Update app.py

app.py

@@ -1,17 +1,30 @@
 import gradio as gr
 from langchain_community.vectorstores import Chroma
-from langchain_community.embeddings import HuggingFaceEmbeddings
+from dotenv import load_dotenv
+from langchain_huggingface import HuggingFaceEmbeddings, HuggingFaceEndpoint
 from langchain_community.llms import HuggingFaceHub
 from langchain.chains import ConversationalRetrievalChain
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.memory import ConversationBufferMemory
 from langchain_community.document_loaders import PyPDFLoader
 
+load_dotenv()
+
+# Get the Hugging Face API token from the .env file
+hf_api_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+
+if hf_api_token is None:
+    raise ValueError("HUGGINGFACEHUB_API_TOKEN not found in .env file")
+
 # Initialize the Hugging Face embedding model
 embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
 
-# Initialize the LLaMA 2 model from Hugging Face Hub
-llm = HuggingFaceHub(repo_id="meta-llama/Llama-2-7b-hf", model_kwargs={"temperature": 0.7, "max_length": 512})
+# Initialize the LLaMA 2 model from Hugging Face Hub using the token from .env
+llm = HuggingFaceEndpoint(
+    repo_id="meta-llama/Llama-2-7b-hf",
+    model_kwargs={"temperature": 0.7, "max_length": 512},
+    huggingfacehub_api_token=hf_api_token
+)
 
 # Initialize ChromaDB for storing and retrieving document embeddings
 vectorstore = Chroma(embedding_function=embedding_model, persist_directory="chroma_db")