Update app.py

app.py

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+import streamlit as st
+import google.generativeai as genai
+from geopy.geocoders import Nominatim
+# from geopy.exc import GeocoderTimedOut, GeocoderUnavailable # Not explicitly caught, requests.timeout handles
+import folium
+from streamlit_folium import st_folium
+import pandas as pd
+import requests
+import re
+import os
+from datetime import datetime, timedelta
+
+# --- Page Configuration ---
+st.set_page_config(
+    layout="wide",
+    page_title="Landslide Factor Explorer | India", # More professional title
+    page_icon="🏔️", # Favicon
+    initial_sidebar_state="collapsed"
+)
+# Custom CSS for enhanced UI
+st.markdown("""
+<style>
+    /* Main styling */
+    .main .block-container {
+        padding-top: 1rem; /* Reduced top padding */
+        padding-bottom: 2rem;
+        padding-left: 2rem; /* Added horizontal padding */
+        padding-right: 2rem; /* Added horizontal padding */
+    }
+
+    /* Header styling */
+    h1, h2, h3, h4, h5 {
+        font-family: 'Roboto', 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+        color: #2c3e50; /* Dark blue-gray for headers */
+    }
+    h1 {
+        color: #1f618d; /* Slightly different color for main title if needed */
+    }
+
+    /* Card-like containers */
+    .card {
+        background-color: #FFFFFF;
+        border-radius: 12px; /* Softer radius */
+        padding: 20px;
+        box-shadow: 0 6px 12px rgba(0, 0, 0, 0.08); /* Softer shadow */
+        margin-bottom: 20px;
+        border: 1px solid #e0e0e0; /* Light border */
+    }
+     .data-card { /* Specific card for data sections */
+        background-color: #f9f9f9; /* Slightly off-white */
+        border-radius: 10px;
+        padding: 15px;
+        box-shadow: 0 4px 8px rgba(0, 0, 0, 0.05);
+        margin-bottom: 15px;
+        border-left: 5px solid #3498db; /* Accent color */
+    }
+
+
+    /* For metric containers */
+    .metric-container {
+        background-color: #f8f9fa; /* Lighter background */
+        border-radius: 8px;
+        padding: 15px;
+        border-left: 4px solid #1abc9c; /* Green accent */
+        margin-bottom: 10px;
+        text-align: center;
+    }
+    .stMetric { /* Target Streamlit's metric component */
+        background-color: #ffffff;
+        border-radius: 8px;
+        padding: 15px 20px;
+        box-shadow: 0 2px 4px rgba(0,0,0,0.05);
+        border: 1px solid #eee;
+    }
+    .stMetric > label { /* Metric label */
+        font-weight: 500 !important;
+        color: #555 !important;
+    }
+     .stMetric > div:nth-child(2) > div { /* Metric value */
+        font-size: 1.6em !important;
+        font-weight: 600 !important;
+        color: #2c3e50 !important;
+    }
+
+
+    /* For maps */
+    .map-container {
+        border-radius: 12px;
+        overflow: hidden;
+        border: 1px solid #ddd;
+        box-shadow: 0 4px 8px rgba(0,0,0,0.05);
+    }
+
+    /* KPI badges (using Streamlit's delta color logic more directly) */
+    /* .stMetric [data-testid="stMetricDelta"] { ... } if specific styling is needed */
+
+
+    /* Data visualization enhancements */
+    .data-viz { /* For charts */
+        border-radius: 8px;
+        overflow: hidden;
+        border: 1px solid #eaeaea;
+        padding: 10px;
+        background-color: #fff;
+    }
+
+    /* Dividers */
+    hr {
+        margin: 30px 0;
+        border: 0;
+        height: 1px;
+        background-image: linear-gradient(to right, rgba(0, 0, 0, 0), rgba(44, 62, 80, 0.2), rgba(0, 0, 0, 0));
+    }
+
+    /* Button enhancements */
+    .stButton>button {
+        border-radius: 25px;
+        font-weight: 600;
+        padding: 10px 20px;
+        transition: all 0.2s ease-in-out;
+        border: 1px solid #3498db; /* Primary color border */
+        background-color: #3498db; /* Primary color */
+        color: white;
+    }
+    .stButton>button:hover {
+        transform: translateY(-2px);
+        box-shadow: 0 5px 10px rgba(52, 152, 219, 0.3);
+        background-color: #2980b9; /* Darker shade on hover */
+        border-color: #2980b9;
+    }
+     .stButton>button[kind="secondary"] { /* For reset button */
+        background-color: #e74c3c;
+        border-color: #e74c3c;
+    }
+    .stButton>button[kind="secondary"]:hover {
+        background-color: #c0392b;
+        border-color: #c0392b;
+        box-shadow: 0 5px 10px rgba(231, 76, 60, 0.3);
+    }
+
+
+    /* Tab styling */
+    .stTabs [data-baseweb="tab-list"] {
+        gap: 10px; /* Increased gap */
+        border-bottom: 2px solid #ddd; /* Underline for tab list */
+    }
+    .stTabs [data-baseweb="tab"] {
+        border-radius: 6px 6px 0px 0px;
+        padding: 12px 18px; /* More padding */
+        font-weight: 600; /* Bolder */
+        background-color: #f0f2f6; /* Light background for inactive tabs */
+        color: #555;
+        transition: background-color 0.2s, color 0.2s;
+    }
+    .stTabs [data-baseweb="tab--selected"] {
+        background-color: #3498db; /* Primary color for selected tab */
+        color: white;
+        border-bottom: 2px solid #3498db; /* Ensure it aligns with tab list border */
+    }
+
+    /* Primary header styling */
+    .main-header {
+        background: white;
+        color: #000080; /* Navy Blue - from original, kept for consistency */
+        padding: 15px 25px;
+        border-radius: 12px;
+        margin-bottom: 25px;
+        text-align: center;
+        box-shadow: 0 4px 10px rgba(0,0,0,0.1);
+    }
+    .main-header h1 {
+        margin: 0;
+        font-size: 2.2em;
+        font-weight: 700;
+        color: #2c3e50; /* Overriding the general h1 for this specific header */
+         text-shadow: 1px 1px 2px rgba(0,0,0,0.1);
+    }
+
+    /* Warning box styling */
+    .warning-box {
+        background-color: #fff9e6; /* Lighter yellow */
+        border-left: 6px solid #ffc107;
+        color: #856404;
+        padding: 20px;
+        border-radius: 8px;
+        margin: 20px 0;
+        box-shadow: 0 2px 4px rgba(0,0,0,0.05);
+    }
+    .warning-box h3 {
+        margin-top: 0;
+        color: #856404; /* Match text color */
+        font-weight: 600;
+    }
+    .warning-box ul {
+        padding-left: 20px;
+        margin-bottom: 0;
+    }
+
+    /* KPI metrics overall container styling */
+    .kpi-grid {
+        display: grid;
+        grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
+        gap: 15px;
+        margin-bottom: 20px;
+    }
+
+    /* Footer styling */
+    .footer {
+        margin-top: 40px;
+        text-align: center;
+        padding: 25px;
+        background-color: #34495e; /* Dark footer */
+        color: #ecf0f1; /* Light text for dark footer */
+        border-radius: 10px 10px 0 0; /* Rounded top corners */
+        font-size: 0.9em;
+    }
+    .footer a {
+        color: #3498db; /* Link color */
+        text-decoration: none;
+    }
+    .footer a:hover {
+        text-decoration: underline;
+    }
+
+    /* Search box enhancement */
+    .search-container .stTextInput input {
+        border-radius: 25px !important;
+        padding: 12px 20px !important;
+        border: 1px solid #bdc3c7 !important; /* Light gray border */
+        box-shadow: none !important; /* Remove default Streamlit shadow */
+        transition: border-color 0.2s, box-shadow 0.2s;
+    }
+    .search-container .stTextInput input:focus {
+        border-color: #3498db !important; /* Primary color on focus */
+        box-shadow: 0 0 0 3px rgba(52, 152, 219, 0.2) !important;
+    }
+
+    /* Styling for info/success messages */
+    .stAlert > div[data-baseweb="alert"] {
+        border-radius: 8px !important;
+    }
+
+    /* Section headers */
+    .section-header {
+        margin-top: 25px;
+        margin-bottom: 15px;
+        padding-bottom: 5px;
+        border-bottom: 2px solid #3498db; /* Primary color underline */
+        display: inline-block; /* To make border only as wide as text */
+    }
+    .section-header h4 {
+        margin-bottom: 0;
+        color: #3498db; /* Primary color for section titles */
+    }
+
+    /* Expander styling */
+    .stExpander {
+        border: 1px solid #e0e0e0 !important;
+        border-radius: 8px !important;
+        box-shadow: 0 2px 4px rgba(0,0,0,0.03) !important;
+    }
+    .stExpander header {
+        background-color: #f8f9fa !important;
+        border-radius: 8px 8px 0 0 !important; /* Match expander radius */
+        padding: 10px 15px !important;
+    }
+
+</style>
+""", unsafe_allow_html=True)
+
+# --- Gemini API Key Handling ---
+API_KEY = os.getenv("GOOGLE_API_KEY", "AIzaSyDkiYr-eSkqIXpZ1fHlik_YFsFtfQoFi0w") # Use yours, or allow env var
+if not API_KEY or API_KEY == "YOUR_API_KEY_HERE": # Default check
+    st.sidebar.error("🔴 GOOGLE_API_KEY not set. Please set it as an environment variable or enter below.")
+    API_KEY = st.sidebar.text_input("Enter your Gemini API Key:", type="password", key="api_key_input_explorer_v4")
+
+if API_KEY and API_KEY != "YOUR_API_KEY_HERE":
+    try:
+        genai.configure(api_key=API_KEY)
+    except Exception as e:
+        st.error(f"Error configuring Gemini API: {e}")
+        st.stop()
+else:
+    st.error("🔴 Gemini API Key is required to run this application.")
+    st.stop()
+
+# --- Services & Constants ---
+geolocator = Nominatim(user_agent="india_landslide_explorer_v4")
+FORECAST_DAYS = 14
+SEISMIC_RADIUS_KM = 150
+SEISMIC_MIN_MAGNITUDE = 4.0
+SEISMIC_DAYS_AGO = 30
+
+# --- Session State Initialization ---
+if 'map_center_india' not in st.session_state: st.session_state.map_center_india = [20.5937, 78.9629]
+if 'map_zoom_india' not in st.session_state: st.session_state.map_zoom_india = 4
+if 'selected_lat_lon' not in st.session_state: st.session_state.selected_lat_lon = None
+if 'location_name' not in st.session_state: st.session_state.location_name = ""
+if 'exploration_output' not in st.session_state: st.session_state.exploration_output = {"kpi_data": {}, "detailed_text": {}}
+if 'api_data_fetched' not in st.session_state: st.session_state.api_data_fetched = {}
+if 'is_fetching_data' not in st.session_state: st.session_state.is_fetching_data = False
+
+
+# --- Helper Functions (get_elevation, fetch_rainfall_data, fetch_seismic_data, reverse_geocode) ---
+def get_elevation(lat, lon):
+    try:
+        url = f"https://api.open-meteo.com/v1/elevation?latitude={lat}&longitude={lon}"
+        response = requests.get(url, timeout=10)
+        response.raise_for_status()
+        data = response.json()
+        return data['elevation'][0]
+    except Exception: return "N/A"
+
+def fetch_rainfall_data(lat, lon, forecast_days_count=FORECAST_DAYS):
+    url = "https://api.open-meteo.com/v1/forecast"
+    params = {
+        "latitude": lat, "longitude": lon,
+        "daily": "precipitation_sum,precipitation_hours",
+        "current": "precipitation,rain,showers,snowfall",
+        "forecast_days": forecast_days_count, "timezone": "auto"
+    }
+    try:
+        response = requests.get(url, params=params, timeout=15)
+        response.raise_for_status()
+        data = response.json()
+        current_data = data.get("current", {})
+        daily_data = data.get("daily", {})
+        df_daily_forecast = pd.DataFrame()
+        if daily_data.get("time") and daily_data.get("precipitation_sum"):
+            df_daily_forecast = pd.DataFrame({
+                "Date": pd.to_datetime(daily_data["time"]),
+                "Rainfall_Sum (mm)": daily_data["precipitation_sum"],
+                "Precipitation_Hours (hrs)": daily_data.get("precipitation_hours", [0]*len(daily_data["time"]))
+            }).set_index("Date")
+        return {
+            "current_precipitation_mm": current_data.get("precipitation", "N/A"),
+            "current_rain_mm": current_data.get("rain", "N/A"),
+            "current_showers_mm": current_data.get("showers", "N/A"),
+            "current_snowfall_cm": current_data.get("snowfall", "N/A"),
+            "daily_forecast_df": df_daily_forecast
+        }
+    except Exception as e:
+        st.toast(f"Weather fetch error: {e}", icon="🌦️")
+        return {"current_precipitation_mm": "Error", "daily_forecast_df": pd.DataFrame()}
+
+def fetch_seismic_data(lat, lon, radius_km=SEISMIC_RADIUS_KM, min_mag=SEISMIC_MIN_MAGNITUDE, days_ago=SEISMIC_DAYS_AGO):
+    try:
+        end_time = datetime.utcnow()
+        start_time = end_time - timedelta(days=days_ago)
+        url = "https://earthquake.usgs.gov/fdsnws/event/1/query"
+        params = {
+            "format": "geojson", "latitude": lat, "longitude": lon,
+            "maxradiuskm": radius_km, "minmagnitude": min_mag,
+            "starttime": start_time.strftime("%Y-%m-%dT%H:%M:%S"),
+            "endtime": end_time.strftime("%Y-%m-%dT%H:%M:%S"), "orderby": "time"
+        }
+        response = requests.get(url, params=params, timeout=15)
+        response.raise_for_status()
+        data = response.json()
+        earthquakes = []
+        for feature in data.get("features", []):
+            props = feature.get("properties", {}); geom = feature.get("geometry", {})
+            if props and geom and props.get("mag") is not None and geom.get("coordinates"):
+                earthquakes.append({
+                    "place": props.get("place", "Unknown"), "magnitude": props.get("mag"),
+                    "time": datetime.utcfromtimestamp(props.get("time") / 1000).strftime('%Y-%m-%d %H:%M UTC'),
+                    "depth_km": geom.get("coordinates")[2] if len(geom.get("coordinates", [])) > 2 else "N/A",
+                    "url": props.get("url")})
+        return earthquakes
+    except Exception as e:
+        st.toast(f"Seismic fetch error: {e}", icon="🌋"); return []
+
+def reverse_geocode(lat, lon):
+    try:
+        location = geolocator.reverse((lat, lon), exactly_one=True, timeout=10)
+        return location.address if location else "Unknown location"
+    except Exception: return "Could not determine address"
+
+# --- Gemini Prompt and Parsing V4 ---
+def get_gemini_exploration_v4(location_name, lat_lon, api_data):
+    model = genai.GenerativeModel('gemini-1.5-flash-latest')
+
+    elevation_str = f"{api_data.get('elevation_m', 'N/A')}"
+    weather_data = api_data.get('weather', {})
+    current_precip_str = f"{weather_data.get('current_precipitation_mm', 'N/A')}"
+    forecast_df = weather_data.get('daily_forecast_df')
+    forecast_summary_str = "N/A"
+    if forecast_df is not None and not forecast_df.empty:
+        summary_days = min(7, len(forecast_df))
+        forecast_days_summary = [f"Day {i+1} ({forecast_df.index[i].strftime('%Y-%m-%d')}): {forecast_df['Rainfall_Sum (mm)'].iloc[i] if pd.notna(forecast_df['Rainfall_Sum (mm)'].iloc[i]) else 'N/A'} mm" for i in range(summary_days)]
+        forecast_summary_str = "; ".join(forecast_days_summary) if forecast_days_summary else "No forecast data."
+    elif isinstance(forecast_df, pd.DataFrame) and forecast_df.empty:
+         forecast_summary_str = "Forecast data empty/unavailable."
+
+    seismic_events = api_data.get('seismic', [])
+    seismic_summary_str = "No significant recent seismic activity reported by USGS in the vicinity."
+    if seismic_events:
+        event_strs = [f"Mag {event['magnitude']} event near {event['place'].split('of')[-1].strip() if 'of' in event['place'] else event['place']}, on {event['time'].split(' ')[0]}" for event in seismic_events[:2]]
+        seismic_summary_str = "Recent Seismic Activity: " + "; ".join(event_strs)
+        if len(seismic_events) > 2: seismic_summary_str += f"; and {len(seismic_events)-2} more similar events."
+
+    prompt = f"""
+    You are an AI assistant for an advanced educational landslide factor exploration tool focused on INDIA (Version 4 - Visual Focus).
+    This tool DOES NOT use specific user observations of local conditions.
+    Your discussion will be based on the provided general location, fetched API data (elevation, weather, recent seismic activity), and your broad knowledge of Indian geography, geology, land cover, and climate.
+    This is strictly for educational purposes to explore POTENTIAL factors for a TYPE of area, NOT a real-time prediction or specific site assessment.
+
+    Location & Fetched Data:
+    - Approximate Location Name: "{location_name}" (Lat/Lon: {lat_lon[0]:.4f}, {lat_lon[1]:.4f})
+    - Elevation: {elevation_str} meters
+    - Current Precipitation Summary: {current_precip_str} mm
+    - Rainfall Forecast Summary (e.g., next 7 days): {forecast_summary_str}
+    - Recent Seismic Activity Summary (within ~{SEISMIC_RADIUS_KM}km, M{SEISMIC_MIN_MAGNITUDE}+, last {SEISMIC_DAYS_AGO} days): {seismic_summary_str}
+
+    Task:
+    Based on the above information and your general knowledge, please provide the following structured exploration.
+    First, provide specific KPI data, then provide the detailed textual explanations.
+
+    KPI_DATA_START
+    GENERAL_SUSCEPTIBILITY_LEVEL: [Provide one single category: Low / Moderate / High / Very High - based on typical regional characteristics for this type of area]
+    RAINFALL_IMPACT_ASSESSMENT: [Provide one single category: Low Concern / Moderate Concern / Significant Concern / High Concern - regarding its potential to trigger landslides in this type of area given the forecast and typical seasonal patterns]
+    SEISMIC_IMPACT_ASSESSMENT: [Provide one single category: Negligible / Low Potential / Moderate Potential / Significant Potential - as a landslide trigger in this type of area, considering reported activity and general regional seismicity]
+    TOP_HYPOTHETICAL_LANDSLIDE_TYPES: [List up to 3 most common/likely landslide types for similar regions in India, separated by commas, e.g., Debris Flow, Rockfall, Rotational Slump]
+    KEY_CONTRIBUTING_FACTORS_POINTS:
+    - [Brief point (max 10 words) on a key natural factor, e.g., Steep topography typical of the region]
+    - [Brief point (max 10 words) on a key human-induced factor, e.g., Unplanned construction if prevalent in similar areas]
+    - [Brief point (max 10 words) on another critical factor, e.g., Intense monsoon rainfall patterns]
+    TYPICAL_LAND_COVER_INFERRED: [Describe in 1-3 words the most typical general land cover you infer for this type of region, e.g., Forested Slopes, Agricultural Terraces, Urbanizing Hillsides, Barren Rocky Terrain]
+    KPI_DATA_END
+
+    Now, provide the detailed textual explanations, structured with the following headers:
+
+    HEADER_KEY_INSIGHTS_SUMMARY
+    Provide 2-3 bullet points elaborating on the most critical potential landslide-related insights or considerations for this TYPE of area in India, building upon the KPI data.
+
+    HEADER_SUSCEPTIBILITY_DISCUSSION
+    A. General Discussion of Landslide Susceptibility for this TYPE of Area:
+       (Elaborate on the GENERAL_SUSCEPTIBILITY_LEVEL. Discuss typical geological features, soil types, or topographical characteristics for this type of area. Ensure the output for GENERAL_SUSCEPTIBILITY_LEVEL provided in KPI_DATA_START is consistent with this discussion and includes "General Susceptibility for this type of area: " before the level, e.g., "General Susceptibility for this type of area: Moderate").
+
+    HEADER_DATA_ANALYSIS
+    B. Analysis of Fetched Data in Context of Potential Landslides:
+       (Elaborate on RAINFALL_IMPACT_ASSESSMENT and SEISMIC_IMPACT_ASSESSMENT. Discuss how fetched rainfall, elevation, and seismic data influence landslide potential, considering seasonal patterns and regional context).
+
+    HEADER_HYPOTHETICAL_FACTORS
+    C. Hypothetical Contributing Factors (Beyond Fetched Data):
+       (Elaborate on KEY_CONTRIBUTING_FACTORS_POINTS and TYPICAL_LAND_COVER_INFERRED. Discuss typical land cover and other natural/human-induced factors common to such regions in India).
+
+    HEADER_COMMON_LANDSLIDE_TYPES
+    D. Common Landslide Types in Similar Indian Regions:
+       (Elaborate on TOP_HYPOTHETICAL_LANDSLIDE_TYPES. Describe their characteristics and triggers relevant to the scenario).
+
+    HEADER_CRITICAL_LOCAL_DATA_NEED
+    E. Critical Importance of Local Site-Specific Data (Emphasize very strongly!):
+       (Explain why absence of local observations makes specific risk assessment impossible. Detail necessary local data).
+
+    HEADER_AWARENESS_PREPAREDNESS
+    F. General Awareness & Preparedness Ideas (India Context):
+       (Suggest general, non-site-specific educational points on landslide awareness/preparedness).
+
+    HEADER_OFFICIAL_RESOURCES
+    G. Official Indian Resources & Further Learning:
+       (List key Indian government agencies and information sources).
+
+    Structure your response exactly with the specified KPI_DATA_START/END and HEADER_ SECTION NAMES.
+    Maintain an educational tone. Explicitly and repeatedly state the limitations.
+    """
+    try:
+        response = model.generate_content(prompt)
+        return response.text
+    except Exception as e:
+        st.error(f"Error communicating with Gemini API: {e}")
+        return None
+
+def parse_gemini_output_v4(text):
+    if not text: return {"kpi_data": {}, "detailed_text": {}}
+
+    kpi_data = {}
+    default_kpi_values = {
+        "GENERAL_SUSCEPTIBILITY_LEVEL": "N/A",
+        "RAINFALL_IMPACT_ASSESSMENT": "N/A",
+        "SEISMIC_IMPACT_ASSESSMENT": "N/A",
+        "TOP_HYPOTHETICAL_LANDSLIDE_TYPES": "Not specified",
+        "KEY_CONTRIBUTING_FACTORS_POINTS": [],
+        "TYPICAL_LAND_COVER_INFERRED": "N/A"
+    }
+    kpi_data.update(default_kpi_values)
+
+    detailed_text_sections_map = {
+        "HEADER_KEY_INSIGHTS_SUMMARY": "🔑 Key Insights Summary",
+        "HEADER_SUSCEPTIBILITY_DISCUSSION": "🧐 General Susceptibility Discussion",
+        "HEADER_DATA_ANALYSIS": "📊 Analysis of Fetched Data",
+        "HEADER_HYPOTHETICAL_FACTORS": "🤔 Contributing Factors",
+        "HEADER_COMMON_LANDSLIDE_TYPES": "🏞️ Common Landslide Types",
+        "HEADER_CRITICAL_LOCAL_DATA_NEED": "❗CRUCIAL: Need for Local Site-Specific Data❗",
+        "HEADER_AWARENESS_PREPAREDNESS": "💡 General Awareness & Preparedness",
+        "HEADER_OFFICIAL_RESOURCES": "🇮🇳 Official Resources & Further Learning"
+    }
+    parsed_detailed_text = {display_name: [] for _, display_name in detailed_text_sections_map.items()}
+
+    in_kpi_section = False
+    current_detailed_section_key = None
+    key_factors_collecting = False
+
+    kpi_regex_map = {
+        "GENERAL_SUSCEPTIBILITY_LEVEL": re.compile(r"GENERAL_SUSCEPTIBILITY_LEVEL:\s*(.+)", re.IGNORECASE),
+        "RAINFALL_IMPACT_ASSESSMENT": re.compile(r"RAINFALL_IMPACT_ASSESSMENT:\s*(.+)", re.IGNORECASE),
+        "SEISMIC_IMPACT_ASSESSMENT": re.compile(r"SEISMIC_IMPACT_ASSESSMENT:\s*(.+)", re.IGNORECASE),
+        "TOP_HYPOTHETICAL_LANDSLIDE_TYPES": re.compile(r"TOP_HYPOTHETICAL_LANDSLIDE_TYPES:\s*(.+)", re.IGNORECASE),
+        "TYPICAL_LAND_COVER_INFERRED": re.compile(r"TYPICAL_LAND_COVER_INFERRED:\s*(.+)", re.IGNORECASE),
+    }
+
+    for line in text.splitlines():
+        line_strip = line.strip()
+        if not line_strip: continue
+
+        if line_strip == "KPI_DATA_START":
+            in_kpi_section = True; continue
+        if line_strip == "KPI_DATA_END":
+            in_kpi_section = False; key_factors_collecting = False; continue
+
+        if in_kpi_section:
+            matched_specific_kpi = False
+            for key, pattern in kpi_regex_map.items():
+                match = pattern.match(line_strip)
+                if match:
+                    kpi_data[key] = match.group(1).strip()
+                    matched_specific_kpi = True; break
+            if matched_specific_kpi: continue
+
+            if line_strip.startswith("KEY_CONTRIBUTING_FACTORS_POINTS:"):
+                key_factors_collecting = True; kpi_data["KEY_CONTRIBUTING_FACTORS_POINTS"] = [] # Reset for new parse
+                continue
+
+            if key_factors_collecting and line_strip.startswith("-"):
+                kpi_data["KEY_CONTRIBUTING_FACTORS_POINTS"].append(line_strip.lstrip("- ").strip())
+            continue
+
+        found_new_header = False
+        for header_key_from_prompt, display_name in detailed_text_sections_map.items():
+            if line_strip == header_key_from_prompt:
+                current_detailed_section_key = display_name
+                found_new_header = True; break
+        if not found_new_header and current_detailed_section_key:
+            parsed_detailed_text[current_detailed_section_key].append(line)
+
+    final_detailed_text = {k: "\n".join(v).strip() for k, v in parsed_detailed_text.items()}
+    return {"kpi_data": kpi_data, "detailed_text": final_detailed_text}
+
+# --- UI Rendering with Enhanced Styling ---
+st.markdown('<div class="main-header"><h1>🇮🇳 India Landslide Factor Explorer V4</h1></div>', unsafe_allow_html=True)
+st.caption("Educational Tool by Google Gemini & Streamlit - Exploring Potential Landslide Factors")
+
+
+
+
+st.markdown("""
+<div class="card">
+    <h3>🗺️ How to Use This Tool</h3>
+    <p>Welcome! Begin by <strong>selecting a location on the map</strong> or using the <strong>search bar</strong> to find a specific place in India.
+    The tool will then fetch publicly available data (elevation, weather forecast, recent seismic activity) for the chosen area.
+    After data retrieval, you can initiate an AI-powered exploration. The AI will provide a <em>generalized discussion</em> on potential landslide susceptibility and contributing factors relevant to that <strong>type of area in India</strong>, based on the fetched data and its broad geographical knowledge.</p>
+</div>
+""", unsafe_allow_html=True)
+
+st.markdown("""
+<div class="warning-box">
+    <h3>⚠️ CRITICAL DISCLAIMER & LIMITATIONS</h3>
+    <ul>
+        <li>This tool <strong>DOES NOT use any specific local observations or detailed site-specific geotechnical data</strong>.</li>
+        <li>The AI-generated discussion is <strong>HIGHLY GENERALIZED, HYPOTHETICAL, and intended for BROAD EDUCATIONAL PURPOSES ONLY</strong>.</li>
+        <li><strong>IT IS NOT A PREDICTION, nor a real-time warning system, nor a site-specific risk assessment.</strong> It cannot replace professional engineering or geological surveys.</li>
+        <li>For actual safety information, risk assessment, or emergency guidance, <strong>ALWAYS consult official Indian government authorities</strong> (like NDMA, GSI) and qualified local geotechnical experts.</li>
+    </ul>
+</div>
+""", unsafe_allow_html=True)
+
+col_map_input, col_ai_output = st.columns([0.45, 0.55]) # Adjusted column ratio
+
+with col_map_input:
+    st.markdown('<div class="card">', unsafe_allow_html=True) # Wrap entire input column in a card
+    st.markdown('<div class="section-header"><h4>📍 Select Location & View Data</h4></div>', unsafe_allow_html=True)
+
+    st.markdown('<div class="search-container">', unsafe_allow_html=True)
+    search_location_input = st.text_input(
+        "Search for a location in India:",
+        key="search_loc_v4",
+        placeholder="e.g., Shimla, Munnar, Darjeeling..."
+    )
+    st.markdown('</div>', unsafe_allow_html=True)
+
+    search_btn_col, reset_btn_col = st.columns([3,1])
+    with search_btn_col:
+        if st.button("🔍 Search Location", key="search_btn_v4", use_container_width=True):
+            if search_location_input:
+                with st.spinner(f"Searching for '{search_location_input}'..."):
+                    try:
+                        loc = geolocator.geocode(search_location_input + ", India", timeout=10)
+                        if loc:
+                            st.session_state.selected_lat_lon = [loc.latitude, loc.longitude]
+                            st.session_state.map_center_india = [loc.latitude, loc.longitude]
+                            st.session_state.map_zoom_india = 11
+                            st.session_state.location_name = loc.address
+                            st.session_state.api_data_fetched = {}
+                            st.session_state.exploration_output = {"kpi_data": {}, "detailed_text": {}}
+                            st.session_state.is_fetching_data = True # Trigger data fetching
+                            st.toast(f"🗺️ Location found: {loc.address.split(',')[0]}. Fetching data...", icon="✅")
+                            st.rerun()
+                        else:
+                            st.warning(f"❌ Could not find '{search_location_input}'. Please try a different or more specific name.")
+                    except Exception as e:
+                        st.error(f"Geocoding error: {e}")
+            else:
+                st.info("Please enter a location name to search.")
+    with reset_btn_col:
+        if st.button("🔄 Reset", key="reset_btn_v4", use_container_width=True, type="secondary"):
+            st.session_state.selected_lat_lon = None
+            st.session_state.map_center_india = [20.5937, 78.9629]
+            st.session_state.map_zoom_india = 4
+            st.session_state.location_name = ""
+            st.session_state.api_data_fetched = {}
+            st.session_state.exploration_output = {"kpi_data": {}, "detailed_text": {}}
+            st.session_state.is_fetching_data = False
+            st.toast("🔄 Map & selection reset.", icon="🗺️")
+            st.rerun()
+
+
+    st.markdown('<div class="map-container">', unsafe_allow_html=True)
+    st.markdown("<small><i>Click on the map to select a point, or use search above.</i></small>", unsafe_allow_html=True)
+    folium_map_display = folium.Map(
+        location=st.session_state.map_center_india,
+        zoom_start=st.session_state.map_zoom_india,
+        tiles="CartoDB positron",
+        key="folium_map_v4_instance" # Ensure unique key if map is complex
+    )
+    if st.session_state.selected_lat_lon:
+        folium.Marker(
+            st.session_state.selected_lat_lon,
+            popup=f"Selected: {st.session_state.location_name.split(',')[0]}" if st.session_state.location_name else "Selected Point",
+            tooltip="Current Selection",
+            icon=folium.Icon(color="red", icon="info-sign")
+        ).add_to(folium_map_display)
+
+    map_interaction_data = st_folium(
+        folium_map_display,
+        width="100%",
+        height=330,
+        key="map_v4_interaction",
+        returned_objects=["last_clicked"]
+    )
+    st.markdown('</div>', unsafe_allow_html=True)
+
+    if map_interaction_data and map_interaction_data.get("last_clicked"):
+        clicked_lat = map_interaction_data["last_clicked"]["lat"]
+        clicked_lon = map_interaction_data["last_clicked"]["lng"]
+        if st.session_state.selected_lat_lon is None or \
+           abs(st.session_state.selected_lat_lon[0] - clicked_lat) > 0.00001 or \
+           abs(st.session_state.selected_lat_lon[1] - clicked_lon) > 0.00001:
+            st.session_state.selected_lat_lon = [clicked_lat, clicked_lon]
+            st.session_state.location_name = reverse_geocode(clicked_lat, clicked_lon)
+            st.session_state.map_center_india = [clicked_lat, clicked_lon] # Recenter map
+            st.session_state.map_zoom_india = max(st.session_state.map_zoom_india, 11) # Zoom in
+            st.session_state.api_data_fetched = {}
+            st.session_state.exploration_output = {"kpi_data": {}, "detailed_text": {}}
+            st.session_state.is_fetching_data = True # Trigger data fetching
+            st.toast(f"📍 Pinned: {st.session_state.location_name.split(',')[0]}. Fetching data...", icon="🗺️")
+            st.rerun()
+
+    explore_button_active = False
+    if st.session_state.selected_lat_lon:
+        st.success(f"**Selected Location:** {st.session_state.location_name}\n(Lat: {st.session_state.selected_lat_lon[0]:.4f}, Lon: {st.session_state.selected_lat_lon[1]:.4f})")
+
+        if st.session_state.is_fetching_data and not st.session_state.api_data_fetched: # Fetch data only if flag is true and not fetched
+            with st.spinner(f"⏳ Fetching environmental data for {st.session_state.location_name.split(',')[0]}... This might take a few seconds."):
+                lat, lon = st.session_state.selected_lat_lon
+                api_data_temp = {}
+                api_data_temp['elevation_m'] = get_elevation(lat, lon)
+                api_data_temp['weather'] = fetch_rainfall_data(lat, lon)
+                api_data_temp['seismic'] = fetch_seismic_data(lat, lon)
+                st.session_state.api_data_fetched = api_data_temp
+                st.session_state.is_fetching_data = False # Reset flag
+                st.rerun() # Rerun to display fetched data
+
+        if st.session_state.api_data_fetched: # Display fetched data
+            st.markdown('<div class="data-card">', unsafe_allow_html=True)
+            st.markdown("##### 🛰️ Fetched Environmental Data:")
+            api_data = st.session_state.api_data_fetched
+            elev = api_data.get('elevation_m', 'N/A')
+            weather = api_data.get('weather', {})
+            curr_precip = weather.get('current_precipitation_mm', 'N/A')
+            seismic_events = api_data.get('seismic', [])
+
+            data_cols = st.columns(2)
+            with data_cols[0]:
+                st.metric(label="🏔️ Elevation", value=f"{elev} m" if elev != "N/A" else "N/A")
+            with data_cols[1]:
+                st.metric(label="💧 Current Precip.", value=f"{curr_precip} mm" if curr_precip not in ["N/A", "Error"] else curr_precip)
+
+            with st.expander(f"🌋 Seismic Activity (Last {SEISMIC_DAYS_AGO} days, M{SEISMIC_MIN_MAGNITUDE}+, ~{SEISMIC_RADIUS_KM}km radius)", expanded=len(seismic_events) > 0):
+                if seismic_events:
+                    st.caption(f"Found {len(seismic_events)} significant earthquake(s) reported by USGS:")
+                    for event in seismic_events[:5]:
+                        st.markdown(f"- **M {event['magnitude']}** - {event['place']} ({event['time']}). Depth: {event['depth_km']} km. [More Info]({event.get('url', '#')})", unsafe_allow_html=True)
+                    if len(seismic_events) > 5: st.caption(f"...and {len(seismic_events)-5} more.")
+                else:
+                    st.caption("No significant recent seismic activity reported by USGS matching criteria.")
+
+            st.markdown("##### 🌦️ Rainfall Forecast (mm/day):")
+            forecast_df = weather.get('daily_forecast_df')
+            if forecast_df is not None and not forecast_df.empty:
+                st.markdown('<div class="data-viz">', unsafe_allow_html=True)
+                st.line_chart(forecast_df['Rainfall_Sum (mm)'], height=180)
+                st.markdown('</div>', unsafe_allow_html=True)
+                cum_rain = forecast_df['Rainfall_Sum (mm)'].cumsum()
+                periods = [3, 7, min(FORECAST_DAYS, len(cum_rain))]
+                st.markdown("**Cumulative Rainfall Forecast:**")
+                cum_cols_display = st.columns(len(periods))
+                for i, p_days in enumerate(periods):
+                    if 0 < p_days <= len(cum_rain):
+                        val = cum_rain.iloc[p_days-1]
+                        with cum_cols_display[i]:
+                            st.metric(label=f"{p_days}-Day Total", value=f"{val:.1f}mm" if pd.notna(val) else "N/A")
+            else:
+                st.caption("Rainfall forecast data unavailable or encountered an error.")
+            st.markdown('</div>', unsafe_allow_html=True) # End data-card
+
+            if elev != "N/A" and curr_precip not in ["N/A", "Error"]: # Enable button if core data is present
+                explore_button_active = True
+    else: # No location selected
+        st.info("👈 Please select a location on the map or use the search bar to begin.")
+
+    if explore_button_active:
+        if st.button("🤖 Explore Potential Factors with AI", type="primary", use_container_width=True, key="explore_btn_v4"):
+            if st.session_state.selected_lat_lon and st.session_state.api_data_fetched:
+                with st.spinner("💡 Gemini AI is analyzing... This may take a moment for a comprehensive exploration."):
+                    raw_gemini_output = get_gemini_exploration_v4(
+                        st.session_state.location_name,
+                        st.session_state.selected_lat_lon,
+                        st.session_state.api_data_fetched
+                    )
+                    if raw_gemini_output:
+                        st.session_state.exploration_output = parse_gemini_output_v4(raw_gemini_output)
+                        st.toast("✅ AI Exploration complete!", icon="💡")
+                    else:
+                        st.error("AI exploration failed. Please check API key or try again later.")
+            else:
+                st.warning("Please select a location and ensure data is fetched before exploring.")
+    elif st.session_state.selected_lat_lon and not st.session_state.api_data_fetched and not st.session_state.is_fetching_data:
+         st.warning("Data for the selected location is still fetching or incomplete. AI exploration is disabled until data is ready.")
+
+    st.markdown('</div>', unsafe_allow_html=True) # End of card for col_map_input
+
+with col_ai_output:
+    st.markdown('<div class="card">', unsafe_allow_html=True) # Wrap entire AI output column in a card
+    st.markdown('<div class="section-header"><h4>🌍 AI-Powered Exploration (Generalized)</h4></div>', unsafe_allow_html=True)
+
+    output_data = st.session_state.exploration_output
+    kpi_results = output_data.get("kpi_data", {})
+    detailed_results = output_data.get("detailed_text", {})
+
+    if kpi_results and any(val != "N/A" and val != "Not specified" and val for val in kpi_results.values()):
+        st.markdown("##### 📊 Key Indicators (AI Inferred for this Type of Area):")
+        st.markdown('<div class="kpi-grid">', unsafe_allow_html=True)
+
+        sus_level = kpi_results.get("GENERAL_SUSCEPTIBILITY_LEVEL", "N/A")
+        sus_delta_color = "normal"
+        if "low" in sus_level.lower(): sus_delta_color = "normal"
+        elif "moderate" in sus_level.lower(): sus_delta_color = "off"
+        elif "high" in sus_level.lower() or "very high" in sus_level.lower(): sus_delta_color = "inverse"
+        st.metric(label="🏔️ General Susceptibility", value=sus_level, delta_color=sus_delta_color, help="AI's assessment of general landslide susceptibility for this type of area in India, based on broad knowledge.")
+
+        rain_impact = kpi_results.get("RAINFALL_IMPACT_ASSESSMENT", "N/A")
+        rain_delta_color = "normal"
+        if "low" in rain_impact.lower(): rain_delta_color = "normal"
+        elif "moderate" in rain_impact.lower(): rain_delta_color = "off"
+        elif "significant" in rain_impact.lower() or "high" in rain_impact.lower(): rain_delta_color = "inverse"
+        st.metric(label="💧 Rainfall Impact", value=rain_impact, delta_color=rain_delta_color, help="AI's assessment of rainfall's potential role, considering forecast and typical seasonal patterns for the area type.")
+
+        seismic_impact = kpi_results.get("SEISMIC_IMPACT_ASSESSMENT", "N/A")
+        seis_delta_color = "normal"
+        if "negligible" in seismic_impact.lower() or "low" in seismic_impact.lower(): seis_delta_color = "normal"
+        elif "moderate" in seismic_impact.lower(): seis_delta_color = "off"
+        elif "significant" in seismic_impact.lower(): seis_delta_color = "inverse"
+        st.metric(label="🌋 Seismic Impact", value=seismic_impact, delta_color=seis_delta_color, help="AI's assessment of seismic activity's potential role as a trigger for this type of area.")
+        st.markdown('</div>', unsafe_allow_html=True) # End kpi-grid
+        st.markdown("---")
+
+        col_kpi_list1, col_kpi_list2 = st.columns(2)
+        with col_kpi_list1:
+            st.markdown("##### 🏞️ Top Landslide Types:")
+            top_types_str = kpi_results.get("TOP_HYPOTHETICAL_LANDSLIDE_TYPES", "Not specified by AI.")
+            top_types_list = [s.strip() for s in top_types_str.split(',') if s.strip()]
+            if top_types_list and top_types_list[0].lower() != "not specified":
+                for l_type in top_types_list: st.markdown(f"- {l_type}")
+            else: st.caption(top_types_str)
+
+        with col_kpi_list2:
+            st.markdown("##### 🌳 Typical Land Cover (Inferred):")
+            land_cover = kpi_results.get("TYPICAL_LAND_COVER_INFERRED", "Not specified by AI.")
+            st.info(f"{land_cover}")
+
+
+        st.markdown("##### 🔑 Key Contributing Factors:")
+        key_factors = kpi_results.get("KEY_CONTRIBUTING_FACTORS_POINTS", [])
+        if key_factors and isinstance(key_factors, list) and any(key_factors):
+            for factor in key_factors: st.markdown(f"- _{factor}_")
+        else: st.caption("Not specified or N/A by AI.")
+
+        st.markdown("---")
+        st.markdown("##### 💬 Detailed AI Exploration Text:")
+        tab_titles = [key for key in detailed_results.keys() if detailed_results[key]]
+        if tab_titles:
+            tabs = st.tabs(tab_titles)
+            for i, title in enumerate(tab_titles):
+                with tabs[i]:
+                    st.markdown(detailed_results[title], unsafe_allow_html=True) # Allow HTML for Gemini's formatting
+        else:
+            st.warning("AI exploration did not yield detailed textual content. The API might have had an issue or the prompt needs adjustment.")
+            if 'gemini_raw_output_debug' in st.session_state and st.session_state['gemini_raw_output_debug']:
+                 with st.expander("Show Raw Gemini Output (for debugging)"):
+                    st.text_area("Raw Output:", st.session_state['gemini_raw_output_debug'], height=200)
+
+    elif st.session_state.selected_lat_lon and explore_button_active:
+        st.info("🤖 Click the 'Explore Potential Factors with AI' button on the left panel after data for the selected location has been fetched. The AI's insights will appear here.")
+    elif not st.session_state.selected_lat_lon:
+        st.info("👈 Please select a location in the left panel first. AI exploration results will then be generated and displayed here.")
+    else:
+        st.info("AI exploration results will appear here once a location is selected, data is fetched, and the AI analysis is run.")
+
+    st.markdown("---")
+    st.markdown('<div class="section-header"><h4>🇮🇳 Official Indian Resources</h4></div>', unsafe_allow_html=True)
+    st.markdown("""
+    For accurate, official, and site-specific landslide information and warnings in India, please consult these primary resources:
+    - **National Disaster Management Authority (NDMA):** [ndma.gov.in](https://ndma.gov.in) - For national guidelines and disaster management.
+    - **Geological Survey of India (GSI):** [gsi.gov.in](https://www.gsi.gov.in/) - For geological data, landslide hazard zonation maps.
+    - **National Remote Sensing Centre (NRSC) Bhuvan Portal (ISRO):** [bhuvan.nrsc.gov.in](https://bhuvan.nrsc.gov.in/bhuvan_links.php) - For satellite imagery, thematic maps & disaster related services.
+    - **India Meteorological Department (IMD):** [mausam.imd.gov.in](https://mausam.imd.gov.in/) - For weather forecasts and warnings.
+    - **Your local State Disaster Management Authority (SDMA)** website (search for your state's SDMA).
+    """, unsafe_allow_html=True)
+    st.markdown('</div>', unsafe_allow_html=True) # End of card for col_ai_output
+
+
+st.markdown("---")
+st.markdown(
+    """
+    <div class="footer">
+        <p><strong>Tool Version:</strong> Explorer 4.0 Enhanced UI</p>
+        <p>This is an <strong>educational tool</strong> for exploring POTENTIAL landslide factors based on generalized knowledge and limited public data.
+        It <strong>DOES NOT</strong> provide official warnings, site-specific risk assessments, or professional geotechnical advice.
+        Real-world landslide analysis requires extensive, detailed local data and expert assessment by qualified professionals.
+        Always refer to official government sources for safety and risk information.</p>
+        <p>Powered by <a href="https://streamlit.io" target="_blank">Streamlit</a> and <a href="https://ai.google.dev/" target="_blank">Google Gemini</a>.</p>
+    </div>
+    """, unsafe_allow_html=True
+)