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import streamlit as st
import llama_index
import logging
import sys
import openai
import os
import wikipedia
from llama_index import VectorStoreIndex, SimpleDirectoryReader
from llama_index.indices.query.query_transform import HyDEQueryTransform
from llama_index.query_engine.transform_query_engine import TransformQueryEngine
# from llama_index.indices.vector_store import ChatGPTRetrievalPluginIndex
from llama_index.readers import ChatGPTRetrievalPluginReader
def get_wikipedia_document(topic):
wiki_wiki = wikipedia.Wikipedia('en')
page = wiki_wiki.page(topic)
if page.exists():
return page.text
else:
return None
def write_string_to_file(text, filename):
with open(filename, 'w') as file:
file.write(text)
def ui():
api_key = st.text_input('Enter your OpenAI key here: ')
if api_key is not None:
os.environ["OPENAI_API_KEY"] = api_key
openai.api_key = os.environ["OPENAI_API_KEY"]
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
topic_name= st.text_input('Enter your topic for Wikipedia: ')
if topic_name is not None:
page_object = wikipedia.page(topic_name)
content = page_object.content
filename = f"./Data/{topic_name}.txt"
write_string_to_file(content, filename)
documents = SimpleDirectoryReader(f'./Data/{topic_name}.txt"').load_data()
index = VectorStoreIndex.from_documents(documents)
query_str= st.text_input('Enter your query for the document: ')
if query_str is not None:
query_engine = index.as_query_engine()
response = query_engine.query(query_str)
hyde = HyDEQueryTransform(include_original=True)
hyde_query_engine = TransformQueryEngine(query_engine, hyde)
response = hyde_query_engine.query(query_str)
query_bundle = hyde(query_str)
hyde_doc = query_bundle.embedding_strs[0]
st.text(hyde_doc)
if __name__=="__main__":
ui()
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