Update app.py

app.py

@@ -1,24 +1,49 @@
-import openai
-from langchain.chains import RetrievalQA
-from langchain.chat_models import ChatOpenAI
-from langchain.document_loaders import CSVLoader
-from langchain.vectorstores import DocArrayInMemorySearch
-from IPython.display import display, Markdown
+# Q&A Chatbot
 from langchain.llms import OpenAI
-from langchain_community.document_loaders import Docx2txtLoader
-from langchain_community.document_loaders import UnstructuredWordDocumentLoader
-from langchain.indexes import VectorstoreIndexCreator
-from langchain_community.document_loaders import PyPDFLoader
-
 import os
 
 from dotenv import load_dotenv, find_dotenv
 _ = load_dotenv(find_dotenv()) # read local .env file
 
-os.getenv("OPENAI_API_KEY"))
+
+import streamlit as st
+import os
+
+
+os.getenv("OPENAI_API_KEY")
+
+## Function to load OpenAI model and get respones
 
 loader = PyPDFLoader("Firm_Data.pdf")
 index = VectorstoreIndexCreator(
     vectorstore_cls=DocArrayInMemorySearch
 ).from_loaders([loader])
 
+
+def get_openai_response(index, question):
+    llm_replacement_model = OpenAI(temperature=0,
+                               model='gpt-3.5-turbo-instruct')
+    response = index.query(question,
+                       llm = llm_replacement_model)
+    return response
+
+st.set_page_config(page_title="Ask Questions on S&P 500 companies financials")
+
+st.header("Langchain Application")
+
+input=st.text_input("Input: ",key="input")
+response=get_openai_response(input)
+
+submit=st.button("Ask the question")
+
+## If ask button is clicked
+
+if submit:
+    st.subheader("The Response is")
+    st.write(response)
+
+
+
+
+
+