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main.py
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main.py
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import os
import streamlit as st
import pickle
import time
from langchain_openai import OpenAI
from langchain.chains import RetrievalQAWithSourcesChain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import UnstructuredURLLoader
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores.faiss import FAISS
from dotenv import load_dotenv
load_dotenv() # take environment variables from .env (especially openai api key)
st.title("Financial News Querying Tool 📈")
st.sidebar.title("News Article URLs")
urls = []
for i in range(3):
url = st.sidebar.text_input(f"URL {i+1}")
urls.append(url)
process_url_clicked = st.sidebar.button("Process URLs")
main_placeholder = st.empty()
llm = OpenAI(temperature=0.9, max_tokens=500)
if process_url_clicked:
# load data
loader = UnstructuredURLLoader(urls=urls)
main_placeholder.text("Data Loading...Started...✅✅✅")
data = loader.load()
# split data
text_splitter = RecursiveCharacterTextSplitter(
separators=['\n\n', '\n', '.', ','],
chunk_size=1000
)
main_placeholder.text("Text Splitter...Started...✅✅✅")
docs = text_splitter.split_documents(data)
# Create embeddings and save it to FAISS index
embeddings = OpenAIEmbeddings()
vectorstore_openai = FAISS.from_documents(docs, embeddings)
main_placeholder.text("Embedding Vector Started Building...✅✅✅")
# Save the vectorstore object locally
vectorstore_openai.save_local("vectorstore")
query = main_placeholder.text_input("Question: ")
if len(query)!=0:
# Load the vectorstore object
vectorstore = FAISS.load_local("vectorstore", OpenAIEmbeddings())
chain = RetrievalQAWithSourcesChain.from_llm(llm=llm, retriever=vectorstore.as_retriever())
result = chain({"question": query}, return_only_outputs=True)
# result will be a dictionary of this format --> {"answer": "", "sources": [] }
st.header("Answer")
st.write(result["answer"])
# Display sources, if available
sources = result.get("sources", "")
if sources:
st.subheader("Sources:")
sources_list = sources.split("\n") # Split the sources by newline
for source in sources_list:
st.write(source)
else:
st.write("Please click on the \'Process URLs\' button if you\'ve entered/updated your question")