app.py

import gradio as gr
import googlemaps
import osmnx as ox
import geopandas as gpd
import pandas as pd
import requests
import zipfile
import os
import glob
import shutil
import time
from shapely.geometry import Point, Polygon
from shapely.ops import transform

# ==========================================
# AUTHENTICATION
# ==========================================
try:
    API_KEY = os.environ.get("GOOGLE_API_KEY")
except:
    API_KEY = None

# ==========================================
# 1. UNIVERSAL FILTER LISTS (FINAL COMPLETE VERSION)
# ==========================================
# Filters out Schools, Doctors, Parking Lots, Repairs, etc.
UNIVERSAL_BAD_TERMS = [
    # Health / Medical
    "dr.", "dds", "md", "phd", "lcsw", "medical", "clinic", "health", "rehab", 
    "therapy", "counseling", "chiropractor", "dental", "orthodontics", "hospital", 
    "ambulance", "transport", "emergency", "veterinary", "vision center", 
    "spinal cord", "urgent care", "hellomed", "spine", "program",
    # Education
    "school", "university", "college", "academy", "campus", "library", "learning", 
    "student", "alum", "education", "institute", "dorm", "residence",
    # Services / Misc 
    "atm", "kiosk", "redbox", "coinme", "fuel", "gas", "repair", "service", 
    "collision", "towing", "plumbing", "hvac", "electric", "tree", "lawn", 
    "gutter", "cleaning", "storage", "warehouse", "distribution", "mural", "statue",
    "part", "accessories", "hair", "salon", "studio", "barber", "spa", "nail",
    "diamonds", "jewelers", "pllc", "llc", "parking", "drive", "cooling", "heating",
    "brandy", "bike shop", "grooming"
]

# Filters out departments inside Big Box stores (Fixes Area Splitting)
DEPARTMENT_TERMS = [
    "grocery", "deli", "bakery", "pharmacy", "optical", "hearing", "photo", 
    "portrait", "garden", "nursery", "mobile", "tech", "geek", "pickup", 
    "money", "bank", "cafe", "bistro", "snack", "food court", "customer service",
    "floral", "flowers", "store on", "tire", "battery", "auto", "lube",
    "credit union", "sephora", "sunglass", "finish line", "pro desk", 
    "rental center", "svc drive", "inside", "at ", 
    "dog training"
]

# ==========================================
# 2. COMPREHENSIVE STORE LIST
# ==========================================
SEARCH_LIST = [
    # Big Box / Dept
    "Walmart", "Target", "Kmart", "Sears", "Kohl's", "Macy's", "JCPenney", 
    "Nordstrom", "Costco", "Sam's Club", "BJ's Wholesale Club",
    
    # Clothing / Discount 
    "TJX", "T.J. Maxx", "Marshalls", "HomeGoods", "HomeSense", 
    "Ross Dress for Less", "Burlington", "Old Navy", "DSW Designer Shoe Warehouse",
    
    # Home Imp / Hardware / Furniture
    "Home Depot", "Lowe's", "Ace Hardware", "Menards",
    "IKEA", "Bob's Discount Furniture", "Raymour & Flanigan", "Ashley Furniture",
    
    # Electronics / Office
    "Best Buy", "Office Depot", "OfficeMax", "Staples",
    
    # Grocery
    "Kroger", "Meijer", "Whole Foods", "Trader Joe's", "Aldi", "Lidl", 
    "Safeway", "Albertsons", "ShopRite", "Stop & Shop", "Publix", "Wegmans",
    
    # Hobbies / Pets / Sporting
    "Dick's Sporting Goods", "Bass Pro Shops", "Cabela's", "REI",
    "Michaels", "Hobby Lobby", "Barnes & Noble", 
    "PetSmart", "Petco"
]

BRAND_FLOORS = {
  "Macy's": 2, "JCPenney": 2, "Nordstrom": 2, "Sears": 2, "IKEA": 2,
  "Target": 1, "Walmart": 1, "Costco": 1, "Home Depot": 1, "Lowe's": 1,
  "Barnes & Noble": 1, "Dick's Sporting Goods": 1, "Kohl's": 1
}

BRAND_AVG_AREA = {
    "IKEA": 28000, "Walmart": 15000, "Costco": 14000, "Sam's Club": 13000, 
    "Meijer": 18000, "Target": 12000, "Home Depot": 10000, "Lowe's": 10000, 
    "Kroger": 6000, "Safeway": 5000, "Whole Foods": 4000, "Macy's": 16000, 
    "JCPenney": 10000, "Sears": 12000, "Kohl's": 8000, "Dick's Sporting Goods": 4500,
    "T.J. Maxx": 2800, "Marshalls": 2800, "Ross Dress for Less": 2800, 
    "Old Navy": 1400, "Barnes & Noble": 2500, "Best Buy": 3500, "Staples": 2000, 
    "Office Depot": 2000, "PetSmart": 1800, "Petco": 1400, "Trader Joe's": 1200, 
    "Aldi": 1500, "Lidl": 1500, "Ace Hardware": 800, "DSW Designer Shoe Warehouse": 2000,
    "Hobby Lobby": 5000, "BJ's Wholesale Club": 10000, "REI": 4000
}

# ==========================================
# HELPER FUNCTIONS
# ==========================================
def load_geodata_to_polygon(file_obj):
    extract_path = "temp_extract"
    if os.path.exists(extract_path):
        shutil.rmtree(extract_path)
    os.makedirs(extract_path)

    target_kml = None
    try:
        # HANDLING KML AND KMZ HERE
        if file_obj.name.lower().endswith('.kmz'):
            with zipfile.ZipFile(file_obj.name, 'r') as zip_ref:
                zip_ref.extractall(extract_path)
            kml_files = glob.glob(extract_path + "/**/*.kml", recursive=True)
            if kml_files:
                target_kml = kml_files[0]
        elif file_obj.name.lower().endswith('.kml'):
            target_kml = file_obj.name

        if target_kml:
            gdf = gpd.read_file(target_kml)
            
            # FORCE 2D FIX (Prevents crashes on 3D KMLs)
            def force_2d(geometry):
                if geometry.has_z:
                    return transform(lambda x, y, z=None: (x, y), geometry)
                return geometry
            
            gdf.geometry = gdf.geometry.apply(force_2d)
            return gdf.union_all()
    except:
        return None
    return None

def get_roof_area(lat, lng, api_key):
    base_url = "https://solar.googleapis.com/v1/buildingInsights:findClosest"
    params = {"location.latitude": lat, "location.longitude": lng, "requiredQuality": "HIGH", "key": api_key}
    try:
        resp = requests.get(base_url, params=params)
        data = resp.json()
        if 'error' in data: return None
        return data.get('solarPotential', {}).get('wholeRoofStats', {}).get('areaMeters2', None)
    except:
        return None

def get_osm_physics(lat, lng):
    try:
        tags = {'building': True}
        gdf = ox.features.features_from_point((lat, lng), tags, dist=60)
        if not gdf.empty:
            gdf_proj = gdf.to_crs(epsg=3857)
            gdf_proj['area_m2'] = gdf_proj.geometry.area
            best = gdf_proj.sort_values(by='area_m2', ascending=False).iloc[0]
            
            floors = None
            if 'building:levels' in best and pd.notna(best['building:levels']):
                try: floors = int(float(str(best['building:levels']).split(';')[0]))
                except: pass
            
            height = None
            if 'height' in best and pd.notna(best['height']):
                try: height = float(str(best['height']).replace('m','').strip())
                except: pass
            return height, floors
    except:
        pass
    return None, None

# ==========================================
# MAIN LOGIC
# ==========================================
def process_data(file_obj):
    if not API_KEY:
        yield "❌ API Key not found! Set GOOGLE_API_KEY in Secrets.", None
        return
        
    if file_obj is None:
        yield "❌ Please upload a file.", None
        return

    yield "📂 Loading Polygon...", None
    polygon = load_geodata_to_polygon(file_obj)
    
    if not polygon:
        yield "❌ Failed to read KML/KMZ file.", None
        return

    # --- CHECK AREA LIMIT HERE (250,000,000 sq m) ---
    gs = gpd.GeoSeries([polygon], crs="EPSG:4326")
    gs_proj = gs.to_crs(epsg=6933)
    area_sq_meters = gs_proj.area.iloc[0]
    if area_sq_meters > 250_000_000:
        yield f"⚠️ AREA TOO LARGE: {area_sq_meters:,.0f} sq m. (Limit: 250M).", None
        return 

    gmaps = googlemaps.Client(key=API_KEY)
    results = []
    seen_ids = set()
    total_brands = len(SEARCH_LIST)
    
    # 1. SEARCH LOOP
    for i, brand in enumerate(SEARCH_LIST):
        yield f"🔍 Scanning Brand {i+1}/{total_brands}: {brand}...", None
        
        try:
            places = gmaps.places_nearby(
                location=(polygon.centroid.y, polygon.centroid.x),
                radius=10000, 
                keyword=brand
            )
            
            all_results = places.get('results', [])
            while 'next_page_token' in places:
                time.sleep(2)
                places = gmaps.places_nearby(
                    location=(polygon.centroid.y, polygon.centroid.x),
                    radius=10000,
                    keyword=brand,
                    page_token=places['next_page_token']
                )
                all_results.extend(places.get('results', []))

            for p in all_results:
                pid = p.get('place_id')
                if pid in seen_ids: continue
                
                name = p.get('name')
                name_clean = name.lower().replace("'", "").replace(".", "")
                brand_clean = brand.lower().replace("'", "").replace(".", "")

                # A. UNIVERSAL NAME CHECK
                if brand_clean not in name_clean:
                    if brand_clean == "tjx" and "t.j. maxx" in name_clean: pass
                    elif brand_clean == "lowe" and "lowe's" in name_clean: pass
                    else: continue

                # B. UNIVERSAL BAD WORD FILTER (Strict)
                if any(term in name_clean for term in UNIVERSAL_BAD_TERMS): continue

                lat = p['geometry']['location']['lat']
                lng = p['geometry']['location']['lng']
                
                # C. STRICT CONTAINMENT
                if not polygon.contains(Point(lng, lat)): continue
                seen_ids.add(pid)
                
                # FETCH DATA
                roof_area = get_roof_area(lat, lng, API_KEY)
                height, floors = get_osm_physics(lat, lng)
                
                # DATA FILLING
                source_note = "SolarAPI"
                if roof_area is None:
                    roof_area = BRAND_AVG_AREA.get(brand, 2500.0)
                    source_note = "Brand_Avg (Missing)"
                else:
                    # Universal Mall Logic
                    if roof_area > 30000 and brand not in ["IKEA", "Costco", "Meijer", "Sam's Club", "Walmart"]:
                        roof_area = BRAND_AVG_AREA.get(brand, 2500.0)
                        source_note = "Brand_Avg (Mall detected)"
                    elif roof_area < 500 and brand not in ["Ace Hardware", "Trader Joe's"]:
                        roof_area = BRAND_AVG_AREA.get(brand, 2500.0)
                        source_note = "Brand_Avg (Too Small detected)"
                
                if floors is None: floors = BRAND_FLOORS.get(brand, 1)
                if height is None: height = floors * 6.0
                
                results.append({
                    'Name': name, 
                    'Brand': brand, 
                    'Latitude': lat, 
                    'Longitude': lng,
                    'Height_m': round(height, 2), 
                    'Num_Floors': int(floors),
                    'Area_sqm': round(roof_area, 2),
                    'Data_Source': source_note
                })
        except:
            pass

    if not results:
        yield "❌ No stores found in this area.", None
        return

    # ==========================================
    # 2. UNIVERSAL POST-PROCESSING
    # ==========================================
    yield "🧹 Performing Universal Deduplication...", None
    
    df = pd.DataFrame(results)

    # A. Remove Departments (Target Grocery, Meijer Deli, Kroger Floral)
    df = df[~df['Name'].str.contains('|'.join(DEPARTMENT_TERMS), case=False, na=False)]

    # B. Spatial Deduplication (Group by Brand + Location)
    # Creates a grid ID approx 11 meters.
    df['Loc_ID'] = df['Latitude'].round(4).astype(str) + "_" + df['Longitude'].round(4).astype(str)
    
    # Sort by Name Length (Shortest name usually "Target", longest usually "Target Grocery ...")
    df['Name_Len'] = df['Name'].str.len()
    df = df.sort_values(by=['Brand', 'Loc_ID', 'Name_Len'])
    
    # Drop duplicates, keeping the shortest name
    df = df.drop_duplicates(subset=['Brand', 'Loc_ID'], keep='first')
    df = df.drop(columns=['Loc_ID', 'Name_Len'])

    # C. Universal Strip Mall Splitter
    df['Tenant_Count'] = df.groupby('Area_sqm')['Area_sqm'].transform('count')
    
    df['Final_Area_sqm'] = df.apply(
        lambda x: x['Area_sqm'] / x['Tenant_Count'] if x['Tenant_Count'] > 1 and x['Area_sqm'] > 5000 else x['Area_sqm'], 
        axis=1
    )
    
    df['Data_Source'] = df.apply(
        lambda x: x['Data_Source'] + f" (Split w/ {x['Tenant_Count']-1} tenants)" if x['Tenant_Count'] > 1 and x['Area_sqm'] > 5000 else x['Data_Source'],
        axis=1
    )

    # Clean Export
    final_cols = ['Name', 'Brand', 'Latitude', 'Longitude', 'Height_m', 'Num_Floors', 'Final_Area_sqm', 'Data_Source']
    df_final = df[final_cols].rename(columns={'Final_Area_sqm': 'Area_sqm'})
    
    output_path = "Universal_Building_Inventory.csv"
    df_final.to_csv(output_path, index=False)
    
    yield f"✅ Success! Found {len(df_final)} unique commercial assets.", output_path

# ==========================================
# GRADIO INTERFACE
# ==========================================
iface = gr.Interface(
    fn=process_data,
    inputs=gr.File(label="Upload Polygon (KML/KMZ) - - Limit 250, Sq KM area"),
    outputs=[
        gr.Textbox(label="Status Log"),
        gr.File(label="Download CSV")
    ],
    title="🌎 Universal Commercial Building Scanner - Test Phase - For Nokia",
    description="Upload any KML/KMZ. Scans for 50+ Big Box Brands, get Area Height/floors. Using Places API, Solar API and OpenStreetMaps API"
)

iface.launch()