offset="0%" style="stop-color:#1E90FF;stop-opacity:1" />
@@ -712,7 +720,7 @@
             <p style="font-size: 14px; color: white; margin-top: 8px;">{industry_regions[industry_id]["name"]}</p>
         </div>
         """, unsafe_allow_html=True)
-        
+
         # Use Streamlit button (invisible but clickable)
         if st.button(f"Select {industry_id}", key=f"industry_{industry_id}"):
             st.session_state.industry_selected = industry_id
@@ -747,7 +755,7 @@
     if st.button("🔮 Climate resilience predictions"):
         st.session_state.active_function = "climate_resilience"
         st.rerun()
-        
+
 with col6:
     if st.button("📝 Generate interactive climate story"):
         st.session_state.active_function = "climate_story"
@@ -763,20 +771,20 @@
     if st.button("🎨 Artistic high-resolution maps"):
         st.session_state.active_function = "artistic_maps"
         st.rerun()
-        
+
 # Placeholder for future buttons
 with col2:
     pass
-    
+
 with col3:
     pass
-    
+
 with col4:
     pass
-    
+
 with col5:
     pass
-    
+
 with col6:
     pass
 
@@ -929,7 +937,7 @@
 """, unsafe_allow_html=True)
 
 # The actual chat input
-user_input = st.text_input("", key="chat_input", placeholder=placeholder_text)
+user_input = st.text_input("Message CeCe", key="chat_input", placeholder=placeholder_text, label_visibility="collapsed")
 
 # Add navigation buttons using Streamlit components instead of raw HTML/JS
 col1, col2 = st.columns([9, 1])
@@ -938,7 +946,7 @@
     # Create a container for the navigation buttons
     with st.container():
         # Top button in a stylish gradient
-        if st.button("⬆", help="Scroll to Very Top", key="scroll_top_btn", 
+        if st.button("⬆", help="Scroll to Very Top", key="scroll_top_btn",
                     use_container_width=True):
             # Use Streamlit's built-in JavaScript to scroll to absolute top (0,0)
             st.components.v1.html(
@@ -963,9 +971,9 @@
                 """,
                 height=0
             )
-        
+
         # Bottom button in a complementary gradient
-        if st.button("⬇", help="Scroll to Chat Input", key="scroll_input_btn", 
+        if st.button("⬇", help="Scroll to Chat Input", key="scroll_input_btn",
                     use_container_width=True):
             # Use Streamlit's built-in JavaScript to scroll to the chat input
             st.components.v1.html(
@@ -1029,7 +1037,7 @@
 """
 st.markdown(footer_html, unsafe_allow_html=True)
 
-# Fallback response function 
+# Fallback response function
 # Import our OpenAI helper module
 import openai_helper
 
@@ -1046,20 +1054,20 @@ def fallback_response(query):
         "help": "I can help you with climate data analysis, visualization, and scientific calculations. Try one of the preset buttons above to get started, or ask me a specific question about climate data.",
         "rain": "I can help you analyze precipitation patterns, but I don't have access to real-time weather forecasts. For the most accurate rain forecasts, I recommend checking a dedicated weather service. Would you like to explore historical precipitation data for your area instead?"
     }
-    
+
     # Check if the query contains any of our predefined topics
     query_lower = query.lower()
     for topic, response in climate_responses.items():
         if topic in query_lower:
             return response
-    
+
     # Default response if no specific topic is matched
     return "I'm CeCe, your Climate Copilot. I can help you analyze climate data, create visualizations, and perform scientific calculations. Try one of the preset buttons above, or ask me a specific question about climate or weather data!"
 
 # Initialize a conversation lock if it doesn't exist
 if 'conversation_lock' not in st.session_state:
     st.session_state.conversation_lock = False
-    
+
 # Initialize suggested_follow_up if it doesn't exist
 if 'suggested_follow_up' not in st.session_state:
     st.session_state.suggested_follow_up = "Create a chart of climate anomalies in 2023"
@@ -1070,15 +1078,15 @@ def fallback_response(query):
     if user_input != st.session_state.current_query:
         # Lock the conversation to prevent multiple responses
         st.session_state.conversation_lock = True
-        
+
         # Add user message to chat history
         st.session_state.current_query = user_input
         st.session_state.chat_history.append({"role": "user", "content": user_input})
-        
+
         # Set thinking status to true and display the "thinking" indicator
         st.session_state.thinking = True
         st.session_state.query_processed = False
-        
+
         # Rerun to show the thinking status immediately
         st.rerun()
 
@@ -1087,19 +1095,19 @@ def fallback_response(query):
     try:
         # Extract user query from chat history
         user_query = st.session_state.chat_history[-1]["content"] if len(st.session_state.chat_history) >= 1 else ""
-        
+
         # Get chat history for context (excluding welcome message and the most recent user message that we're about to process)
         messages = [
-            {"role": msg["role"], "content": msg["content"]} 
+            {"role": msg["role"], "content": msg["content"]}
             for msg in st.session_state.chat_history[1:-1]  # Skip welcome message and exclude latest user message
         ]
-        
+
         # Import for timeout handling
         import threading
         import queue
-        
+
         response_queue = queue.Queue()
-        
+
         def get_response():
             try:
                 # Use our improved OpenAI helper to generate a response
@@ -1107,15 +1115,15 @@ def get_response():
                 response_queue.put(response)
             except Exception as e:
                 response_queue.put(e)
-        
+
         # Create and start thread
         response_thread = threading.Thread(target=get_response)
         response_thread.daemon = True
         response_thread.start()
-        
+
         # Wait for the thread to complete or timeout
         response_thread.join(40)  # 40 second total timeout
-        
+
         if response_thread.is_alive():
             print("Response generation timed out after 40 seconds")
             response_content = fallback_response(user_query)
@@ -1125,14 +1133,14 @@ def get_response():
             # Get response from queue
             try:
                 response_or_error = response_queue.get(block=False)
-                
+
                 # Check if we got an error
                 if isinstance(response_or_error, Exception):
                     raise response_or_error
-                    
+
                 # Otherwise, we got a response
                 response_content = response_or_error
-                
+
                 # Log success or failure
                 if response_content:
                     print("Successfully generated response using OpenAI API")
@@ -1144,17 +1152,17 @@ def get_response():
                 # This shouldn't happen, but just in case
                 print("Queue was empty - unexpected error")
                 response_content = fallback_response(user_query)
-            
+
     except Exception as e:
         # Something went wrong, provide an error message with details
         error_msg = str(e)
         print(f"Error processing chat request: {error_msg}")
         # Use a more user-friendly message without exposing technical details
         response_content = fallback_response(user_query)
-    
+
     # Add the response to chat history
     st.session_state.chat_history.append({"role": "assistant", "content": response_content})
-    
+
     # Extract follow-up suggestion from response if available - handle multiple patterns
     # Common patterns for follow-up suggestions
     follow_up_patterns = [
@@ -1164,7 +1172,7 @@ def get_response():
         "I can also",
         "Would you like to see"
     ]
-    
+
     # Check for each pattern
     for pattern in follow_up_patterns:
         if pattern in response_content:
@@ -1176,14 +1184,14 @@ def get_response():
                 suggestion = f"{pattern} {suggestion_text}?"
                 st.session_state.suggested_follow_up = suggestion
                 break
-    
+
     # Set thinking to False and mark query as processed
     st.session_state.thinking = False
     st.session_state.query_processed = True
-    
+
     # Unlock the conversation for new queries
     st.session_state.conversation_lock = False
-    
+
     # Clear the input field and refresh the page
     st.rerun()
 
@@ -1195,14 +1203,14 @@ def get_city_coordinates(city_name):
     try:
         # Create a more robust user agent string
         geolocator = Nominatim(user_agent="climate_copilot_application")
-        
+
         # Try to geocode with the original city name
         location = geolocator.geocode(city_name, timeout=10, exactly_one=True)
-        
+
         # If successful, return the coordinates
         if location:
             return location.latitude, location.longitude
-            
+
         # If not successful, try some alternative formats
         # Try without commas
         if "," in city_name:
@@ -1210,7 +1218,7 @@ def get_city_coordinates(city_name):
             location = geolocator.geocode(clean_name, timeout=10, exactly_one=True)
             if location:
                 return location.latitude, location.longitude
-        
+
         # Try adding explicit country if not present
         if "," not in city_name and " " in city_name:
             # This might be a city without a country specified
@@ -1219,10 +1227,10 @@ def get_city_coordinates(city_name):
                 location = geolocator.geocode(test_name, timeout=10, exactly_one=True)
                 if location:
                     return location.latitude, location.longitude
-        
+
         # If all attempts fail, return None
         return None, None
-        
+
     except Exception as e:
         st.error(f"Error geocoding city: {str(e)}")
         return None, None
@@ -1236,22 +1244,22 @@ def get_city_coordinates(city_name):
 if st.session_state.active_function == "artistic_maps":
     # Run the simplified artistic map demo
     run_artistic_map_demo()
-    
+
 elif st.session_state.active_function == "contour_map":
     # Interactive terrain contour map
     from simple_contour_map import display_simple_contour_map
     display_simple_contour_map()
-    
+
 elif st.session_state.active_function == "precipitation_map":
     st.subheader("Precipitation Map for Your Region")
-    
+
     # Location input method selection
     location_method = st.radio("Select location input method:", ["City Name", "Coordinates"], horizontal=True)
-    
+
     if location_method == "City Name":
-        city = st.text_input("Enter city name (e.g., 'New York', 'London, UK')", 
+        city = st.text_input("Enter city name (e.g., 'New York', 'London, UK')",
                              value="San Francisco, CA" if "last_city" not in st.session_state else st.session_state.last_city)
-        
+
         if city:
             st.session_state.last_city = city
             lat, lon = get_city_coordinates(city)
@@ -1270,38 +1278,38 @@ def get_city_coordinates(city_name):
             latitude = st.number_input("Latitude", value=st.session_state.user_location["lat"], min_value=-90.0, max_value=90.0)
         with col2:
             longitude = st.number_input("Longitude", value=st.session_state.user_location["lon"], min_value=-180.0, max_value=180.0)
-        
+
         st.session_state.user_location = {"lat": latitude, "lon": longitude}
-    
+
     # Date range
     col1, col2 = st.columns(2)
     with col1:
         start_date = st.date_input("Start Date", datetime.now() - timedelta(days=30))
     with col2:
         end_date = st.date_input("End Date", datetime.now())
-        
+
     # Performance option
     fast_mode = st.checkbox("Use fast map mode (recommended for larger date ranges)", value=True,
                           help="Generates the map quickly using improved interpolation techniques")
-    
+
     if st.button("Generate Precipitation Map"):
         with st.spinner("Fetching precipitation data from NASA POWER API..."):
             try:
                 # Get real precipitation data from NASA POWER API
                 from nasa_data import fetch_precipitation_map_data
-                
+
                 # Convert dates to string format for API
                 start_date_str = start_date.strftime('%Y-%m-%d')
                 end_date_str = end_date.strftime('%Y-%m-%d')
-                
+
                 # Status message
                 st.text(f"Fetching precipitation data for {city if location_method == 'City Name' else f'({latitude:.2f}, {longitude:.2f})'} from {start_date_str} to {end_date_str}...")
-                
+
                 # Fetch NASA POWER precipitation data for a region
                 # Pass the fast_mode parameter to control data fetching behavior
-                precip_df = fetch_precipitation_map_data(latitude, longitude, start_date_str, end_date_str, 
+                precip_df = fetch_precipitation_map_data(latitude, longitude, start_date_str, end_date_str,
                                                       radius_degrees=1.0, fast_mode=fast_mode)
-                
+
                 # Create a list of [lat, lon, precip] for each grid point
                 heat_data = []
                 for _, row in precip_df.iterrows():
@@ -1309,35 +1317,35 @@ def get_city_coordinates(city_name):
                     lon = row['longitude']
                     precip = row['precipitation']
                     heat_data.append([lat, lon, precip])
-                
+
                 # Get the max value for scaling the heatmap
                 max_precip = max([x[2] for x in heat_data]) if heat_data else 100
-                
+
                 # Create a standard base map centered on the location first (we'll decide which to show later)
-                m = folium.Map(location=[latitude, longitude], zoom_start=7, 
+                m = folium.Map(location=[latitude, longitude], zoom_start=7,
                               tiles="cartodb dark_matter")
-                
+
                 # Add a marker for the selected location
                 folium.Marker(
                     [latitude, longitude],
                     popup=f"Selected Location: {city if location_method == 'City Name' else f'({latitude:.2f}, {longitude:.2f})'}",
                     icon=folium.Icon(color="purple")
                 ).add_to(m)
-                
+
                 # Map style selection
                 map_styles = ["Standard Map", "Felt-Inspired Map Demo"]
                 map_style = st.radio("Map Style", map_styles, index=0)
-                
+
                 if map_style == "Felt-Inspired Map Demo":
                     # Use Felt-inspired maps for a more modern look
                     st.info("Using Felt-inspired map with enhanced visualization and interactive features.")
-                    
+
                     # Display the title
                     st.subheader(f"NASA POWER Precipitation Map ({start_date_str} to {end_date_str})")
-                    
+
                     # Import our new embedded map module
                     import embedded_felt_map
-                    
+
                     # Create an embedded Felt-inspired map with the current location
                     location_name = city if location_method == 'City Name' else f'({latitude:.2f}, {longitude:.2f})'
                     embedded_felt_map.create_embedded_felt_map(
@@ -1345,7 +1353,7 @@ def get_city_coordinates(city_name):
                         lon=longitude,
                         location_name=location_name
                     )
-                    
+
                     # Add info about the map
                     with st.expander("About this Map"):
                         st.markdown("""
@@ -1355,13 +1363,13 @@ def get_city_coordinates(city_name):
                         - Points of interest
                         - Clean, modern interface inspired by Felt.com
                         """)
-                    
+
                     # Note about the real data integration
                     st.info("This is showing a sample visualization of precipitation data. In the next update, we'll integrate the real NASA POWER precipitation data with this modern map style.")
                 else:
                     # Add the heatmap to the standard map
                     from folium.plugins import HeatMap
-                    
+
                     # Create a heatmap with string-based gradient values and improved parameters
                     HeatMap(
                         heat_data,
@@ -1379,7 +1387,7 @@ def get_city_coordinates(city_name):
                             '1.0': 'red'
                         }
                     ).add_to(m)
-                    
+
                     # Add a legend
                     legend_html = '''
                     <div style="position: fixed; 
@@ -1413,9 +1421,9 @@ def get_city_coordinates(city_name):
                         </div>
                     </div>
                     '''
-                    
+
                     m.get_root().html.add_child(folium.Element(legend_html))
-                    
+
                     # Title for the map
                     title_html = f'''
                     <h3 style="position: absolute; 
@@ -1430,20 +1438,20 @@ def get_city_coordinates(city_name):
                         Precipitation Map for {city if location_method == 'City Name' else f'({latitude:.2f}, {longitude:.2f})'}
                     </h3>
                     '''
-                    
+
                     m.get_root().html.add_child(folium.Element(title_html))
-                    
+
                     # Display the standard map
                     st.subheader(f"NASA POWER Precipitation Map ({start_date_str} to {end_date_str})")
                     folium_static(m)
-                    
+
                     # Download button is now handled outside the if/else statement
-                
 
-                
+
+
                 # Add context about the data
                 st.info(f"This map shows real precipitation data from NASA POWER API around your selected location for the date range {start_date_str} to {end_date_str}. Data source: NASA POWER (Prediction of Worldwide Energy Resources).")
-                
+
                 # Add "What is NASA POWER" explanation
                 with st.expander("What is NASA POWER data?"):
                     st.markdown("""
@@ -1459,7 +1467,7 @@ def get_city_coordinates(city_name):
                     
                     The precipitation data shown on this map represents the total accumulated precipitation (rain, snow, etc.) over the selected time period, measured in millimeters.
                     """)
-                
+
                 # Option to download the map
                 if map_style == "Standard Map":
                     st.download_button(
@@ -1468,12 +1476,12 @@ def get_city_coordinates(city_name):
                         file_name="precipitation_map.html",
                         mime="text/html"
                     )
-                
+
             except Exception as e:
                 # If NASA POWER API data fetching fails, display error and fallback to simulated data
                 st.error(f"Error fetching NASA POWER data: {str(e)}")
                 st.warning("Falling back to simulated precipitation data for demonstration purposes.")
-                
+
                 # Note about NASA POWER API
                 st.markdown("""
                 ### About NASA POWER API:
@@ -1486,10 +1494,10 @@ def get_city_coordinates(city_name):
                 
                 Please try again later or try with different parameters.
                 """)
-                
+
                 # Create a fallback map
                 m = folium.Map(location=[latitude, longitude], zoom_start=10, tiles="cartodb dark_matter")
-                
+
                 # Add a heatmap layer for precipitation
                 import random
                 heat_data = []
@@ -1500,10 +1508,10 @@ def get_city_coordinates(city_name):
                     # Random precipitation values between 0 and 50mm
                     intensity = random.random() * 50
                     heat_data.append([heat_lat, heat_lon, intensity])
-                
+
                 from folium.plugins import HeatMap
                 HeatMap(
-                    heat_data, 
+                    heat_data,
                     radius=25,  # Increased radius for more coverage
                     min_opacity=0.5,  # Lower min_opacity for better visibility
                     blur=15,  # Increased blur for smoother transitions
@@ -1516,46 +1524,46 @@ def get_city_coordinates(city_name):
                         '1.0': 'red'
                     }
                 ).add_to(m)
-                
+
                 # Add a marker for the selected location
                 folium.Marker(
                     [latitude, longitude],
                     popup=f"Selected Location ({latitude:.4f}, {longitude:.4f})",
                     icon=folium.Icon(color="purple")
                 ).add_to(m)
-                
+
                 # Display the map
                 st.subheader(f"Simulated Precipitation Heatmap (DEMO MODE)")
                 folium_static(m)
-                
+
                 # Add note about the simulated data
                 st.warning("This is simulated data for demonstration purposes only. It does not represent real precipitation patterns.")
 
 elif st.session_state.active_function == "industry_map":
     st.subheader("Industry-Specific Climate Risk Analysis")
-    
+
     # Display a select box to choose the industry
     industry_options = list(industry_regions.keys())
     industry_names = [industry_regions[i]["name"] for i in industry_options]
-    
+
     industry_index = 0  # Default to aerospace
     if "industry_selected" in st.session_state:
         if st.session_state.industry_selected in industry_options:
             industry_index = industry_options.index(st.session_state.industry_selected)
-    
+
     selected_industry_name = st.selectbox(
         "Select Industry Sector:",
         industry_names,
         index=industry_index
     )
-    
+
     # Get the industry ID from the name
     selected_industry = industry_options[industry_names.index(selected_industry_name)]
     st.session_state.industry_selected = selected_industry
-    
+
     # Get industry data
     industry_data = industry_regions[selected_industry]
-    
+
     # Display industry description
     st.markdown(f"""
     <div style='background-color: rgba(30, 30, 30, 0.6); padding: 15px; border-radius: 8px; margin-bottom: 20px; border-left: 3px solid #1E90FF;'>
@@ -1563,17 +1571,17 @@ def get_city_coordinates(city_name):
         <p style='color: #FFFFFF;'>{industry_data['description']}</p>
     </div>
     """, unsafe_allow_html=True)
-    
+
     col1, col2 = st.columns([2, 1])
-    
+
     with col1:
         # Create a map centered on the industry's default location
         m = folium.Map(
-            location=industry_data["center"], 
+            location=industry_data["center"],
             zoom_start=industry_data["zoom"],
             tiles="cartodb dark_matter"
         )
-        
+
         # Add climate risk zones based on industry type
         if selected_industry == "aerospace":
             # Add wind pattern areas
@@ -1585,7 +1593,7 @@ def get_city_coordinates(city_name):
                 fill_opacity=0.4,
                 popup="High Wind Zone - Average wind speed >25 mph"
             ).add_to(m)
-            
+
             # Add turbulence risk zone
             folium.Circle(
                 location=[industry_data["center"][0] - 0.1, industry_data["center"][1] + 0.2],
@@ -1595,7 +1603,7 @@ def get_city_coordinates(city_name):
                 fill_opacity=0.4,
                 popup="Turbulence Risk Zone - 65% probability of moderate or severe turbulence"
             ).add_to(m)
-            
+
             # Add visibility issues zone
             folium.Circle(
                 location=[industry_data["center"][0] + 0.3, industry_data["center"][1] + 0.1],
@@ -1605,7 +1613,7 @@ def get_city_coordinates(city_name):
                 fill_opacity=0.3,
                 popup="Low Visibility Zone - Historical fog patterns"
             ).add_to(m)
-            
+
             # Add flight paths with climate risk indicators
             folium.PolyLine(
                 locations=[
@@ -1617,7 +1625,7 @@ def get_city_coordinates(city_name):
                 opacity=0.7,
                 popup="Primary Flight Path - Low climate risk"
             ).add_to(m)
-            
+
             folium.PolyLine(
                 locations=[
                     [industry_data["center"][0], industry_data["center"][1]],
@@ -1628,7 +1636,7 @@ def get_city_coordinates(city_name):
                 opacity=0.7,
                 popup="Secondary Flight Path - High climate risk (wind shear)"
             ).add_to(m)
-            
+
         elif selected_industry == "agriculture":
             # Add drought risk zones
             folium.Circle(
@@ -1639,7 +1647,7 @@ def get_city_coordinates(city_name):
                 fill_opacity=0.4,
                 popup="Drought Risk Zone - 40% precipitation deficit"
             ).add_to(m)
-            
+
             # Add frost risk zone
             folium.Circle(
                 location=[industry_data["center"][0] - 0.5, industry_data["center"][1] + 0.2],
@@ -1649,7 +1657,7 @@ def get_city_coordinates(city_name):
                 fill_opacity=0.3,
                 popup="Frost Risk Zone - Early frost probability 35%"
             ).add_to(m)
-            
+
             # Add heat stress zone
             folium.Circle(
                 location=[industry_data["center"][0] + 0.3, industry_data["center"][1] + 0.4],
@@ -1659,7 +1667,7 @@ def get_city_coordinates(city_name):
                 fill_opacity=0.3,
                 popup="Heat Stress Zone - 12 days >90°F per month"
             ).add_to(m)
-            
+
             # Add growing degree day contours
             folium.GeoJson(
                 {
@@ -1689,7 +1697,7 @@ def get_city_coordinates(city_name):
                 },
                 popup=folium.GeoJsonPopup(fields=["name"])
             ).add_to(m)
-            
+
         elif selected_industry == "energy":
             # Add extreme heat risk to infrastructure
             folium.Circle(
@@ -1700,7 +1708,7 @@ def get_city_coordinates(city_name):
                 fill_opacity=0.4,
                 popup="Extreme Heat Risk - Grid stress 30% above normal"
             ).add_to(m)
-            
+
             # Add flood risk to substations
             folium.Circle(
                 location=[industry_data["center"][0] - 0.3, industry_data["center"][1] + 0.2],
@@ -1710,7 +1718,7 @@ def get_city_coordinates(city_name):
                 fill_opacity=0.3,
                 popup="Flood Risk Zone - 15% of substations vulnerable"
             ).add_to(m)
-            
+
             # Add transmission lines with risk indicators
             folium.PolyLine(
                 locations=[
@@ -1722,7 +1730,7 @@ def get_city_coordinates(city_name):
                 opacity=0.7,
                 popup="Main Transmission Corridor - Medium climate risk"
             ).add_to(m)
-            
+
             # Add wind risk to transmission
             folium.Circle(
                 location=[industry_data["center"][0] + 0.5, industry_data["center"][1] - 0.1],
@@ -1732,7 +1740,7 @@ def get_city_coordinates(city_name):
                 fill_opacity=0.3,
                 popup="High Wind Risk - 25% increased line damage probability"
             ).add_to(m)
-            
+
         elif selected_industry == "insurance":
             # Add flood risk zones
             folium.Circle(
@@ -1743,7 +1751,7 @@ def get_city_coordinates(city_name):
                 fill_opacity=0.4,
                 popup="Flood Zone A - High risk, 26% annual premium increase"
             ).add_to(m)
-            
+
             # Add hurricane path with risk contours
             folium.PolyLine(
                 locations=[
@@ -1757,7 +1765,7 @@ def get_city_coordinates(city_name):
                 opacity=0.7,
                 popup="Historical Hurricane Path - Category 3-4"
             ).add_to(m)
-            
+
             # Add storm surge risk
             folium.Circle(
                 location=[industry_data["center"][0] + 0.05, industry_data["center"][1] - 0.05],
@@ -1767,7 +1775,7 @@ def get_city_coordinates(city_name):
                 fill_opacity=0.3,
                 popup="Storm Surge Zone - 9-12 ft surge potential"
             ).add_to(m)
-            
+
             # Add property value risk gradient
             folium.GeoJson(
                 {
@@ -1797,7 +1805,7 @@ def get_city_coordinates(city_name):
                 },
                 popup=folium.GeoJsonPopup(fields=["risk"])
             ).add_to(m)
-            
+
         elif selected_industry == "forestry":
             # Add wildfire risk zones
             folium.Circle(
@@ -1808,7 +1816,7 @@ def get_city_coordinates(city_name):
                 fill_opacity=0.4,
                 popup="Extreme Wildfire Risk - 72% probability within 5 years"
             ).add_to(m)
-            
+
             # Add drought stress zone
             folium.Circle(
                 location=[industry_data["center"][0] - 0.1, industry_data["center"][1] + 0.2],
@@ -1818,7 +1826,7 @@ def get_city_coordinates(city_name):
                 fill_opacity=0.3,
                 popup="Drought Stress Zone - 45% canopy loss risk"
             ).add_to(m)
-            
+
             # Add pest outbreak risk
             folium.Circle(
                 location=[industry_data["center"][0] + 0.3, industry_data["center"][1] + 0.1],
@@ -1828,7 +1836,7 @@ def get_city_coordinates(city_name):
                 fill_opacity=0.3,
                 popup="Pest Outbreak Risk - Bark beetle probability 60%"
             ).add_to(m)
-            
+
             # Add forest management zones
             folium.GeoJson(
                 {
@@ -1858,7 +1866,7 @@ def get_city_coordinates(city_name):
                 },
                 popup=folium.GeoJsonPopup(fields=["name"])
             ).add_to(m)
-            
+
         elif selected_industry == "catastrophes":
             # Add hurricane risk zones
             folium.Circle(
@@ -1869,7 +1877,7 @@ def get_city_coordinates(city_name):
                 fill_opacity=0.4,
                 popup="Hurricane Impact Zone - Category 4-5 risk"
             ).add_to(m)
-            
+
             # Add flood zones
             folium.Circle(
                 location=[industry_data["center"][0] - 0.1, industry_data["center"][1] + 0.1],
@@ -1879,7 +1887,7 @@ def get_city_coordinates(city_name):
                 fill_opacity=0.3,
                 popup="Severe Flood Zone - 25-year flood risk"
             ).add_to(m)
-            
+
             # Add evacuation routes with risk assessment
             folium.PolyLine(
                 locations=[
@@ -1891,7 +1899,7 @@ def get_city_coordinates(city_name):
                 opacity=0.7,
                 popup="Primary Evacuation Route - Low flood risk"
             ).add_to(m)
-            
+
             folium.PolyLine(
                 locations=[
                     [industry_data["center"][0], industry_data["center"][1]],
@@ -1902,7 +1910,7 @@ def get_city_coordinates(city_name):
                 opacity=0.7,
                 popup="Secondary Evacuation Route - Medium flood risk"
             ).add_to(m)
-            
+
             # Add storm surge risk contour
             folium.GeoJson(
                 {
@@ -1932,14 +1940,14 @@ def get_city_coordinates(city_name):
                 },
                 popup=folium.GeoJsonPopup(fields=["name"])
             ).add_to(m)
-        
+
         # Add a marker for the primary location
         folium.Marker(
             industry_data["center"],
             popup=f"{industry_data['name']} Industry Hub",
             icon=folium.Icon(color="green", icon="info-sign"),
         ).add_to(m)
-        
+
         # Add a title to the map
         title_html = f"""
         <h3 style="position: absolute; top: 10px; left: 50%; transform: translateX(-50%); z-index: 9999; 
@@ -1948,14 +1956,14 @@ def get_city_coordinates(city_name):
         </h3>
         """
         m.get_root().html.add_child(folium.Element(title_html))
-        
+
         # Add a legend for the climate risks
         legend_html = """
         <div style="position: fixed; bottom: 50px; right: 50px; background-color: rgba(0, 0, 0, 0.7);
             border-radius: 5px; padding: 10px; color: white; font-family: Arial, sans-serif; z-index: 9999;">
             <p><strong>Climate Risk Legend</strong></p>
         """
-        
+
         risk_colors = {
             "#FF4500": "Extreme Risk",
             "#FF8C00": "High Risk",
@@ -1964,7 +1972,7 @@ def get_city_coordinates(city_name):
             "#1E90FF": "Flood/Water Risk",
             "#9370DB": "Wind/Storm Risk"
         }
-        
+
         for color, label in risk_colors.items():
             legend_html += f"""
             <div style="display: flex; align-items: center; margin-bottom: 5px;">
@@ -1972,13 +1980,13 @@ def get_city_coordinates(city_name):
                 <span>{label}</span>
             </div>
             """
-        
+
         legend_html += "</div>"
         m.get_root().html.add_child(folium.Element(legend_html))
-        
+
         # Display the map
         folium_static(m)
-    
+
     with col2:
         # Display industry climate risks
         st.markdown(f"""
@@ -1986,47 +1994,47 @@ def get_city_coordinates(city_name):
             <h4 style='color: #FFFFFF; margin-top: 0;'>Climate Risks</h4>
             <ul style='color: #FFFFFF; padding-left: 20px;'>
         """, unsafe_allow_html=True)
-        
+
         for risk in industry_data["risks"]:
             st.markdown(f"<li>{risk}</li>", unsafe_allow_html=True)
-        
+
         st.markdown("</ul></div>", unsafe_allow_html=True)
-        
+
         # Display climate parameters being monitored
         st.markdown(f"""
         <div style='background-color: rgba(30, 144, 255, 0.1); padding: 15px; border-radius: 8px; margin-bottom: 20px; border-left: 3px solid #1E90FF;'>
             <h4 style='color: #FFFFFF; margin-top: 0;'>Key Climate Parameters</h4>
             <ul style='color: #FFFFFF; padding-left: 20px;'>
         """, unsafe_allow_html=True)
-        
+
         for param in industry_data["parameters"]:
             st.markdown(f"<li>{param.replace('_', ' ').title()}</li>", unsafe_allow_html=True)
-        
+
         st.markdown("</ul></div>", unsafe_allow_html=True)
-        
+
         # Display risk mitigation strategies
         st.markdown(f"""
         <div style='background-color: rgba(50, 205, 50, 0.1); padding: 15px; border-radius: 8px; margin-bottom: 20px; border-left: 3px solid #32CD32;'>
             <h4 style='color: #FFFFFF; margin-top: 0;'>Climate Risk Mitigation Strategies</h4>
             <ul style='color: #FFFFFF; padding-left: 20px;'>
         """, unsafe_allow_html=True)
-        
+
         for strategy in industry_data["mitigation"]:
             st.markdown(f"<li>{strategy}</li>", unsafe_allow_html=True)
-        
+
         st.markdown("</ul></div>", unsafe_allow_html=True)
-        
+
         # Add a "Create custom analysis" button
         if st.button("Create Custom Climate Risk Analysis", key="custom_analysis"):
             st.session_state.active_function = "precipitation_map"
             st.rerun()
-    
+
     # Add context about the data
     st.info(f"This visualization represents climate risk analysis for the {industry_data['name']} sector based on historical climate data and projected patterns. The data is derived from NASA POWER API and climate models. Use this visualization to understand regional climate risks and develop targeted mitigation strategies.")
 
 elif st.session_state.active_function == "climate_resilience":
     st.subheader("Climate Resilience Prediction Tool")
-    
+
     # Show explanation
     st.markdown("""
     <div style="padding: 15px; background-color: rgba(30, 144, 255, 0.1); border-radius: 8px; margin-bottom: 20px;">
@@ -2037,30 +2045,30 @@ def get_city_coordinates(city_name):
         </p>
     </div>
     """, unsafe_allow_html=True)
-    
+
     # Create two columns for inputs
     col1, col2 = st.columns(2)
-    
+
     with col1:
         # Location input method selection
-        location_method = st.radio("Select location input method:", ["City Name", "Coordinates"], 
+        location_method = st.radio("Select location input method:", ["City Name", "Coordinates"],
                                  horizontal=True, key="resilience_location_method")
-        
+
         # Initialize location session variables if not present
         if "user_location" not in st.session_state:
             st.session_state.user_location = {"lat": 37.7749, "lon": -122.4194}
         if "last_city" not in st.session_state:
             st.session_state.last_city = "San Francisco, CA"
-        
+
         # Flag to track if location has changed
         if "resilience_data_needs_update" not in st.session_state:
             st.session_state.resilience_data_needs_update = True
-        
+
         if location_method == "City Name":
-            city = st.text_input("Enter city name (e.g., 'New York', 'London, UK')", 
+            city = st.text_input("Enter city name (e.g., 'New York', 'London, UK')",
                                  value=st.session_state.last_city,
                                  key="resilience_city")
-            
+
             if city:
                 # Only geocode if city changed
                 if city != st.session_state.last_city:
@@ -2068,15 +2076,15 @@ def get_city_coordinates(city_name):
                     lat, lon = get_city_coordinates(city)
                     if lat and lon:
                         st.success(f"Location found: {lat:.4f}, {lon:.4f}")
-                        
+
                         # Check if location has changed
-                        if (abs(st.session_state.user_location.get("lat", 0) - lat) > 0.001 or 
+                        if (abs(st.session_state.user_location.get("lat", 0) - lat) > 0.001 or
                             abs(st.session_state.user_location.get("lon", 0) - lon) > 0.001):
                             st.session_state.user_location = {"lat": lat, "lon": lon}
                             # Force a refresh of resilience data
                             st.session_state.resilience_data_needs_update = True
                             st.info("Location updated! Climate data will refresh when you generate the report.")
-                            
+
                         latitude = lat
                         longitude = lon
                     else:
@@ -2091,19 +2099,19 @@ def get_city_coordinates(city_name):
                 longitude = st.session_state.user_location["lon"]
         else:
             # Direct coordinate input
-            latitude = st.number_input("Latitude", value=st.session_state.user_location["lat"], 
+            latitude = st.number_input("Latitude", value=st.session_state.user_location["lat"],
                                       min_value=-90.0, max_value=90.0, step=0.01, key="resilience_lat")
-            longitude = st.number_input("Longitude", value=st.session_state.user_location["lon"], 
+            longitude = st.number_input("Longitude", value=st.session_state.user_location["lon"],
                                        min_value=-180.0, max_value=180.0, step=0.01, key="resilience_lon")
-            
+
             # Check if coordinates have changed
-            if (abs(st.session_state.user_location.get("lat", 0) - latitude) > 0.001 or 
+            if (abs(st.session_state.user_location.get("lat", 0) - latitude) > 0.001 or
                 abs(st.session_state.user_location.get("lon", 0) - longitude) > 0.001):
                 st.session_state.user_location = {"lat": latitude, "lon": longitude}
                 # Force a refresh of resilience data
                 st.session_state.resilience_data_needs_update = True
                 st.info("Location updated! Climate data will refresh when you generate the report.")
-        
+
         # Industry selection
         industry_options = ["aerospace", "agriculture", "energy", "insurance", "forestry", "catastrophes"]
         industry_names = {
@@ -2114,21 +2122,21 @@ def get_city_coordinates(city_name):
             "forestry": "Forestry",
             "catastrophes": "Catastrophe Management"
         }
-        
-        selected_industry = st.selectbox("Select Industry", 
+
+        selected_industry = st.selectbox("Select Industry",
                                        options=industry_options,
                                        format_func=lambda x: industry_names[x],
                                        key="resilience_industry")
-    
+
     with col2:
         # Time horizon selection
-        target_year = st.slider("Target Year for Projection", 
-                              min_value=2030, 
-                              max_value=2100, 
-                              value=2050, 
+        target_year = st.slider("Target Year for Projection",
+                              min_value=2030,
+                              max_value=2100,
+                              value=2050,
                               step=5,
                               key="resilience_year")
-        
+
         # Climate scenario selection
         scenario_options = ["optimistic", "moderate", "severe"]
         scenario_descriptions = {
@@ -2136,44 +2144,44 @@ def get_city_coordinates(city_name):
             "moderate": "Moderate Scenario (RCP 4.5) - Intermediate warming",
             "severe": "Severe Scenario (RCP 8.5) - High emissions scenario"
         }
-        
-        selected_scenario = st.selectbox("Select Climate Scenario", 
+
+        selected_scenario = st.selectbox("Select Climate Scenario",
                                       options=scenario_options,
                                       format_func=lambda x: scenario_descriptions[x],
                                       key="resilience_scenario")
-        
+
         # Button to generate the report
         generate_report = st.button("Generate Resilience Report", type="primary", key="generate_resilience_report")
-    
+
     # Map visualization selection
     st.write("### Climate Impact Map Visualization")
     map_view_options = [
-        "Location Only", 
-        "Temperature Change", 
-        "Precipitation Change", 
-        "Sea Level Rise Impact", 
-        "Extreme Heat Days", 
+        "Location Only",
+        "Temperature Change",
+        "Precipitation Change",
+        "Sea Level Rise Impact",
+        "Extreme Heat Days",
         "Industry Risk Zones"
     ]
     selected_map_view = st.selectbox(
-        "Select map visualization type:", 
+        "Select map visualization type:",
         options=map_view_options,
         key="resilience_map_view"
     )
-    
+
     # Initialize the base map
     m = folium.Map(location=[latitude, longitude], zoom_start=5, control_scale=True)
-    
+
     # Add the base marker for selected location
     folium.Marker(
         [latitude, longitude],
         popup=f"Selected Location: {city if location_method == 'City Name' else f'{latitude:.4f}, {longitude:.4f}'}",
         icon=folium.Icon(color="blue", icon="info-sign")
     ).add_to(m)
-    
+
     # Get the report from session state if available
     report = st.session_state.resilience_report if 'resilience_report' in st.session_state and st.session_state.resilience_report else None
-    
+
     # Generate different map visualizations based on selection
     if selected_map_view == "Temperature Change":
         # Create a circle around the location showing temperature change
@@ -2181,7 +2189,7 @@ def get_city_coordinates(city_name):
             try:
                 # Use the temperature change data from the report
                 temp_change = report['climate_projections']['temperature']['change']
-                
+
                 # Determine color based on the temperature change
                 if temp_change < 1.0:
                     color = "#4575b4"  # Blue for minor warming
@@ -2191,7 +2199,7 @@ def get_city_coordinates(city_name):
                     color = "#fdae61"  # Orange for significant warming
                 else:
                     color = "#d73027"  # Red for severe warming
-                
+
                 # Add a circle with a radius based on the magnitude of change
                 radius_km = 50 + (temp_change * 15)  # Scale the radius by temperature change
                 folium.Circle(
@@ -2202,7 +2210,7 @@ def get_city_coordinates(city_name):
                     fill_opacity=0.5,
                     popup=f"Projected Temperature Change: +{temp_change:.1f}°C by {target_year}",
                 ).add_to(m)
-                
+
                 # Add a legend
                 legend_html = '''
                     <div style="position: fixed; bottom: 50px; left: 50px; background-color: white; 
@@ -2217,14 +2225,14 @@ def get_city_coordinates(city_name):
                 m.get_root().html.add_child(folium.Element(legend_html))
             except:
                 st.info("Generate a report first to see temperature change projections on the map.")
-    
+
     elif selected_map_view == "Precipitation Change":
         # Create a visualization for precipitation change
         if report:  # Only show if report is available
             try:
                 # Use the precipitation change data from the report
                 precip_change = report['climate_projections']['precipitation']['change_percent']
-                
+
                 # Determine color based on the precipitation change
                 if precip_change < -10:
                     color = "#d73027"  # Red for significant drying
@@ -2234,7 +2242,7 @@ def get_city_coordinates(city_name):
                     color = "#fee090"  # Yellow for minor changes
                 else:
                     color = "#4575b4"  # Blue for wetter conditions
-                
+
                 # Add a circle with a radius based on the magnitude of change
                 radius_km = 50 + (abs(precip_change) * 1)  # Scale the radius by precipitation change
                 folium.Circle(
@@ -2245,7 +2253,7 @@ def get_city_coordinates(city_name):
                     fill_opacity=0.5,
                     popup=f"Projected Precipitation Change: {precip_change:.1f}% by {target_year}",
                 ).add_to(m)
-                
+
                 # Add a legend
                 legend_html = '''
                     <div style="position: fixed; bottom: 50px; left: 50px; background-color: white; 
@@ -2260,17 +2268,17 @@ def get_city_coordinates(city_name):
                 m.get_root().html.add_child(folium.Element(legend_html))
             except:
                 st.info("Generate a report first to see precipitation change projections on the map.")
-    
+
     elif selected_map_view == "Sea Level Rise Impact":
         # Create a visualization for sea level rise impact
         if report:  # Only show if report is available
             try:
                 # Use the sea level rise data from the report
                 slr = report['climate_projections']['sea_level_rise']
-                
+
                 # Coastal vulnerability threshold (in km from the center)
                 coastal_zone_km = 30
-                
+
                 # Add a coastal vulnerability zone (simplified)
                 folium.Circle(
                     location=[latitude, longitude],
@@ -2280,7 +2288,7 @@ def get_city_coordinates(city_name):
                     fill_opacity=0.4,
                     popup=f"Projected Sea Level Rise: {slr:.2f}m by {target_year}",
                 ).add_to(m)
-                
+
                 # Add more detailed annotations
                 if slr > 0.5:
                     # Add high risk zone for significant sea level rise
@@ -2292,7 +2300,7 @@ def get_city_coordinates(city_name):
                         fill_opacity=0.4,
                         popup="High risk zone with potential inundation",
                     ).add_to(m)
-                
+
                 # Add a legend
                 legend_html = f'''
                     <div style="position: fixed; bottom: 50px; left: 50px; background-color: white; 
@@ -2306,18 +2314,18 @@ def get_city_coordinates(city_name):
                 m.get_root().html.add_child(folium.Element(legend_html))
             except:
                 st.info("Generate a report first to see sea level rise projections on the map.")
-    
+
     elif selected_map_view == "Extreme Heat Days":
         # Create a visualization for extreme heat days
         if report:  # Only show if report is available
             try:
                 # Use the extreme heat data from the report
                 heat_multiplier = report['climate_projections']['extreme_weather']['heat_days_multiplier']
-                
+
                 # Estimate current extreme heat days (simplified model)
                 baseline_heat_days = 5  # Assumed baseline
                 projected_heat_days = int(baseline_heat_days * heat_multiplier)
-                
+
                 # Determine color based on the number of extreme heat days
                 if heat_multiplier < 1.5:
                     color = "#fee090"  # Yellow for minor increase
@@ -2327,7 +2335,7 @@ def get_city_coordinates(city_name):
                     color = "#f46d43"  # Dark orange for significant increase
                 else:
                     color = "#d73027"  # Red for severe increase
-                
+
                 # Add a heat impact radius
                 radius_km = 40 + (heat_multiplier * 10)  # Scale the radius by heat multiplier
                 folium.Circle(
@@ -2338,7 +2346,7 @@ def get_city_coordinates(city_name):
                     fill_opacity=0.5,
                     popup=f"Extreme Heat Days Projection: {projected_heat_days} days/year (x{heat_multiplier:.1f} increase) by {target_year}",
                 ).add_to(m)
-                
+
                 # Add a legend
                 legend_html = f'''
                     <div style="position: fixed; bottom: 50px; left: 50px; background-color: white; 
@@ -2355,14 +2363,14 @@ def get_city_coordinates(city_name):
                 m.get_root().html.add_child(folium.Element(legend_html))
             except:
                 st.info("Generate a report first to see extreme heat day projections on the map.")
-    
+
     elif selected_map_view == "Industry Risk Zones":
         # Create a visualization specific to the selected industry
         if report and 'selected_industry' in locals():  # Only show if report is available
             try:
                 # Use the impact assessment from the report
                 impact_severity = report['impact_assessment']['adjusted_severity']
-                
+
                 # Define colors based on severity
                 severity_colors = {
                     "low": "#4CAF50",     # Green
@@ -2370,9 +2378,9 @@ def get_city_coordinates(city_name):
                     "high": "#FF9800",     # Orange
                     "severe": "#F44336"    # Red
                 }
-                
+
                 color = severity_colors.get(impact_severity, "#4CAF50")
-                
+
                 # Create concentric circles showing impact zones
                 # High impact zone
                 folium.Circle(
@@ -2383,7 +2391,7 @@ def get_city_coordinates(city_name):
                     fill_opacity=0.6,
                     popup=f"High Impact Zone: {industry_names[selected_industry]} Industry",
                 ).add_to(m)
-                
+
                 # Medium impact zone
                 folium.Circle(
                     location=[latitude, longitude],
@@ -2393,7 +2401,7 @@ def get_city_coordinates(city_name):
                     fill_opacity=0.3,
                     popup=f"Medium Impact Zone: {industry_names[selected_industry]} Industry",
                 ).add_to(m)
-                
+
                 # Low impact zone
                 folium.Circle(
                     location=[latitude, longitude],
@@ -2403,7 +2411,7 @@ def get_city_coordinates(city_name):
                     fill_opacity=0.1,
                     popup=f"Low Impact Zone: {industry_names[selected_industry]} Industry",
                 ).add_to(m)
-                
+
                 # Add industry-specific markers based on the type of industry
                 if selected_industry == "agriculture":
                     # Add crop vulnerability markers
@@ -2417,7 +2425,7 @@ def get_city_coordinates(city_name):
                             popup=f"Agricultural Impact Point {i+1}",
                             icon=folium.Icon(color="green", icon="leaf")
                         ).add_to(m)
-                        
+
                 elif selected_industry == "energy":
                     # Add energy infrastructure vulnerability markers
                     for i, (energy_lat, energy_lon) in enumerate([
@@ -2430,7 +2438,7 @@ def get_city_coordinates(city_name):
                             popup=f"Energy Infrastructure Point {i+1}",
                             icon=folium.Icon(color="orange", icon="flash")
                         ).add_to(m)
-                        
+
                 elif selected_industry == "forestry":
                     # Add forest vulnerability markers
                     for i, (forest_lat, forest_lon) in enumerate([
@@ -2443,7 +2451,7 @@ def get_city_coordinates(city_name):
                             popup=f"Forest Vulnerability Point {i+1}",
                             icon=folium.Icon(color="green", icon="tree")
                         ).add_to(m)
-                
+
                 # Add a legend
                 legend_html = f'''
                     <div style="position: fixed; bottom: 50px; left: 50px; background-color: white; 
@@ -2458,14 +2466,14 @@ def get_city_coordinates(city_name):
                 m.get_root().html.add_child(folium.Element(legend_html))
             except:
                 st.info("Generate a report first to see industry risk zones on the map.")
-    
+
     # Add the map to the Streamlit app
     st_data = folium_static(m)
-    
+
     # Initialize report in session state if not present to ensure proper scope
     if 'resilience_report' not in st.session_state:
         st.session_state.resilience_report = None
-        
+
     # Generate and display the resilience report when the button is clicked
     if generate_report:
         with st.spinner(f"Generating climate resilience report for {industry_names[selected_industry]} industry in {target_year}..."):
@@ -2473,10 +2481,10 @@ def get_city_coordinates(city_name):
             try:
                 # Reset the update flag since we're fetching new data
                 st.session_state.resilience_data_needs_update = False
-                
+
                 # Log key parameters for debugging
                 st.write(f"Fetching climate data for: {latitude}, {longitude}")
-                
+
                 # Store the report in session state for global access
                 st.session_state.resilience_report = climate_resilience.generate_resilience_report(
                     lat=latitude,
@@ -2485,13 +2493,13 @@ def get_city_coordinates(city_name):
                     target_year=target_year,
                     scenario=selected_scenario
                 )
-                
+
                 # Create a local reference to the report for convenience
                 report = st.session_state.resilience_report
-                
+
                 # Display the report in an organized fashion
                 st.subheader(f"Climate Resilience Report: {industry_names[selected_industry]} Industry")
-                
+
                 # Scenario and location information
                 st.markdown(f"""
                 <div style="padding: 15px; background-color: rgba(30, 30, 30, 0.6); border-radius: 8px; margin-bottom: 20px;">
@@ -2516,13 +2524,13 @@ def get_city_coordinates(city_name):
                     </table>
                 </div>
                 """, unsafe_allow_html=True)
-                
+
                 # Climate projections visualization
                 st.markdown("<h4 style='color: #1E90FF;'>Climate Projections</h4>", unsafe_allow_html=True)
-                
+
                 # Use plotly to create visualization of key metrics
                 fig = go.Figure()
-                
+
                 # Temperature change
                 fig.add_trace(go.Indicator(
                     mode = "number+delta",
@@ -2531,7 +2539,7 @@ def get_city_coordinates(city_name):
                     delta = {'reference': report['climate_projections']['temperature']['baseline'], 'relative': False},
                     domain = {'row': 0, 'column': 0}
                 ))
-                
+
                 # Precipitation change
                 fig.add_trace(go.Indicator(
                     mode = "number+delta",
@@ -2540,7 +2548,7 @@ def get_city_coordinates(city_name):
                     delta = {'reference': report['climate_projections']['precipitation']['baseline'], 'relative': False},
                     domain = {'row': 0, 'column': 1}
                 ))
-                
+
                 # Extreme weather multiplier
                 fig.add_trace(go.Indicator(
                     mode = "number",
@@ -2548,7 +2556,7 @@ def get_city_coordinates(city_name):
                     title = {"text": "Extreme Heat Days Multiplier"},
                     domain = {'row': 1, 'column': 0}
                 ))
-                
+
                 # Sea level rise
                 fig.add_trace(go.Indicator(
                     mode = "number",
@@ -2556,7 +2564,7 @@ def get_city_coordinates(city_name):
                     title = {"text": "Sea Level Rise (m)"},
                     domain = {'row': 1, 'column': 1}
                 ))
-                
+
                 # Update layout
                 fig.update_layout(
                     grid = {'rows': 2, 'columns': 2, 'pattern': "independent"},
@@ -2565,20 +2573,20 @@ def get_city_coordinates(city_name):
                     plot_bgcolor="rgba(0,0,0,0)",
                     font=dict(color="white")
                 )
-                
+
                 st.plotly_chart(fig, use_container_width=True)
-                
+
                 # Show seasonal changes in temperature and precipitation
                 st.markdown("<h4 style='color: #1E90FF;'>Seasonal Changes</h4>", unsafe_allow_html=True)
-                
+
                 # Create dataframes for seasonal data
                 seasons = ["winter", "spring", "summer", "fall"]
                 temp_changes = [report['climate_projections']['temperature']['seasonal_changes'][season] for season in seasons]
                 precip_changes = [report['climate_projections']['precipitation']['seasonal_changes'][season] for season in seasons]
-                
+
                 # Create a plotly figure for seasonal changes
                 fig = go.Figure()
-                
+
                 # Add temperature changes
                 fig.add_trace(go.Bar(
                     x=seasons,
@@ -2586,7 +2594,7 @@ def get_city_coordinates(city_name):
                     name='Temperature Change (°C)',
                     marker_color='rgba(30, 144, 255, 0.7)'
                 ))
-                
+
                 # Add precipitation changes on secondary y-axis
                 fig.add_trace(go.Bar(
                     x=seasons,
@@ -2595,7 +2603,7 @@ def get_city_coordinates(city_name):
                     marker_color='rgba(147, 112, 219, 0.7)',
                     yaxis='y2'
                 ))
-                
+
                 # Update layout
                 fig.update_layout(
                     barmode='group',
@@ -2609,9 +2617,9 @@ def get_city_coordinates(city_name):
                     plot_bgcolor="rgba(0,0,0,0)",
                     font=dict(color="white")
                 )
-                
+
                 st.plotly_chart(fig, use_container_width=True)
-                
+
                 # Industry impact assessment
                 impact_severity = report['impact_assessment']['adjusted_severity']
                 severity_colors = {
@@ -2620,7 +2628,7 @@ def get_city_coordinates(city_name):
                     "high": "#FF9800",
                     "severe": "#F44336"
                 }
-                
+
                 st.markdown(f"""
                 <h4 style='color: #1E90FF;'>Industry Impact Assessment</h4>
                 <div style="padding: 15px; background-color: rgba(30, 30, 30, 0.6); border-radius: 8px; margin-bottom: 20px;">
@@ -2634,22 +2642,22 @@ def get_city_coordinates(city_name):
                         <div style="font-weight: bold; margin-bottom: 5px;">Key Impact Areas:</div>
                         <ul style="margin-top: 5px;">
                 """, unsafe_allow_html=True)
-                
+
                 for impact in report['impact_assessment']['impact_areas']:
                     st.markdown(f"<li style='color: white; margin-bottom: 5px;'>{impact}</li>", unsafe_allow_html=True)
-                
+
                 st.markdown("""
                         </ul>
                     </div>
                 </div>
                 """, unsafe_allow_html=True)
-                
+
                 # Adaptive strategies
                 st.markdown("<h4 style='color: #1E90FF;'>Recommended Adaptive Strategies</h4>", unsafe_allow_html=True)
-                
+
                 # Create tabs for different timeline categories
                 timeline_tabs = st.tabs(["Near-term (1-5 years)", "Mid-term (5-15 years)", "Long-term (15+ years)"])
-                
+
                 with timeline_tabs[0]:
                     if report['implementation_timeline']['near_term']:
                         for i, strategy in enumerate(report['implementation_timeline']['near_term']):
@@ -2661,7 +2669,7 @@ def get_city_coordinates(city_name):
                             """, unsafe_allow_html=True)
                     else:
                         st.info("No near-term strategies identified for this scenario.")
-                
+
                 with timeline_tabs[1]:
                     if report['implementation_timeline']['mid_term']:
                         for i, strategy in enumerate(report['implementation_timeline']['mid_term']):
@@ -2673,7 +2681,7 @@ def get_city_coordinates(city_name):
                             """, unsafe_allow_html=True)
                     else:
                         st.info("No mid-term strategies identified for this scenario.")
-                
+
                 with timeline_tabs[2]:
                     if report['implementation_timeline']['long_term']:
                         for i, strategy in enumerate(report['implementation_timeline']['long_term']):
@@ -2685,7 +2693,7 @@ def get_city_coordinates(city_name):
                             """, unsafe_allow_html=True)
                     else:
                         st.info("No long-term strategies identified for this scenario.")
-                
+
                 # Cost implication
                 cost_colors = {
                     "low": "#4CAF50",
@@ -2693,7 +2701,7 @@ def get_city_coordinates(city_name):
                     "high": "#FF9800",
                     "transformative": "#F44336"
                 }
-                
+
                 st.markdown(f"""
                 <div style="padding: 15px; background-color: rgba(30, 30, 30, 0.6); border-radius: 8px; margin-top: 20px; margin-bottom: 20px;">
                     <h4 style="margin-top: 0; color: #1E90FF;">Implementation Considerations</h4>
@@ -2709,7 +2717,7 @@ def get_city_coordinates(city_name):
                     </div>
                 </div>
                 """, unsafe_allow_html=True)
-                
+
                 # Add option to export the report as JSON
                 report_json = json.dumps(report, indent=2)
                 b64 = base64.b64encode(report_json.encode()).decode()
@@ -2719,13 +2727,13 @@ def get_city_coordinates(city_name):
                     </button>
                 </a>"""
                 st.markdown(href, unsafe_allow_html=True)
-                
+
                 # Add information about data sources
                 st.markdown("---")
                 st.markdown("""
                 <h3 style='color: #1E90FF;'>About Our Climate Data</h3>
                 """, unsafe_allow_html=True)
-                
+
                 with st.expander("Why We Trust This Data", expanded=False):
                     st.markdown("""
                     <div style='color: white;'>
@@ -2770,7 +2778,7 @@ def get_city_coordinates(city_name):
                         <p>For the most critical decisions, we recommend consulting with climate scientists and industry-specific experts to supplement this analysis.</p>
                     </div>
                     """, unsafe_allow_html=True)
-                
+
                 with st.expander("Data Processing & Methodology", expanded=False):
                     st.markdown("""
                     <div style='color: white;'>
@@ -2808,7 +2816,7 @@ def get_city_coordinates(city_name):
                         <p>All visualizations are generated using real climate data and industry-specific vulnerabilities established through scientific literature.</p>
                     </div>
                     """, unsafe_allow_html=True)
-                    
+
                 with st.expander("Data Sources & References", expanded=False):
                     st.markdown("""
                     <div style='color: white;'>
@@ -2838,7 +2846,7 @@ def get_city_coordinates(city_name):
                         <p>For more detailed information about our data sources and methodology, please contact us.</p>
                     </div>
                     """, unsafe_allow_html=True)
-                
+
             except Exception as e:
                 st.error(f"An error occurred while generating the report: {str(e)}")
                 st.error("Please try again with different parameters or check the console for more details.")
@@ -2846,15 +2854,15 @@ def get_city_coordinates(city_name):
 
 elif st.session_state.active_function == "climate_story":
     st.subheader("Interactive Climate Story Generator")
-    
+
     # Location input method selection
     location_method = st.radio("Select location input method:", ["City Name", "Coordinates"], horizontal=True, key="story_location_method")
-    
+
     if location_method == "City Name":
-        city = st.text_input("Enter city name (e.g., 'New York', 'London, UK')", 
+        city = st.text_input("Enter city name (e.g., 'New York', 'London, UK')",
                            value="San Francisco, CA" if "last_city" not in st.session_state else st.session_state.last_city,
                            key="story_city_input")
-        
+
         if city:
             st.session_state.last_city = city
             lat, lon = get_city_coordinates(city)
@@ -2870,12 +2878,12 @@ def get_city_coordinates(city_name):
     else:
         col1, col2 = st.columns(2)
         with col1:
-            latitude = st.number_input("Latitude", value=st.session_state.user_location["lat"], 
+            latitude = st.number_input("Latitude", value=st.session_state.user_location["lat"],
                                       min_value=-90.0, max_value=90.0, key="story_lat")
         with col2:
-            longitude = st.number_input("Longitude", value=st.session_state.user_location["lon"], 
+            longitude = st.number_input("Longitude", value=st.session_state.user_location["lon"],
                                        min_value=-180.0, max_value=180.0, key="story_lon")
-    
+
     # Date range
     st.write("Select time period for your climate story:")
     col1, col2 = st.columns(2)
@@ -2883,19 +2891,19 @@ def get_city_coordinates(city_name):
         start_date = st.date_input("Start Date", datetime.now() - timedelta(days=365), key="story_start")
     with col2:
         end_date = st.date_input("End Date", datetime.now(), key="story_end")
-    
+
     # Story type selection
-    story_type = st.radio("Select story type:", 
-                        ["Personal Experience", "Educational", "Historical Context"], 
+    story_type = st.radio("Select story type:",
+                        ["Personal Experience", "Educational", "Historical Context"],
                         horizontal=True, key="story_type")
-    
+
     # Map story type to the format expected by the generator function
     story_type_map = {
         "Personal Experience": "personal",
         "Educational": "educational",
         "Historical Context": "historical"
     }
-    
+
     # Generate button
     if st.button("Generate Climate Story"):
         try:
@@ -2903,39 +2911,39 @@ def get_city_coordinates(city_name):
                 # Convert dates to string format
                 start_date_str = start_date.strftime('%Y-%m-%d')
                 end_date_str = end_date.strftime('%Y-%m-%d')
-                
+
                 # Status message
                 st.text(f"Fetching climate data for {city if location_method == 'City Name' else f'({latitude:.2f}, {longitude:.2f})'} from {start_date_str} to {end_date_str}...")
-                
+
                 # Fetch NASA POWER data
                 from nasa_data import fetch_nasa_power_data
                 climate_data = fetch_nasa_power_data(
-                    latitude, 
-                    longitude, 
-                    start_date_str, 
-                    end_date_str, 
+                    latitude,
+                    longitude,
+                    start_date_str,
+                    end_date_str,
                     parameters=["T2M", "PRECTOTCORR", "RH2M", "WS2M"]
                 )
-                
+
                 if climate_data is None or len(climate_data) == 0:
                     st.error("Could not fetch climate data for the specified location and time period.")
                     st.stop()
-                
+
                 # Prepare location and timeframe data for the story generator
                 location_data = {
                     "city": city if location_method == "City Name" else None,
                     "lat": latitude,
                     "lon": longitude
                 }
-                
+
                 timeframe_data = {
                     "start_date": start_date,
                     "end_date": end_date
                 }
-                
+
                 # Generate the climate story
                 from climate_story_generator import generate_climate_story
-                
+
                 # Check if we have the OpenAI API key
                 if not os.getenv("OPENAI_API_KEY"):
                     st.warning("OpenAI API key not found. Please provide your API key in the settings to use this feature.")
@@ -2946,32 +2954,32 @@ def get_city_coordinates(city_name):
                         st.success("API key set! Click the button again to generate your story.")
                         st.rerun()
                     st.stop()
-                
+
                 # Generate the story
                 story = generate_climate_story(
-                    climate_data, 
-                    location_data, 
-                    timeframe_data, 
+                    climate_data,
+                    location_data,
+                    timeframe_data,
                     story_type=story_type_map[story_type]
                 )
-                
+
                 # Display the story
                 if "text" in story and "title" in story:
                     st.title(story["title"])
                     st.markdown(story["text"])
-                    
+
                     # Display insights
                     if "insights" in story and story["insights"]:
                         st.subheader("Key Climate Insights")
                         for insight in story["insights"]:
                             st.markdown(f"- {insight}")
-                    
+
                     # Display visualization suggestions
                     if "visualization_suggestions" in story and story["visualization_suggestions"]:
                         st.subheader("Suggested Visualizations")
                         for suggestion in story["visualization_suggestions"]:
                             st.markdown(f"- {suggestion}")
-                    
+
                     # Option to download the story
                     story_text = f"# {story['title']}\n\n{story['text']}\n\n## Key Climate Insights\n"
                     for insight in story.get("insights", []):
@@ -2979,7 +2987,7 @@ def get_city_coordinates(city_name):
                     story_text += f"\n## Suggested Visualizations\n"
                     for suggestion in story.get("visualization_suggestions", []):
                         story_text += f"- {suggestion}\n"
-                    
+
                     st.download_button(
                         label="Download Story as Text",
                         data=story_text,
@@ -2988,35 +2996,35 @@ def get_city_coordinates(city_name):
                     )
                 else:
                     st.error(f"Error generating story: {story.get('text', 'Unknown error')}")
-        
+
         except Exception as e:
             st.error(f"Error generating climate story: {str(e)}")
             st.info("This feature requires an OpenAI API key. Please check your settings and try again.")
 
 elif st.session_state.active_function == "export_anomalies":
     st.subheader("Export Climate Anomalies as a Table")
-    
+
     # Data source selection
     data_source = st.radio("Select Data Source", ["Upload CSV File", "Fetch from NASA POWER API"])
-    
+
     if data_source == "Upload CSV File":
         uploaded_file = st.file_uploader("Choose a CSV file with climate data", type="csv")
-        
+
         if uploaded_file is not None:
             try:
                 data = pd.read_csv(uploaded_file)
                 st.session_state.uploaded_data = data
-                
+
                 # Show preview of uploaded data
                 st.write("Preview of uploaded data:")
                 st.dataframe(data.head())
-                
+
                 # Check if the data has date and temperature columns
                 if st.button("Calculate Temperature Anomalies"):
                     # Get the temperature column
                     temp_column = st.selectbox("Select Temperature Column", data.columns)
                     date_column = st.selectbox("Select Date Column", data.columns)
-                    
+
                     if temp_column and date_column:
                         # Convert date column to datetime if not already
                         if data[date_column].dtype != 'datetime64[ns]':
@@ -3025,32 +3033,32 @@ def get_city_coordinates(city_name):
                             except:
                                 st.error("Could not convert the selected column to date format. Please select a valid date column.")
                                 st.stop()
-                        
+
                         # Calculate monthly averages
                         data['Month'] = data[date_column].dt.month
                         data['Year'] = data[date_column].dt.year
-                        
+
                         # Group by month and calculate the average temperature
                         monthly_avg = data.groupby('Month')[temp_column].mean().reset_index()
                         monthly_avg.columns = ['Month', 'Average_Temperature']
-                        
+
                         # Merge the monthly averages back to the original data
                         data = pd.merge(data, monthly_avg, on='Month')
-                        
+
                         # Calculate the anomalies
                         data['Temperature_Anomaly'] = data[temp_column] - data['Average_Temperature']
-                        
+
                         # Create a result dataframe
                         result = data[[date_column, temp_column, 'Average_Temperature', 'Temperature_Anomaly']]
                         result = result.rename(columns={date_column: 'Date', temp_column: 'Temperature'})
-                        
+
                         # Store the result in session state
                         st.session_state.processed_data = result
-                        
+
                         # Display the result
                         st.subheader("Temperature Anomalies")
                         st.dataframe(result)
-                        
+
                         # Download button for the anomalies table
                         csv = result.to_csv(index=False)
                         st.download_button(
@@ -3059,29 +3067,29 @@ def get_city_coordinates(city_name):
                             file_name="temperature_anomalies.csv",
                             mime="text/csv",
                         )
-                        
+
                         # Visualization of anomalies
                         st.subheader("Visualization of Temperature Anomalies")
-                        fig = px.scatter(result, x='Date', y='Temperature_Anomaly', 
+                        fig = px.scatter(result, x='Date', y='Temperature_Anomaly',
                                         color='Temperature_Anomaly',
                                         color_continuous_scale=px.colors.diverging.RdBu_r,
                                         title="Temperature Anomalies Over Time")
                         st.plotly_chart(fig)
-            
+
             except Exception as e:
                 st.error(f"Error processing file: {str(e)}")
-    
+
     else:  # NASA POWER API
         st.info("This feature will connect to the NASA POWER API to fetch climate data for a specified location and date range.")
-        
+
         # Location input method selection
         location_method = st.radio("Select location input method:", ["City Name", "Coordinates"], horizontal=True, key="nasa_location_method")
-        
+
         if location_method == "City Name":
-            city = st.text_input("Enter city name (e.g., 'New York', 'London, UK')", 
+            city = st.text_input("Enter city name (e.g., 'New York', 'London, UK')",
                                 value="San Francisco, CA" if "nasa_last_city" not in st.session_state else st.session_state.nasa_last_city,
                                 key="nasa_city")
-            
+
             if city:
                 st.session_state.nasa_last_city = city
                 lat, lon = get_city_coordinates(city)
@@ -3100,63 +3108,63 @@ def get_city_coordinates(city_name):
                 latitude = st.number_input("Latitude", value=st.session_state.user_location["lat"], min_value=-90.0, max_value=90.0, key="nasa_lat")
             with col2:
                 longitude = st.number_input("Longitude", value=st.session_state.user_location["lon"], min_value=-180.0, max_value=180.0, key="nasa_lon")
-        
+
         # Date range
         col1, col2 = st.columns(2)
         with col1:
             start_date = st.date_input("Start Date", datetime.now() - timedelta(days=365*5), key="nasa_start")
         with col2:
             end_date = st.date_input("End Date", datetime.now(), key="nasa_end")
-        
+
         if st.button("Fetch Data and Calculate Anomalies"):
             with st.spinner("Fetching data from NASA POWER API..."):
                 try:
                     # Fetch real temperature data from NASA POWER API
                     from nasa_data import fetch_nasa_power_data
-                    
+
                     # Convert dates to string format for API
                     start_date_str = start_date.strftime('%Y-%m-%d')
                     end_date_str = end_date.strftime('%Y-%m-%d')
-                    
+
                     # Status message
                     st.text(f"Fetching temperature data for {city if location_method == 'City Name' else f'({latitude:.2f}, {longitude:.2f})'} from {start_date_str} to {end_date_str}...")
-                    
+
                     # Fetch NASA POWER temperature data
                     nasa_df = fetch_nasa_power_data(
-                        latitude, 
-                        longitude, 
-                        start_date_str, 
-                        end_date_str, 
+                        latitude,
+                        longitude,
+                        start_date_str,
+                        end_date_str,
                         parameters=["T2M"]  # Temperature at 2 Meters
                     )
-                    
+
                     # Create a dataframe with the temperature data
                     data = pd.DataFrame({
                         'Date': nasa_df['Date'],
                         'Temperature': nasa_df['T2M']  # Temperature in Celsius
                     })
-                    
+
                     # Calculate monthly climatology (long-term averages)
                     data['Month'] = data['Date'].dt.month
                     monthly_avg = data.groupby('Month')['Temperature'].mean().reset_index()
                     monthly_avg.columns = ['Month', 'Average_Temperature']
-                    
+
                     # Merge the monthly averages back to the original data
                     data = pd.merge(data, monthly_avg, on='Month')
-                    
+
                     # Calculate the anomalies
                     data['Temperature_Anomaly'] = data['Temperature'] - data['Average_Temperature']
-                    
+
                     # Final result
                     result = data[['Date', 'Temperature', 'Average_Temperature', 'Temperature_Anomaly']]
-                    
+
                     # Store the result in session state
                     st.session_state.processed_data = result
-                    
+
                     # Display the result
                     st.subheader("Temperature Anomalies")
                     st.dataframe(result)
-                    
+
                     # Download button for the anomalies table
                     csv = result.to_csv(index=False)
                     st.download_button(
@@ -3165,15 +3173,15 @@ def get_city_coordinates(city_name):
                         file_name="temperature_anomalies.csv",
                         mime="text/csv",
                     )
-                    
+
                     # Visualization of anomalies
                     st.subheader("Visualization of Temperature Anomalies")
-                    fig = px.scatter(result, x='Date', y='Temperature_Anomaly', 
+                    fig = px.scatter(result, x='Date', y='Temperature_Anomaly',
                                     color='Temperature_Anomaly',
                                     color_continuous_scale=px.colors.diverging.RdBu_r,
                                     title="Temperature Anomalies Over Time")
                     st.plotly_chart(fig)
-                    
+
                 except Exception as e:
                     st.error(f"Error fetching data: {str(e)}")
 
@@ -3181,7 +3189,7 @@ def get_city_coordinates(city_name):
 if st.session_state.chat_history:
     st.markdown("### Chat History")
     chat_container = st.container()
-    
+
     with chat_container:
         # Skip the first welcome message in chat history display
         for message in st.session_state.chat_history[1:]:
@@ -3189,12 +3197,12 @@ def get_city_coordinates(city_name):
                 st.markdown(f"**You:** {message['content']}")
             else:
                 st.markdown(f"**CeCe:** {message['content']}")
-    
+
     # Add option to clear chat history
     if st.button("Clear Chat History"):
         st.session_state.chat_history = []
         st.rerun()
-    
+
     # Add option to download chat history
     if st.button("Download Chat History"):
         chat_text = ""
@@ -3202,7 +3210,7 @@ def get_city_coordinates(city_name):
         for message in st.session_state.chat_history[1:]:
             prefix = "You: " if message["role"] == "user" else "CeCe: "
             chat_text += f"{prefix}{message['content']}\n\n"
-        
+
         # Provide the chat history as a downloadable txt file
         st.download_button(
             label="Download Chat as TXT",
@@ -3214,45 +3222,45 @@ def get_city_coordinates(city_name):
 # File upload section in sidebar
 with st.sidebar:
     st.title("Data Management")
-    
+
     upload_file = st.file_uploader("Upload Climate/Weather Dataset", type=["csv", "xlsx"])
-    
+
     if upload_file is not None:
         try:
             if upload_file.name.endswith('.xlsx'):
                 data = pd.read_excel(upload_file)
             else:
                 data = pd.read_csv(upload_file)
-            
+
             st.session_state.uploaded_data = data
             st.success(f"Successfully loaded {upload_file.name}")
-            
+
             # Preview the data
             st.write("Data Preview:")
             st.dataframe(data.head())
-            
+
             # Basic data info
             st.write("Data Info:")
             buffer = io.StringIO()
             data.info(buf=buffer)
             s = buffer.getvalue()
             st.text(s)
-            
+
         except Exception as e:
             st.error(f"Error loading file: {str(e)}")
-    
+
     # Settings section
     st.subheader("Settings")
-    
+
     api_option = st.selectbox("LLM Provider", ["DeepSeek-V3", "OpenAI"])
-    
+
     # Only show API key input if not using environment variables
     if st.checkbox("Enter API Key Manually"):
         api_key = st.text_input("API Key", type="password")
-    
+
     # Units preference
     units = st.radio("Temperature Units", ["Celsius", "Fahrenheit"])
-    
+
     # Save settings button
     if st.button("Save Settings"):
         st.success("Settings saved!")
@@ -3260,15 +3268,15 @@ def get_city_coordinates(city_name):
 # Adding the missing function implementations for the preset buttons
 if st.session_state.active_function == "temperature_trends":
     st.subheader("Temperature Trends from the Past 5 Years")
-    
+
     # Location selection similar to precipitation map
     location_method = st.radio("Select location input method:", ["City Name", "Coordinates"], horizontal=True, key="temp_location_method")
-    
+
     if location_method == "City Name":
-        city = st.text_input("Enter city name (e.g., 'New York', 'London, UK')", 
+        city = st.text_input("Enter city name (e.g., 'New York', 'London, UK')",
                              value="San Francisco, CA" if "last_city" not in st.session_state else st.session_state.last_city,
                              key="temp_city_input")
-        
+
         if city:
             st.session_state.last_city = city
             lat, lon = get_city_coordinates(city)
@@ -3284,14 +3292,14 @@ def get_city_coordinates(city_name):
     else:
         col1, col2 = st.columns(2)
         with col1:
-            latitude = st.number_input("Latitude", value=st.session_state.user_location["lat"], 
+            latitude = st.number_input("Latitude", value=st.session_state.user_location["lat"],
                                       min_value=-90.0, max_value=90.0, key="temp_lat_input")
         with col2:
-            longitude = st.number_input("Longitude", value=st.session_state.user_location["lon"], 
+            longitude = st.number_input("Longitude", value=st.session_state.user_location["lon"],
                                        min_value=-180.0, max_value=180.0, key="temp_lon_input")
-        
+
         st.session_state.user_location = {"lat": latitude, "lon": longitude}
-    
+
     # Date range selection
     col1, col2 = st.columns(2)
     with col1:
@@ -3299,41 +3307,41 @@ def get_city_coordinates(city_name):
     with col2:
         # Calculate start date as 5 years ago from end date
         start_date = st.date_input("Start Date (5 years ago)", end_date - timedelta(days=5*365), key="temp_start_date")
-    
+
     if st.button("Generate Temperature Trends"):
         with st.spinner("Generating temperature trends for the past 5 years..."):
             try:
                 # Fetch real temperature data from NASA POWER API
                 from nasa_data import get_temperature_trends
-                
+
                 # Convert dates to string format for API
                 start_date_str = start_date.strftime('%Y-%m-%d')
                 end_date_str = end_date.strftime('%Y-%m-%d')
-                
+
                 # Status message
                 st.text(f"Fetching temperature trends data for {city if location_method == 'City Name' else f'({latitude:.2f}, {longitude:.2f})'} from {start_date_str} to {end_date_str}...")
-                
+
                 # Get temperature trends from NASA POWER API
                 df, trend_per_decade = get_temperature_trends(latitude, longitude, start_date_str, end_date_str)
-                
+
                 # Calculate a 12-month moving average
                 df['12-Month Moving Avg'] = df['Temperature (°C)'].rolling(window=12).mean()
-                
+
                 # Calculate the trend line using linear regression
                 from scipy import stats
                 x = np.arange(len(df))
                 slope, intercept, r_value, p_value, std_err = stats.linregress(x, df['Temperature (°C)'])
                 df['Trend'] = intercept + slope * x
-                
+
                 # Create a Plotly visualization
                 fig = px.line(df, x='Date', y='Temperature (°C)', title=f'Monthly Temperature Trends for {city if location_method == "City Name" else f"({latitude:.2f}, {longitude:.2f})"}')
-                
+
                 # Add the moving average line
                 fig.add_scatter(x=df['Date'], y=df['12-Month Moving Avg'], mode='lines', name='12-Month Moving Average', line=dict(color='red'))
-                
+
                 # Add the trend line
                 fig.add_scatter(x=df['Date'], y=df['Trend'], mode='lines', name='Long-term Trend', line=dict(color='green', dash='dash'))
-                
+
                 # Customize the layout
                 fig.update_layout(
                     xaxis_title='Date',
@@ -3344,27 +3352,27 @@ def get_city_coordinates(city_name):
                     paper_bgcolor='rgba(0,0,0,0)',
                     font=dict(color='white')
                 )
-                
+
                 # Display the chart
                 st.plotly_chart(fig, use_container_width=True)
-                
+
                 # Display key insights
                 trend_per_decade = slope * 120  # 120 months in a decade
                 st.subheader("Key Insights")
-                
+
                 insights_col1, insights_col2 = st.columns(2)
                 with insights_col1:
                     st.metric("Average Temperature", f"{df['Temperature (°C)'].mean():.1f}°C", delta=None)
                     st.metric("Temperature Range", f"{df['Temperature (°C)'].min():.1f}°C to {df['Temperature (°C)'].max():.1f}°C", delta=None)
-                
+
                 with insights_col2:
-                    st.metric("Trend per Decade", f"{trend_per_decade:.2f}°C", 
+                    st.metric("Trend per Decade", f"{trend_per_decade:.2f}°C",
                              delta="warming" if trend_per_decade > 0 else "cooling")
                     st.metric("Seasonal Variation", f"{df['Temperature (°C)'].std():.1f}°C", delta=None)
-                
+
                 # Add context about the data
                 st.info(f"This chart shows simulated monthly temperature data for your selected location. The trend line indicates an overall temperature change of approximately {trend_per_decade:.2f}°C per decade. In a real implementation, this would use actual climate data from NASA POWER API or similar sources.")
-                
+
                 # Option to download the data
                 csv_data = df.to_csv(index=False).encode('utf-8')
                 st.download_button(
@@ -3373,21 +3381,21 @@ def get_city_coordinates(city_name):
                     file_name="temperature_trends.csv",
                     mime="text/csv"
                 )
-                
+
             except Exception as e:
                 st.error(f"Error generating temperature trends: {str(e)}")
 
 if st.session_state.active_function == "extreme_heat":
     st.subheader("Identify Extreme Heat Days in Your Area")
-    
+
     # Location selection
     location_method = st.radio("Select location input method:", ["City Name", "Coordinates"], horizontal=True, key="heat_location_method")
-    
+
     if location_method == "City Name":
-        city = st.text_input("Enter city name (e.g., 'New York', 'London, UK')", 
+        city = st.text_input("Enter city name (e.g., 'New York', 'London, UK')",
                              value="Phoenix, AZ" if "last_city" not in st.session_state else st.session_state.last_city,
                              key="heat_city_input")
-        
+
         if city:
             st.session_state.last_city = city
             lat, lon = get_city_coordinates(city)
@@ -3403,78 +3411,78 @@ def get_city_coordinates(city_name):
     else:
         col1, col2 = st.columns(2)
         with col1:
-            latitude = st.number_input("Latitude", value=st.session_state.user_location["lat"], 
+            latitude = st.number_input("Latitude", value=st.session_state.user_location["lat"],
                                       min_value=-90.0, max_value=90.0, key="heat_lat_input")
         with col2:
-            longitude = st.number_input("Longitude", value=st.session_state.user_location["lon"], 
+            longitude = st.number_input("Longitude", value=st.session_state.user_location["lon"],
                                        min_value=-180.0, max_value=180.0, key="heat_lon_input")
-        
+
         st.session_state.user_location = {"lat": latitude, "lon": longitude}
-    
+
     # Parameters for extreme heat identification
     col1, col2 = st.columns(2)
     with col1:
         year = st.selectbox("Select Year", list(range(datetime.now().year, datetime.now().year - 10, -1)), key="heat_year")
     with col2:
         percentile = st.slider("Extreme Heat Threshold (Percentile)", min_value=90, max_value=99, value=95, key="heat_percentile")
-    
+
     # Options for analysis
     analysis_type = st.radio("Analysis Type", ["By Maximum Temperature", "By Heat Index (Temperature + Humidity)"], key="heat_analysis_type")
-    
+
     if st.button("Identify Extreme Heat Days"):
         with st.spinner("Analyzing temperature data to identify extreme heat days..."):
             try:
                 # Fetch real temperature and heat data from NASA POWER API
                 from nasa_data import get_extreme_heat_days
-                
+
                 # Convert year to date format
                 start_date_str = f"{year}-01-01"
                 end_date_str = f"{year}-12-31"
-                
+
                 # Status message
                 st.text(f"Fetching temperature data for {city if location_method == 'City Name' else f'({latitude:.2f}, {longitude:.2f})'} for year {year}...")
-                
+
                 # Get extreme heat days from NASA POWER API
                 df, temp_threshold, hi_threshold = get_extreme_heat_days(latitude, longitude, year, percentile)
-                
+
                 # Select which value to analyze based on user selection
                 if analysis_type == "By Heat Index (Temperature + Humidity)":
                     # Determine extreme heat days based on heat index
                     df['Is Extreme Heat'] = df['Heat Index (°C)'] > hi_threshold
-                    
+
                     # Value to analyze
                     analysis_value = 'Heat Index (°C)'
                     threshold = hi_threshold
-                    
+
                 else:  # By Maximum Temperature
                     # Determine extreme heat days based on temperature
                     df['Is Extreme Heat'] = df['T2M_MAX'] > temp_threshold
-                    
+
                     # Value to analyze
                     analysis_value = 'T2M_MAX'
                     threshold = temp_threshold
-                    
+
                 # Rename temperature column for display
                 df = df.rename(columns={'T2M_MAX': 'Temperature (°C)'})
-                
+
                 # Filter to extreme heat days
                 extreme_days = df[df['Is Extreme Heat']].copy()
-                
+
                 # Display the results
                 st.subheader(f"Extreme Heat Days in {year} (Above {percentile}th Percentile)")
-                
+
                 # Show the threshold
                 st.info(f"Threshold value: {threshold:.1f}°C {analysis_value.split(' ')[0]}")
-                
+
                 # Create calendar heatmap
-                fig = px.scatter(df, x=df['Date'].dt.month, y=df['Date'].dt.day, 
+                fig = px.scatter(df, x=df['Date'].dt.month, y=df['Date'].dt.day,
                                color=df[analysis_value],
                                color_continuous_scale='Turbo',  # Red-hot color scale
                                title=f"Heat Calendar for {year}",
                                labels={'x': 'Month', 'y': 'Day', 'color': analysis_value},
                                size_max=10,
                                height=400)
-                
+
                 # Add custom shapes for extreme heat days
                 for idx, row in extreme_days.iterrows():
                     month = row['Date'].month
@@ -3486,7 +3494,7 @@ def get_city_coordinates(city_name):
                         line=dict(color="red", width=2),
                         fillcolor="rgba(255,0,0,0)"
                     )
-                
+
                 # Customize the layout
                 fig.update_layout(
                     xaxis=dict(
@@ -3502,12 +3510,12 @@ def get_city_coordinates(city_name):
                     paper_bgcolor='rgba(0,0,0,0)',
                     font=dict(color='white')
                 )
-                
+
                 st.plotly_chart(fig, use_container_width=True)
-                
+
                 # Display statistics
                 st.subheader("Heat Statistics")
-                
+
                 col1, col2, col3 = st.columns(3)
                 with col1:
                     st.metric("Total Extreme Heat Days", f"{len(extreme_days)}", delta=None)
@@ -3518,7 +3526,7 @@ def get_city_coordinates(city_name):
                     max_extreme = extreme_days[analysis_value].max()
                     max_date = extreme_days.loc[extreme_days[analysis_value].idxmax(), 'Date'].strftime('%b %d')
                     st.metric("Maximum " + analysis_value, f"{max_extreme:.1f}°C on {max_date}", delta=None)
-                
+
                 # Display extreme days data table
                 st.subheader("List of Extreme Heat Days")
                 # Format the table for display
@@ -3527,13 +3535,13 @@ def get_city_coordinates(city_name):
                 # Round numeric columns to 1 decimal place
                 numeric_cols = display_df.select_dtypes(include=[np.number]).columns
                 display_df[numeric_cols] = display_df[numeric_cols].round(1)
-                
+
                 # Sort by the analysis value in descending order
                 display_df = display_df.sort_values(by=analysis_value, ascending=False)
-                
+
                 # Show the table
                 st.dataframe(display_df)
-                
+
                 # Option to download the data
                 csv_data = display_df.to_csv(index=False).encode('utf-8')
                 st.download_button(
@@ -3542,24 +3550,24 @@ def get_city_coordinates(city_name):
                     file_name="extreme_heat_days.csv",
                     mime="text/csv"
                 )
-                
+
                 # Add context about the data
                 st.info(f"This analysis shows simulated extreme heat days for your selected location. In a real implementation, this would use actual climate data from NASA POWER API or similar sources, and could include more sophisticated heat metrics like wet-bulb temperature for heat stress analysis.")
-                
+
             except Exception as e:
                 st.error(f"Error identifying extreme heat days: {str(e)}")
 
 if st.session_state.active_function == "rainfall_comparison":
     st.subheader("Compare Rainfall: This Season vs. Last Year")
-    
+
     # Location selection
     location_method = st.radio("Select location input method:", ["City Name", "Coordinates"], horizontal=True, key="rain_location_method")
-    
+
     if location_method == "City Name":
-        city = st.text_input("Enter city name (e.g., 'New York', 'London, UK')", 
+        city = st.text_input("Enter city name (e.g., 'New York', 'London, UK')",
                              value="Seattle, WA" if "last_city" not in st.session_state else st.session_state.last_city,
                              key="rain_city_input")
-        
+
         if city:
             st.session_state.last_city = city
             lat, lon = get_city_coordinates(city)
@@ -3575,14 +3583,14 @@ def get_city_coordinates(city_name):
     else:
         col1, col2 = st.columns(2)
         with col1:
-            latitude = st.number_input("Latitude", value=st.session_state.user_location["lat"], 
+            latitude = st.number_input("Latitude", value=st.session_state.user_location["lat"],
                                       min_value=-90.0, max_value=90.0, key="rain_lat_input")
         with col2:
-            longitude = st.number_input("Longitude", value=st.session_state.user_location["lon"], 
+            longitude = st.number_input("Longitude", value=st.session_state.user_location["lon"],
                                        min_value=-180.0, max_value=180.0, key="rain_lon_input")
-        
+
         st.session_state.user_location = {"lat": latitude, "lon": longitude}
-    
+
     # Define seasons
     current_month = datetime.now().month
     # Approximate seasons (adjust as needed for your application)
@@ -3593,21 +3601,21 @@ def get_city_coordinates(city_name):
         9: "Fall", 10: "Fall", 11: "Fall"
     }
     current_season = season_mapping[current_month]
-    
+
     # Allow user to select a different season
-    season = st.selectbox("Select Season to Compare", ["Winter", "Spring", "Summer", "Fall"], 
+    season = st.selectbox("Select Season to Compare", ["Winter", "Spring", "Summer", "Fall"],
                          index=list(["Winter", "Spring", "Summer", "Fall"]).index(current_season),
                          key="rain_season")
-    
+
     # Define the current year and last year
     current_year = datetime.now().year
-    
+
     if st.button("Compare Rainfall"):
         with st.spinner("Comparing rainfall data between seasons..."):
             try:
                 # Fetch real precipitation data from NASA POWER API
                 from nasa_data import get_rainfall_comparison
-                
+
                 # Define season date ranges for current and previous year
                 season_dates = {
                     "Winter": {"start_month": 12, "start_day": 1, "end_month": 2, "end_day": 28},
@@ -3615,7 +3623,7 @@ def get_city_coordinates(city_name):
                     "Summer": {"start_month": 6, "start_day": 1, "end_month": 8, "end_day": 31},
                     "Fall": {"start_month": 9, "start_day": 1, "end_month": 11, "end_day": 30}
                 }
-                
+
                 # Handle the case where winter spans across years
                 if season == "Winter":
                     # Current winter
@@ -3626,7 +3634,7 @@ def get_city_coordinates(city_name):
                         else:  # Jan or Feb
                             current_start = datetime(current_year - 1, 12, 1)
                             current_end = datetime(current_year, 2, 28)
-                        
+
                         # Previous winter
                         prev_start = datetime(current_year - 1, 12, 1)
                         prev_end = datetime(current_year, 2, 28)
@@ -3642,7 +3650,7 @@ def get_city_coordinates(city_name):
                     start_day = season_dates[season]["start_day"]
                     end_month = season_dates[season]["end_month"]
                     end_day = season_dates[season]["end_day"]
-                    
+
                     # Check if the selected season has already passed in the current year
                     if current_month > end_month:
                         # Season has passed, so "current" is this year
@@ -3662,50 +3670,50 @@ def get_city_coordinates(city_name):
                         current_end = datetime.now()
                         prev_start = datetime(current_year - 1, start_month, start_day)
                         prev_end = datetime(current_year - 1, end_month, end_day)
-                
+
                 # Convert dates to string format for API
                 current_start_str = current_start.strftime('%Y-%m-%d')
                 current_end_str = current_end.strftime('%Y-%m-%d')
                 prev_start_str = prev_start.strftime('%Y-%m-%d')
                 prev_end_str = prev_end.strftime('%Y-%m-%d')
-                
+
                 # Status message
                 st.text(f"Fetching precipitation data for {city if location_method == 'City Name' else f'({latitude:.2f}, {longitude:.2f})'} for {season} season...")
-                
+
                 # Get rainfall comparison from NASA POWER API
                 current_df, prev_df = get_rainfall_comparison(
-                    latitude, 
-                    longitude, 
-                    current_start_str, 
-                    current_end_str, 
-                    prev_start_str, 
+                    latitude,
+                    longitude,
+                    current_start_str,
+                    current_end_str,
+                    prev_start_str,
                     prev_end_str
                 )
-                
+
                 # Combine the data
                 combined_df = pd.concat([current_df, prev_df])
-                
+
                 # Calculate cumulative precipitation
                 current_cumulative = current_df['Precipitation (mm)'].cumsum()
                 prev_cumulative = prev_df['Precipitation (mm)'].cumsum()
-                
+
                 # Calculate statistics
                 current_total = current_df['Precipitation (mm)'].sum()
                 prev_total = prev_df['Precipitation (mm)'].sum()
                 current_days_with_rain = len(current_df[current_df['Precipitation (mm)'] > 0])
                 prev_days_with_rain = len(prev_df[prev_df['Precipitation (mm)'] > 0])
-                
+
                 # Normalize the dates to display them on the same x-axis (days from start of season)
                 current_df['Day of Season'] = range(len(current_df))
                 prev_df['Day of Season'] = range(len(prev_df))
-                
+
                 # Create the comparison plots
-                
+
                 # 1. Daily precipitation comparison
                 fig1 = px.bar(combined_df, x='Date', y='Precipitation (mm)', color='Year',
                              barmode='group', title=f"{season} Daily Precipitation Comparison",
                              color_discrete_map={'This Year': '#1E90FF', 'Last Year': '#9370DB'})
-                
+
                 fig1.update_layout(
                     xaxis_title='Date',
                     yaxis_title='Precipitation (mm)',
@@ -3715,12 +3723,12 @@ def get_city_coordinates(city_name):
                     paper_bgcolor='rgba(0,0,0,0)',
                     font=dict(color='white')
                 )
-                
+
                 st.plotly_chart(fig1, use_container_width=True)
-                
+
                 # 2. Cumulative precipitation comparison
                 fig2 = go.Figure()
-                
+
                 # Add current year line
                 fig2.add_trace(go.Scatter(
                     x=current_df['Day of Season'],
@@ -3729,7 +3737,7 @@ def get_city_coordinates(city_name):
                     name='This Year',
                     line=dict(color='#1E90FF', width=3)
                 ))
-                
+
                 # Add previous year line
                 fig2.add_trace(go.Scatter(
                     x=prev_df['Day of Season'],
@@ -3738,7 +3746,7 @@ def get_city_coordinates(city_name):
                     name='Last Year',
                     line=dict(color='#9370DB', width=3)
                 ))
-                
+
                 # Update layout
                 fig2.update_layout(
                     title=f"{season} Cumulative Precipitation Comparison",
@@ -3750,37 +3758,37 @@ def get_city_coordinates(city_name):
                     paper_bgcolor='rgba(0,0,0,0)',
                     font=dict(color='white')
                 )
-                
+
                 st.plotly_chart(fig2, use_container_width=True)
-                
+
                 # Display statistics
                 st.subheader("Rainfall Statistics")
-                
+
                 col1, col2, col3 = st.columns(3)
-                
+
                 with col1:
                     percent_change = ((current_total - prev_total) / prev_total * 100) if prev_total > 0 else 0
-                    st.metric("Total Precipitation", 
-                             f"{current_total:.1f} mm", 
+                    st.metric("Total Precipitation",
+                             f"{current_total:.1f} mm",
                              delta=f"{percent_change:.1f}% vs last year")
-                
+
                 with col2:
-                    st.metric("Days with Rain", 
-                             f"{current_days_with_rain}", 
+                    st.metric("Days with Rain",
+                             f"{current_days_with_rain}",
                              delta=f"{current_days_with_rain - prev_days_with_rain} days vs last year")
-                
+
                 with col3:
                     current_avg = current_total / max(1, len(current_df))
                     prev_avg = prev_total / max(1, len(prev_df))
                     avg_percent_change = ((current_avg - prev_avg) / prev_avg * 100) if prev_avg > 0 else 0
-                    st.metric("Avg. Daily Precipitation", 
-                             f"{current_avg:.1f} mm", 
+                    st.metric("Avg. Daily Precipitation",
+                             f"{current_avg:.1f} mm",
                              delta=f"{avg_percent_change:.1f}% vs last year")
-                
+
                 # Context about the data
                 comparison_result = "wetter" if current_total > prev_total else "drier"
                 st.info(f"Based on NASA POWER climate data, the {season.lower()} season this year is {comparison_result} than last year. This analysis uses real precipitation data for your selected location.")
-                
+
                 # Option to download the data
                 csv_data = combined_df.to_csv(index=False).encode('utf-8')
                 st.download_button(
@@ -3789,21 +3797,21 @@ def get_city_coordinates(city_name):
                     file_name="rainfall_comparison.csv",
                     mime="text/csv"
                 )
-                
+
             except Exception as e:
                 st.error(f"Error comparing rainfall data: {str(e)}")
 
 if st.session_state.active_function == "export_anomalies":
     st.subheader("Export Climate Anomalies as a Table")
-    
+
     # Location selection
     location_method = st.radio("Select location input method:", ["City Name", "Coordinates"], horizontal=True, key="anomaly_location_method")
-    
+
     if location_method == "City Name":
-        city = st.text_input("Enter city name (e.g., 'New York', 'London, UK')", 
+        city = st.text_input("Enter city name (e.g., 'New York', 'London, UK')",
                              value="Boulder, CO" if "last_city" not in st.session_state else st.session_state.last_city,
                              key="anomaly_city_input")
-        
+
         if city:
             st.session_state.last_city = city
             lat, lon = get_city_coordinates(city)
@@ -3819,24 +3827,24 @@ def get_city_coordinates(city_name):
     else:
         col1, col2 = st.columns(2)
         with col1:
-            latitude = st.number_input("Latitude", value=st.session_state.user_location["lat"], 
+            latitude = st.number_input("Latitude", value=st.session_state.user_location["lat"],
                                       min_value=-90.0, max_value=90.0, key="anomaly_lat_input")
         with col2:
-            longitude = st.number_input("Longitude", value=st.session_state.user_location["lon"], 
+            longitude = st.number_input("Longitude", value=st.session_state.user_location["lon"],
                                        min_value=-180.0, max_value=180.0, key="anomaly_lon_input")
-        
+
         st.session_state.user_location = {"lat": latitude, "lon": longitude}
-    
+
     # Date range selection
     col1, col2, col3 = st.columns(3)
     with col1:
-        selected_period = st.selectbox("Time Period", 
+        selected_period = st.selectbox("Time Period",
                                      ["Last 5 Years", "Last 10 Years", "Last 30 Years", "Custom Range"],
                                      key="anomaly_period")
-    
+
     # Set the date range based on the selected period
     end_date = datetime.now()
-    
+
     if selected_period == "Last 5 Years":
         start_date = datetime(end_date.year - 5, 1, 1)
     elif selected_period == "Last 10 Years":
@@ -3845,38 +3853,38 @@ def get_city_coordinates(city_name):
         start_date = datetime(end_date.year - 30, 1, 1)
     else:  # Custom Range
         with col2:
-            start_date = st.date_input("Start Date", 
+            start_date = st.date_input("Start Date",
                                       datetime(end_date.year - 10, 1, 1),
                                       key="anomaly_start_date")
         with col3:
-            end_date = st.date_input("End Date", 
+            end_date = st.date_input("End Date",
                                     end_date,
                                     key="anomaly_end_date")
-    
+
     # Variable selection
-    variable = st.selectbox("Climate Variable", 
+    variable = st.selectbox("Climate Variable",
                           ["Temperature", "Precipitation", "Humidity", "Wind Speed"],
                           key="anomaly_variable")
-    
+
     # Baseline selection
-    baseline_period = st.selectbox("Baseline Period", 
+    baseline_period = st.selectbox("Baseline Period",
                                  ["1951-1980", "1971-2000", "1981-2010", "1991-2020"],
                                  key="anomaly_baseline")
-    
+
     # Calculate button
     if st.button("Calculate and Export Anomalies"):
         with st.spinner("Calculating climate anomalies..."):
             try:
                 # Fetch real climate data from NASA POWER API
                 from nasa_data import calculate_climate_anomalies
-                
+
                 # Convert dates to string format for API
-                start_date_str = start_date.strftime('%Y-%m-%d') 
+                start_date_str = start_date.strftime('%Y-%m-%d')
                 end_date_str = end_date.strftime('%Y-%m-%d')
-                
+
                 # Status message
                 st.text(f"Fetching climate data for {city if location_method == 'City Name' else f'({latitude:.2f}, {longitude:.2f})'} for {variable.lower()}...")
-                
+
                 # Get climate anomalies data from NASA POWER API
                 df = calculate_climate_anomalies(
                     latitude,
@@ -3886,23 +3894,23 @@ def get_city_coordinates(city_name):
                     variable.lower(),
                     baseline_period
                 )
-                
+
                 # Display the anomalies table
                 st.subheader(f"{variable} Anomalies Relative to {baseline_period}")
-                
+
                 # Format the dataframe for display
                 display_df = df.copy()
                 display_df['Date'] = display_df['Date'].dt.strftime('%b %Y')
-                
+
                 # Round values for display
                 value_cols = [col for col in display_df.columns if col not in ['Date', 'Year', 'Month', 'Anomaly', 'Anomaly Unit']]
                 if len(value_cols) > 0:
                     for col in value_cols:
                         display_df[col] = display_df[col].round(1)
-                
+
                 # Round anomaly values
                 display_df['Anomaly'] = display_df['Anomaly'].round(1)
-                
+
                 # Create a color-coded dataframe for the anomalies
                 st.dataframe(
                     display_df,
@@ -3915,17 +3923,17 @@ def get_city_coordinates(city_name):
                     },
                     hide_index=True
                 )
-                
+
                 # Create visualization of anomalies
                 if variable == "Temperature":
                     # Color mapping for temperature anomalies
-                    colors = ['blue' if a < -1 else 'lightblue' if a < 0 
+                    colors = ['blue' if a < -1 else 'lightblue' if a < 0
                              else 'salmon' if a < 1 else 'red' for a in df['Anomaly']]
-                    
+
                     # Create plot
                     fig = px.bar(
-                        df, 
-                        x='Date', 
+                        df,
+                        x='Date',
                         y='Anomaly',
                         title=f"Monthly Temperature Anomalies Relative to {baseline_period}",
                         labels={'Anomaly': 'Temperature Anomaly (°C)', 'Date': ''},
@@ -3935,15 +3943,15 @@ def get_city_coordinates(city_name):
                 else:
                     # For non-temperature variables, use a diverging color scale centered at 0
                     fig = px.bar(
-                        df, 
-                        x='Date', 
+                        df,
+                        x='Date',
                         y='Anomaly',
                         title=f"Monthly {variable} Anomalies Relative to {baseline_period}",
                         labels={'Anomaly': f'{variable} Anomaly (%)', 'Date': ''},
                         color='Anomaly',
                         color_continuous_scale='RdBu'
                     )
-                
+
                 # Layout customization
                 fig.update_layout(
                     xaxis=dict(
@@ -3956,60 +3964,60 @@ def get_city_coordinates(city_name):
                     paper_bgcolor='rgba(0,0,0,0)',
                     font=dict(color='white')
                 )
-                
+
                 st.plotly_chart(fig, use_container_width=True)
-                
+
                 # Calculate some statistics about the anomalies
                 mean_anomaly = df['Anomaly'].mean()
                 trend_per_decade = df['Anomaly'].iloc[-60:].mean() - df['Anomaly'].iloc[:60].mean() if len(df) > 120 else np.nan
-                
+
                 # Display statistics
                 st.subheader("Anomaly Statistics")
-                
+
                 col1, col2, col3 = st.columns(3)
                 with col1:
                     if variable == "Temperature":
-                        st.metric("Mean Anomaly", f"{mean_anomaly:.2f} °C", 
+                        st.metric("Mean Anomaly", f"{mean_anomaly:.2f} °C",
                                  delta=f"{mean_anomaly:.2f} °C" if mean_anomaly != 0 else None)
                     else:
-                        st.metric("Mean Anomaly", f"{mean_anomaly:.1f}%", 
+                        st.metric("Mean Anomaly", f"{mean_anomaly:.1f}%",
                                  delta=f"{mean_anomaly:.1f}%" if mean_anomaly != 0 else None)
-                
+
                 with col2:
                     if not np.isnan(trend_per_decade):
                         if variable == "Temperature":
-                            st.metric("Recent Trend (per decade)", f"{trend_per_decade:.2f} °C", 
+                            st.metric("Recent Trend (per decade)", f"{trend_per_decade:.2f} °C",
                                      delta=f"{trend_per_decade:.2f} °C" if trend_per_decade != 0 else None)
                         else:
-                            st.metric("Recent Trend (per decade)", f"{trend_per_decade:.1f}%", 
+                            st.metric("Recent Trend (per decade)", f"{trend_per_decade:.1f}%",
                                      delta=f"{trend_per_decade:.1f}%" if trend_per_decade != 0 else None)
-                
+
                 with col3:
                     extreme_anomalies = len(df[abs(df['Anomaly']) > (2 if variable == "Temperature" else 50)])
-                    st.metric("Extreme Anomalies", f"{extreme_anomalies}", 
+                    st.metric("Extreme Anomalies", f"{extreme_anomalies}",
                              delta=None)
-                
+
                 # Allow downloading the data
                 csv = df.to_csv(index=False).encode('utf-8')
-                
+
                 st.download_button(
                     label=f"Download {variable} Anomalies as CSV",
                     data=csv,
                     file_name=f"{variable.lower()}_anomalies_{baseline_period}.csv",
                     mime="text/csv"
                 )
-                
+
                 # Add Excel export option
                 excel_buffer = io.BytesIO()
                 with pd.ExcelWriter(excel_buffer, engine='xlsxwriter') as writer:
                     df.to_excel(writer, sheet_name='Anomalies', index=False)
-                    
+
                     # Add a summary sheet
                     summary_data = {
-                        'Statistic': ['Mean Anomaly', 'Trend per Decade', 'Extreme Anomalies', 
+                        'Statistic': ['Mean Anomaly', 'Trend per Decade', 'Extreme Anomalies',
                                      'Baseline Period', 'Data Period'],
                         'Value': [
-                            f"{mean_anomaly:.2f} {display_df['Anomaly Unit'].iloc[0]}", 
+                            f"{mean_anomaly:.2f} {display_df['Anomaly Unit'].iloc[0]}",
                             f"{trend_per_decade:.2f} {display_df['Anomaly Unit'].iloc[0]} per decade" if not np.isnan(trend_per_decade) else "N/A",
                             str(extreme_anomalies),
                             baseline_period,
@@ -4017,18 +4025,18 @@ def get_city_coordinates(city_name):
                         ]
                     }
                     pd.DataFrame(summary_data).to_excel(writer, sheet_name='Summary', index=False)
-                
+
                 excel_data = excel_buffer.getvalue()
-                
+
                 st.download_button(
                     label=f"Download {variable} Anomalies as Excel",
                     data=excel_data,
                     file_name=f"{variable.lower()}_anomalies_{baseline_period}.xlsx",
                     mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
                 )
-                
+
                 # Add context about the data
                 st.info(f"This chart shows {variable.lower()} anomalies for your selected location relative to a {baseline_period} baseline using NASA POWER climate data. Positive anomalies indicate values above the baseline, while negative anomalies indicate values below the baseline.")
-                
+
             except Exception as e:
                 st.error(f"Error calculating climate anomalies: {str(e)}")