Update app.py

app.py

@@ -5,8 +5,9 @@ import numpy as np
 from dotenv import load_dotenv
 import gradio as gr
 import logging
+from huggingface_hub import HfApi, HfFolder
 
-# Load environment variables (optional if you want to keep the dotenv usage)
+# Load environment variables
 load_dotenv()
 
 # Define OpenAI-based model
@@ -72,6 +73,15 @@ class LocalEmbeddingStore:
         print(f"Search results: {results}")  # Debugging: Print results to check for any issues
         return results['documents'], results['distances']
 
+    def add_embedding(self, document_id, embedding, metadata=None):
+        """Add a new embedding to the collection."""
+        self.collection.add(
+            ids=[document_id],
+            embeddings=[embedding.tolist()],
+            metadatas=[metadata] if metadata else None
+        )
+        print(f"Added embedding for document ID: {document_id}")
+
 # Modify RAGSystem to integrate ChromaDB search
 class RAGSystem:
     def __init__(self, openai_client, embedding_store):
@@ -133,4 +143,21 @@ with gr.Blocks() as demo:
     submit_button.click(chat_ui, inputs=[user_input, api_key_input, chat_history], outputs=chat_history)
 
 if __name__ == "__main__":
+    # Load Hugging Face token from environment variables
+    hf_token = os.getenv("HUGGINGFACE_TOKEN")
+    if not hf_token:
+        raise ValueError("Hugging Face token not found in environment variables.")
+
+    # Set up the Hugging Face token for any necessary API calls
+    os.environ["HUGGINGFACE_TOKEN"] = hf_token
+
+    # Verify token permissions (optional)
+    try:
+        api = HfApi()
+        api.whoami(token=hf_token)
+        print("Hugging Face token is valid and has the necessary permissions.")
+    except Exception as e:
+        print(f"Error verifying Hugging Face token: {e}")
+        raise ValueError("Invalid Hugging Face token or insufficient permissions.")
+
     demo.launch()