Update app.py

app.py

@@ -1,12 +1,17 @@
+import os
 import pandas as pd
 import numpy as np
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics.pairwise import cosine_similarity
-from transformers import pipeline, HfApi, HfFolder
+from transformers import pipeline
 import gradio as gr
 
-# Hugging Face login - Ensure that you are logged into Hugging Face before running this space
-HfFolder.save_token("<your_hugging_face_token>")  # This is set once to avoid token exposure in the code
+# Access the Hugging Face token from the environment variable
+HF_TOKEN = os.getenv("HF_Token")
+
+# Ensure the token is loaded correctly (optional)
+if not HF_TOKEN:
+    raise ValueError("Hugging Face token not found. Please set it as a secret.")
 
 # Load and preprocess the data
 def preprocess_data(file_path):
@@ -67,7 +72,7 @@ def main_pipeline(file_path, user_query):
     retrieved_text = "\n".join(results['Knowledge_Text'].tolist())
 
     # Use Llama3.2 for question answering with prompt engineering
-    llm = pipeline("text-generation", model="meta-llama/Llama-3.2-1B-Instruct")
+    llm = pipeline("text-generation", model="meta-llama/Llama-3.2-1B-Instruct", use_auth_token=HF_TOKEN)
     prompt = (
         f"You are an expert sports analyst. Based on the following training data, provide a detailed and insightful answer to the user's question. "
         f"Always include relevant numerical data in your response. Limit your response to a maximum of 200 words.\n\n"