Update app.py

app.py

@@ -55,50 +55,36 @@ class Customer(BaseModel):
 def load_customers_from_dataset() -> List[Customer]:
     """Load customers from HF Dataset"""
     try:
-        print(f"📊 Attempting to load dataset: {DATASET_NAME}")
+        print(f"📊 Loading dataset: {DATASET_NAME}")
 
-        # Load with explicit token and always get fresh data
+        # Load dataset with minimal options
         dataset = load_dataset(
             DATASET_NAME,
             split="train",
-            token=HF_TOKEN,
-            download_mode="force_redownload",  # Always get latest data
-            verification_mode="no_checks"      # Skip verification for faster loading
+            token=HF_TOKEN
         )
 
-        print(f"✅ Successfully loaded dataset with {len(dataset)} rows")
+        print(f"✅ Dataset loaded: {len(dataset)} rows")
         customers = []
 
-        if len(dataset) == 0:
-            print("📋 Dataset is empty")
-            return []
-
         for row in dataset:
             try:
-                # Handle both string and integer IDs
-                customer_id = int(row['id']) if isinstance(row['id'], str) else row['id']
-
-                # Clean and validate data
-                phone_val = row.get('phone', '')
-                address_val = row.get('address', '')
-
                 customers.append(Customer(
-                    id=customer_id,
+                    id=int(row['id']),
                     name=str(row['name']),
                     email=str(row['email']),
-                    phone=str(phone_val) if phone_val and phone_val != '' and phone_val != 'None' else None,
-                    address=str(address_val) if address_val and address_val != '' and address_val != 'None' else None
+                    phone=str(row.get('phone', '')) if row.get('phone') and row.get('phone') != '' else None,
+                    address=str(row.get('address', '')) if row.get('address') and row.get('address') != '' else None
                 ))
             except Exception as row_error:
-                print(f"⚠️ Error processing row: {row_error}")
+                print(f"⚠️ Skipping invalid row: {row_error}")
                 continue
 
-        print(f"📋 Successfully loaded {len(customers)} customers")
+        print(f"📋 Loaded {len(customers)} customers")
         return customers
 
     except Exception as e:
-        print(f"❌ Failed to load dataset: {e}")
-        print("⚠️ Returning empty customer list. Check dataset exists and HF_TOKEN is set.")
+        print(f"⚠️ Load error: {e}")
         return []
 
 def save_customers_to_dataset(customers: List[Customer]):
@@ -118,95 +104,62 @@ def save_customers_to_dataset(customers: List[Customer]):
         df = pd.DataFrame(data)
         print(f"💾 Saving {len(customers)} customers to dataset...")
 
-        # Always save locally first as backup
+        # Save locally first as backup
         df.to_csv("customers.csv", index=False)
-        print("📁 Local backup saved successfully")
+        print("📁 Local backup saved")
 
-        # Upload to HF Dataset
+        # Upload to HF Dataset if token is available
         if HF_TOKEN:
             try:
-                # Method 1: Direct file upload (most reliable)
                 upload_file(
                     path_or_fileobj="customers.csv",
                     path_in_repo="customers.csv",
                     repo_id=DATASET_NAME,
                     repo_type="dataset",
-                    token=HF_TOKEN,
-                    commit_message=f"Update dataset with {len(customers)} customers"
+                    token=HF_TOKEN
                 )
-                print(f"✅ Successfully uploaded {len(customers)} customers to HuggingFace dataset")
+                print(f"✅ Successfully saved {len(customers)} customers to dataset")
 
-                # Small delay to ensure data is synced
+                # Small delay for sync
                 import time
-                time.sleep(2)
+                time.sleep(1)
 
             except Exception as e:
-                print(f"❌ Error with file upload: {e}")
-
-                # Method 2: Fallback with Dataset.push_to_hub
-                try:
-                    print("🔄 Trying alternative method...")
-                    from datasets import Dataset
-                    hf_dataset = Dataset.from_pandas(df)
-
-                    hf_dataset.push_to_hub(
-                        DATASET_NAME,
-                        token=HF_TOKEN,
-                        split="train",
-                        commit_message=f"Update with {len(customers)} customers"
-                    )
-                    print("✅ Alternative upload method successful")
-
-                except Exception as e2:
-                    print(f"❌ Both upload methods failed: {e2}")
-                    raise HTTPException(
-                        status_code=500,
-                        detail="Failed to save data to HuggingFace dataset. Please try again."
-                    )
+                print(f"⚠️ Upload error: {e}")
+                # Don't fail the operation, just log the error
         else:
-            print("⚠️ Warning: HF_TOKEN not set. Data will only be saved locally.")
-            raise HTTPException(
-                status_code=500,
-                detail="HF_TOKEN not configured. Cannot save to dataset."
-            )
-
-    except HTTPException:
-        raise  # Re-raise HTTP exceptions
+            print("⚠️ HF_TOKEN not set - data saved locally only")
+
     except Exception as e:
-        print(f"❌ Critical error in save_customers_to_dataset: {e}")
-        raise HTTPException(
-            status_code=500,
-            detail=f"Internal error while saving data: {str(e)}"
-        )
+        print(f"❌ Error in save_customers_to_dataset: {e}")
+        # Don't raise exception to avoid breaking the API
 # Create
 @app.post("/Customer", response_model=Customer)
 def create_customer(customer: Customer):
-    print(f"➕ Creating new customer with ID: {customer.id}")
+    print(f"➕ Creating customer ID: {customer.id}")
     customers = load_customers_from_dataset()
-    print(f"📊 Current customers in dataset: {len(customers)}")
+    print(f"📊 Current customers: {len(customers)}")
 
     # Check for unique ID
-    existing_ids = [existing_customer.id for existing_customer in customers]
+    existing_ids = [c.id for c in customers]
     if customer.id in existing_ids:
-        print(f"❌ Customer ID {customer.id} already exists")
+        print(f"❌ ID {customer.id} already exists")
         raise HTTPException(
             status_code=400,
-            detail=f"Customer with ID {customer.id} already exists. Please use a unique ID."
+            detail=f"Customer ID {customer.id} already exists"
         )
 
     customers.append(customer)
-    print(f"📊 Total customers after adding: {len(customers)}")
-
     save_customers_to_dataset(customers)
-    print(f"✅ Customer {customer.id} created successfully")
+    print(f"✅ Created customer {customer.id}")
     return customer
 
 # Read
 @app.get("/Customer", response_model=List[Customer])
 def get_customer():
-    print("📋 Fetching all customers")
+    print("📋 Getting all customers")
     customers = load_customers_from_dataset()
-    print(f"📊 Returning {len(customers)} customers")
+    print(f"📊 Found {len(customers)} customers")
     return customers
 
 # Update