Update app.py

app.py

@@ -1,17 +1,20 @@
 import streamlit as st
 import os
-from huggingface_hub import InferenceClient
+from langchain.llms import HuggingFacePipeline
 from langchain_community.document_loaders import PDFPlumberLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_core.vectorstores import InMemoryVectorStore
 from langchain_core.prompts import ChatPromptTemplate
 from langchain.embeddings import HuggingFaceEmbeddings
+from transformers import pipeline
 
 
 # Set up Hugging Face model and token
-model = "mistralai/Mixtral-8x7B-Instruct-v0.1"  # You can change to a model of your choice from Hugging Face
+model_name = "mistralai/Mixtral-8x7B-Instruct-v0.1"  # Change to your preferred model
 access_token = os.getenv("HF_TOKEN")  # Your Hugging Face API token
-client = InferenceClient(model=model, token=access_token)
+
+# Set up HuggingFace pipeline
+hf_pipeline = pipeline("text-generation", model=model_name, token=access_token)
 
 # Template for response generation
 template = """
@@ -26,7 +29,7 @@ pdfs_directory = '../pdfs'
 os.makedirs(pdfs_directory, exist_ok=True)
 
 # Initialize the embedding model
-embedding = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")  # You can choose any model from Hugging Face
+embedding = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")  # Choose any model
 
 # Initialize the vector store for document indexing
 vector_store = InMemoryVectorStore(embedding=embedding)
@@ -65,14 +68,11 @@ def answer_question(question, documents):
     full_context = f"{context}"
     prompt = ChatPromptTemplate.from_template(template)
     
-    # Use the prompt and send it directly to the Hugging Face model
-    question_with_context = prompt.format(question=question, context=full_context)
-    
-    # Use the client (InferenceClient) to get a response
-    response = client.query(question_with_context)
-    
-    return response["generated_text"]  # Assuming the response is in "generated_text"
+    # Use HuggingFacePipeline for generating responses
+    hf_chain = HuggingFacePipeline(pipeline=hf_pipeline)  # Wrap pipeline with HuggingFacePipeline
+    chain = prompt | hf_chain  # Send the prompt to Hugging Face model via HuggingFacePipeline
 
+    return chain.invoke({"question": question, "context": full_context})
 
 # Streamlit file uploader for PDF
 uploaded_file = st.file_uploader(