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import os
import streamlit as st
import pickle
import time
from langchain import OpenAI
from langchain.chains import RetrievalQAWithSourcesChain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import UnstructuredURLLoader
# from langchain.embeddings import OpenAIEmbeddings
from langchain.embeddings import FakeEmbeddings
from langchain.llms import HuggingFaceHub
from langchain.chains import LLMChain
from langchain.vectorstores import FAISS
from dotenv import load_dotenv
# load_dotenv() # take environment variables from .env (especially openai api key)
os.environ["HUGGINGFACEHUB_API_TOKEN"] = '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'
st.title("RockyBot: News Research 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")
file_path = "faiss_store_openai.pkl"
main_placeholder = st.empty()
llm = HuggingFaceHub( repo_id="google/flan-t5-xxl", model_kwargs={"temperature": 0.5, "max_length": 64} )
@st.cache
def process_urls(urls):
"""Processes the given URLs and saves the FAISS index to a pickle file."""
# load data
loader = UnstructuredURLLoader(urls=urls)
# split data
text_splitter = RecursiveCharacterTextSplitter(
separators=['\n\n', '\n', '.', ','],
chunk_size=1000
)
docs = text_splitter.split_documents(loader.load())
# create embeddings and save it to FAISS index
embeddings = FakeEmbeddings(size=1352)
vectorstore_openai = FAISS.from_documents(docs, embeddings)
# Save the FAISS index to a pickle file
with open(file_path, "wb") as f:
pickle.dump(vectorstore_openai, f)
if process_url_clicked:
with st.progress(0.0):
process_urls(urls)
st.progress(100.0)
query = main_placeholder.text_input("Question: ")
if query:
try:
with open(file_path, "rb") as f:
vectorstore = pickle.load(f)
chain = RetrievalQAWithSourcesChain.from_llm(llm=llm, retriever=vector_store.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)
except Exception as e:
st.error(e)
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