Update app.py

app.py

@@ -1,10 +1,14 @@
 import gradio as gr
-from langchain.document_loaders import PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings import OpenAIEmbeddings
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_huggingface import HuggingFaceEmbeddings
 from langchain.vectorstores import FAISS
 from langchain.chains import RetrievalQA
-from langchain.llms import OpenAI
+from langchain_groq import ChatGroq
+from langchain_core.prompts import PromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+
 
 # Initialize the FAISS vector store
 vector_store = None
@@ -16,15 +20,17 @@ def index_pdf(pdf):
     # Load the PDF
     loader = PyPDFLoader(pdf.name)
     documents = loader.load()
-    
+
     # Split the documents into chunks
     text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
     texts = text_splitter.split_documents(documents)
-    
-    # Embed the chunks and store them in the vector store
-    embeddings = OpenAIEmbeddings()
+
+    # Embed the chunks 
+    embeddings = HuggingFaceEmbeddings(model_name="bert-base-uncased", encode_kwargs={"normalize_embeddings": True})
+
+    # Store the embeddings in the vector store
     vector_store = FAISS.from_documents(texts, embeddings)
-    
+
     return "PDF indexed successfully!"
 
 # Function to handle chatbot queries