Update app.py

app.py

@@ -1,23 +1,21 @@
 import os
-from huggingface_hub import login
 from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 import gradio as gr
 import pandas as pd
 import torch
 
-# Get Hugging Face token securely
-HUGGINGFACE_TOKEN = os.getenv("HF_TOKEN")  # ✅ Fetch from environment variable
-login(HUGGINGFACE_TOKEN)
+# ✅ Get Hugging Face token securely from Space Secrets
+HUGGINGFACE_TOKEN = os.getenv("HF_TOKEN")  # Make sure you set this in HF Secrets!
 
-# Load Llama-2 Model & Tokenizer
+# ✅ Load Llama-2 Model & Tokenizer (without login())
 model_name = "meta-llama/Llama-2-7b-chat-hf"
-tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=True)
+tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=HUGGINGFACE_TOKEN)
 model = AutoModelForCausalLM.from_pretrained(
-    model_name, torch_dtype=torch.float16, device_map="auto", use_auth_token=True
+    model_name, torch_dtype=torch.float16, device_map="auto", use_auth_token=HUGGINGFACE_TOKEN
 )
 pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
 
-# Function to analyze CSV data
+# ✅ Function to analyze CSV data
 def analyze_csv(file):
     df = pd.read_csv(file.name)  # Read uploaded CSV
     benchmark_text = df.to_string()  # Convert DataFrame to text
@@ -30,7 +28,7 @@ def analyze_csv(file):
     
     return analysis_result  # Return the analysis
 
-# Gradio Interface
+# ✅ Gradio Interface
 iface = gr.Interface(
     fn=analyze_csv,
     inputs=gr.File(label="Upload CSV File"),