Update app.py

app.py

@@ -60,7 +60,6 @@ def process_csv_files(
     max_tokens,
     temperature,
     top_p,
-    request: gr.Request,
     progress=gr.Progress()
 ):
     """
@@ -69,27 +68,12 @@ def process_csv_files(
     if not files or len(files) == 0:
         return "Please upload at least one CSV file.", None
     
-    # Try to get token from the request (when user is logged in)
+    # Get HF token from environment variables
     import os
-    hf_token = None
+    hf_token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_TOKEN")
     
-    # Check if user is logged in via OAuth
-    if request:
-        # Try to get token from request headers or OAuth
-        try:
-            # In HF Spaces with OAuth, token is available in request
-            if hasattr(request, 'username') and request.username:
-                # User is logged in, try to get their token from environment
-                hf_token = os.environ.get("OAUTH_TOKEN") or os.environ.get("HF_TOKEN")
-        except:
-            pass
-    
-    # Fallback to environment variables
     if not hf_token:
-        hf_token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_TOKEN")
-    
-    if not hf_token:
-        return "❌ Error: Please login with your Hugging Face account using the button in the sidebar, or add your HF token as a Space Secret (HF_TOKEN).", None
+        return "❌ Error: HF_TOKEN not found. Please add your Hugging Face token as a Space Secret.\n\nGo to Space Settings → Secrets → Add 'HF_TOKEN'", None
     
     client = InferenceClient(token=hf_token, model="meta-llama/Llama-3.3-70B-Instruct")
     
@@ -186,20 +170,17 @@ with gr.Blocks(title="Business Category Description Generator") as demo:
     CLIP-ready visual descriptions for each category using AI.
     
     **Instructions:**
-    1. **Login** with your Hugging Face account (use the Login button below)
-    2. Upload one or more CSV files
-    3. Specify the column name that contains the category keywords
+    1. Upload one or more CSV files
+    2. Specify the column name that contains the category keywords
+    3. Adjust model settings if needed (optional)
     4. Click "Process Files" to generate descriptions
     5. Download the output CSV files
     
-    *Note: You need a Hugging Face account with API access. Pro accounts recommended for best performance.*
+    *Note: Authentication is handled via HF_TOKEN secret configured in Space settings.*
     """)
     
     with gr.Row():
         with gr.Column(scale=1):
-            gr.Markdown("### 🔐 Authentication")
-            gr.LoginButton()
-            
             gr.Markdown("### ⚙️ Model Settings")
             max_tokens = gr.Slider(
                 minimum=64,