Create app.py

app.py

@@ -0,0 +1,347 @@
+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 250,000,000 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()