Update app.py

app.py

@@ -3,8 +3,7 @@ import gradio as gr
 from langchain.chains import RetrievalQA
 from langchain_community.vectorstores import Chroma
 from langchain_community.llms import OpenAI  # ✅ Fixed Import
-from langchain_huggingface import HuggingFaceEndpoint  # ✅ Fixed Import
-from langchain_community.embeddings import OpenAIEmbeddings, HuggingFaceEmbeddings  # ✅ Fixed Import
+from langchain_huggingface import HuggingFaceEndpoint, HuggingFaceEmbeddings  # ✅ Fixed Import
 from langchain_community.document_loaders import PyPDFLoader  # ✅ Fixed Import
 import time
 
@@ -30,7 +29,7 @@ def load_data():
             loader = PyPDFLoader(pdf)
             all_docs.extend(loader.load())
 
-    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")  # ✅ Use updated embeddings
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")  # ✅ Updated import
 
     return Chroma.from_documents(all_docs, embeddings)
 
@@ -51,7 +50,7 @@ qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=vector_db.as_retriever
 # Function to simulate futuristic typing effect
 def chatbot_response(question):
     """Handles chatbot queries with a typing effect"""
-    response = qa_chain.invoke(question)  # ✅ Use invoke instead of deprecated `run`
+    response = qa_chain.invoke(question)  # ✅ Use `invoke` instead of deprecated `run`
     displayed_response = ""
     for char in response:
         displayed_response += char