Create app.py

app.py

@@ -0,0 +1,281 @@
+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
+
+# ==========================================
+# 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:
+        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)
+            
+            # --- UNIVERSAL FIX: FORCE 2D ---
+            # This ensures ANY KML with height data works correctly
+            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
+
+# DATA CONSTANTS
+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
+}
+
+SEARCH_LIST = [
+    "Walmart", "Target", "Kmart", "Sears", "Kohl's", "Macy's", "JCPenney", 
+    "TJX", "TJX Companies", "T.J. Maxx", "Marshalls", "HomeGoods", "HomeSense", 
+    "Ross", "Ross Dress for Less", "Burlington", "Dick's Sporting Goods", 
+    "Albertsons", "Safeway", "Home Depot", "Lowe's", "Best Buy", 
+    "IKEA", "Bob's Furniture", "Bob's Discount Furniture", "Raymour & Flanigan", 
+    "Barnes & Noble", "Office Depot", "OfficeMax", "Staples", "Lowe",
+    "PetSmart", "Petco", "Kroger", "Meijer", "Costco", "BJ's Wholesale Club", 
+    "Sam's Club", "Whole Foods", "ShopRite", "Stop & Shop", "Trader Joe's", 
+    "Michaels", "Lidl", "Aldi", "DSW Designer Shoe Warehouse", "Old Navy", 
+    "Ace", "Ace Hardware", "Hobby Lobby", "Trader Joes"
+]
+
+# ==========================================
+# MAIN LOGIC WITH GENERATOR (YIELD)
+# ==========================================
+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
+
+    # --- UNIVERSAL AREA LIMIT CHECK ---
+    try:
+        gs = gpd.GeoSeries([polygon], crs="EPSG:4326")
+        gs_proj = gs.to_crs(epsg=6933)
+        area_sq_meters = gs_proj.area.iloc[0]
+        limit_sq_meters = 250_000_000
+        
+        if area_sq_meters > limit_sq_meters:
+            yield f"⚠️ AREA TOO LARGE: {area_sq_meters:,.0f} sq m. (Limit: {limit_sq_meters:,.0f}). Upload a smaller file.", None
+            return 
+    except:
+        pass
+
+    gmaps = googlemaps.Client(key=API_KEY)
+    results = []
+    seen_ids = set()
+    total_brands = len(SEARCH_LIST)
+    
+    # LOOP THROUGH BRANDS
+    for i, brand in enumerate(SEARCH_LIST):
+        yield f"🔍 Scanning Brand {i+1}/{total_brands}: {brand}...", None
+        
+        try:
+            # --- 1. DEEP SEARCH (Pagination enabled) ---
+            # 10km radius + 3 Pages of results ensures we don't miss local stores
+            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')
+                
+                # --- 2. UNIVERSAL NAME VERIFICATION ---
+                # Check: Is the brand name actually inside the store name?
+                # This prevents "Meijer" showing up when searching for "Walmart"
+                
+                # Normalize strings (remove case, apostrophes, periods)
+                name_clean = name.lower().replace("'", "").replace(".", "")
+                brand_clean = brand.lower().replace("'", "").replace(".", "")
+
+                if brand_clean not in name_clean:
+                    # Exceptions for tricky names (TJX/Lowe)
+                    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 # Reject the result
+                
+                # --- 3. BAD KEYWORD FILTER ---
+                # Filters out ATMs, Doctors, Fuel, Vision Centers, etc.
+                bad_terms = ["atm", "redbox", "kiosk", "coinme", "gas", "fuel", "lcsw", "dr.", "dds", "hair", "salon", "studio", "tire", "repair"]
+                if any(term in name_clean for term in bad_terms): continue
+                
+                lat = p['geometry']['location']['lat']
+                lng = p['geometry']['location']['lng']
+                
+                # --- 4. STRICT CONTAINMENT ---
+                # Only keep if strictly inside the KML polygon
+                if not polygon.contains(Point(lng, lat)): continue
+                seen_ids.add(pid)
+                
+                # Get Data
+                roof_area = get_roof_area(lat, lng, API_KEY)
+                height, floors = get_osm_physics(lat, lng)
+                
+                # Logic
+                source_note = "SolarAPI"
+                if roof_area is None:
+                    roof_area = BRAND_AVG_AREA.get(brand, 2500.0)
+                    source_note = "Brand_Avg (Missing)"
+                else:
+                    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", "GameStop"]:
+                        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
+
+    df = pd.DataFrame(results)
+    
+    # --- FINAL CLEANUP (Sub-departments) ---
+    # Double check to remove things like "Walmart Vision Center"
+    bad_keywords = ['Mobile', 'Salon', 'Floral', 'Bakery', 'Pharmacy', 'Optical', 'Geek Squad', 'Photo', 'Tire', 'Vision']
+    df = df[~df['Name'].str.contains('|'.join(bad_keywords), case=False, na=False)]
+    
+    output_path = "Building_Inventory.csv"
+    df.to_csv(output_path, index=False)
+    
+    yield f"✅ Success! Found {len(df)} stores.", output_path
+
+# ==========================================
+# GRADIO INTERFACE
+# ==========================================
+iface = gr.Interface(
+    fn=process_data,
+    inputs=gr.File(label="Upload Polygon (Limit: 250,000,000 sq m)"),
+    outputs=[
+        gr.Textbox(label="Status Log"),
+        gr.File(label="Download CSV")
+    ],
+    title="🏙️ Commercial Building Inventory Generator",
+    description="Upload a KMZ file. The tool will scan for major Big Box brands, check Solar API for area, and OpenStreetMap for height/floors."
+)
+
+iface.launch()