Delete app.py

app.py

@@ -1,346 +0,0 @@
-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 POLISH)
-# ==========================================
-
-# Filters out Schools, Doctors, Industrial services, 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",
-    # 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"
-]
-
-# Filters out departments inside Big Box stores
-
-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"
-]
-
-# ==========================================
-# 2. COMPREHENSIVE STORE LIST
-# ==========================================
-# This covers every variation you requested (Walmart, Ikea, Dicks, BJs, etc)
-SEARCH_LIST = [
-    # Big Box / Department
-    "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 Improvement / Hardware
-    "Home Depot", "Lowe's", "Ace Hardware", "Menards",
-    
-    # Electronics / Office
-    "Best Buy", "Office Depot", "OfficeMax", "Staples",
-    
-    # Furniture
-    "IKEA", "Bob's Discount Furniture", "Raymour & Flanigan", "Ashley Furniture",
-    
-    # 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
-}
-
-# ==========================================
-# 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 250,000,000 Sq meters area"),
-    outputs=[
-        gr.Textbox(label="Status Log"),
-        gr.File(label="Download CSV")
-    ],
-    title="🌎 Universal Commercial Asset Scanner",
-    description="Upload any KML/KMZ. Scans for 50+ Big Box Brands, strip malls. Using Places API, Solar API and OpenStreetMaps API"
-)
-
-iface.launch()