Update app.py

app.py

@@ -1,13 +1,19 @@
+import os
+from huggingface_hub import login
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 import gradio as gr
 import pandas as pd
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 import torch
 
+# Get Hugging Face token securely
+HUGGINGFACE_TOKEN = os.getenv("HF_TOKEN")  # ✅ Fetch from environment variable
+login(HUGGINGFACE_TOKEN)
+
 # Load Llama-2 Model & Tokenizer
 model_name = "meta-llama/Llama-2-7b-chat-hf"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=True)
 model = AutoModelForCausalLM.from_pretrained(
-    model_name, torch_dtype=torch.float16, device_map="auto"
+    model_name, torch_dtype=torch.float16, device_map="auto", use_auth_token=True
 )
 pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
 
@@ -24,13 +30,12 @@ def analyze_csv(file):
     
     return analysis_result  # Return the analysis
 
-# Gradio Interface for CSV Upload and Analysis
+# Gradio Interface
 iface = gr.Interface(
     fn=analyze_csv,
     inputs=gr.File(label="Upload CSV File"),
     outputs="text",
-    title="Llama-2 CSV Benchmark Analyzer",
-    description="Upload a CSV file with benchmark scores, and Llama-2 will analyze and recommend the best model."
+    title="Llama-2 CSV Benchmark Analyzer"
 )
 
 iface.launch()