Upload E-Commerce Product Intelligence Dashboard (frontend + backend)

backend/app.py

@@ -13,16 +13,13 @@ logger = logging.getLogger(__name__)
 
 app = FastAPI(title="E-Commerce Product Intelligence Platform")
 
-# HF Dataset config
 HF_DATASET_ID = "Vincentran/ecommerce-dataset"
 HF_CSV_PATH = "data/ecommerce_products.csv"
 
-# Cache DataFrame
 _data_cache = None
 
 
 def load_data():
-    """Load CSV từ HF Dataset with cache."""
     try:
         if _data_cache is not None:
             logger.info("Using cached DataFrame")
@@ -44,7 +41,6 @@ def load_data():
         df = pd.read_csv(local_csv_path)
         logger.info(f"Loaded {len(df)} rows, columns: {list(df.columns)}")
 
-        # Cache DataFrame
         _data_cache = df
         return df
 
@@ -54,7 +50,6 @@ def load_data():
 
 
 def refresh_cache():
-    """Refresh data cache."""
     _data_cache = None
     return load_data()
 
@@ -65,10 +60,7 @@ def root():
 
 
 @app.get("/data")
-def get_data(
-        page: int = Query(1, ge=1, description="Page number"),
-        limit: int = Query(100, ge=1, le=500, description="Items per page")
-):
+def get_data(page: int = Query(1, ge=1), limit: int = Query(100, ge=1, le=500)):
     df = load_data()
     total = len(df)
     start = (page - 1) * limit
@@ -123,7 +115,6 @@ def stats_rating():
 
 @app.get("/stats/price-range")
 def stats_price_range():
-    """Price distribution by range."""
     df = load_data()
     if "price" not in df.columns:
         raise HTTPException(status_code=400, detail="Missing 'price' column")
@@ -153,15 +144,10 @@ def insights():
 
 
 @app.get("/search")
-def search(
-        query: str = Query(..., description="Search query"),
-        page: int = Query(1, ge=1, description="Page number"),
-        limit: int = Query(100, ge=1, le=500, description="Items per page")
-):
+def search(query: str = Query(...), page: int = Query(1, ge=1), limit: int = Query(100, ge=1, le=500)):
     df = load_data()
     q = query.lower()
 
-    # Search only in important columns
     search_cols = ["product_name", "category", "brand", "description"]
     search_cols = [col for col in search_cols if col in df.columns]
 
@@ -192,16 +178,15 @@ def search(
 
 @app.get("/filter")
 def filter_products(
-        category: Optional[str] = Query(None, description="Filter by category"),
-        min_price: Optional[float] = Query(None, description="Min price"),
-        max_price: Optional[float] = Query(None, description="Max price"),
-        min_rating: Optional[float] = Query(None, description="Min rating"),
-        page: int = Query(1, ge=1, description="Page number"),
-        limit: int = Query(100, ge=1, le=500, description="Items per page")
+        category: Optional[str] = Query(None),
+        min_price: Optional[float] = Query(None),
+        max_price: Optional[float] = Query(None),
+        min_rating: Optional[float] = Query(None),
+        page: int = Query(1, ge=1),
+        limit: int = Query(100, ge=1, le=500)
 ):
     df = load_data()
 
-    # Apply filters
     if category and "category" in df.columns:
         df = df[df["category"] == category]
     if min_price and "price" in df.columns:
@@ -221,12 +206,7 @@ def filter_products(
     data = df.iloc[start:end].to_dict("records")
     return {
         "data": data,
-        "filters": {
-            "category": category,
-            "min_price": min_price,
-            "max_price": max_price,
-            "min_rating": min_rating
-        },
+        "filters": {"category": category, "min_price": min_price, "max_price": max_price, "min_rating": min_rating},
         "page": page,
         "limit": limit,
         "total": total,
@@ -235,7 +215,7 @@ def filter_products(
 
 
 @app.get("/recommend")
-def recommend(category: str, limit: int = Query(10, ge=1, le=50, description="Number of recommendations")):
+def recommend(category: str, limit: int = Query(10, ge=1, le=50)):
     df = load_data()
     if "category" not in df.columns:
         raise HTTPException(status_code=400, detail="Missing 'category' column")
@@ -252,7 +232,6 @@ def recommend(category: str, limit: int = Query(10, ge=1, le=50, description="Nu
 
 @app.post("/refresh-data")
 def refresh_data():
-    """Refresh data cache from HF Dataset."""
     try:
         df = refresh_cache()
         return {"status": "Data refreshed successfully", "rows": len(df)}
@@ -262,21 +241,19 @@ def refresh_data():
 
 @app.post("/run-scraper")
 def trigger_scraper():
-    """Trigger download Kaggle → save CSV → upload to HF."""
     import subprocess
     result = subprocess.run(["python", "backend/scraper.py"], capture_output=True, text=True)
     if result.returncode == 0:
-        # Refresh cache after scraper
         refresh_cache()
         return {"status": "Scraper completed successfully", "output": result.stdout}
     else:
         return {"status": "Scraper failed", "error": result.stderr}
 
 
-# Mount frontend
+# ✅ Mount frontend at /frontend (not /)
 frontend_dir = Path("frontend")
 if frontend_dir.exists():
-    app.mount("/", StaticFiles(directory=str(frontend_dir), html=True), name="frontend")
+    app.mount("/frontend", StaticFiles(directory=str(frontend_dir), html=True), name="frontend")
 else:
     @app.get("/")
     def frontend_placeholder():