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import streamlit as st
import pinecone
from makechain import get_chain
from langchain.vectorstores.pinecone import Pinecone
from langchain.embeddings.openai import OpenAIEmbeddings
import os
PINECONE_INDEX_NAME = os.environ.get("PINECONE_INDEX_NAME")
PINECONE_ENVIRONMENT = os.environ.get("PINECONE_ENVIRONMENT")
PINECONE_API_KEY = os.environ.get("PINECONE_API_KEY")
OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
st.title("Ask the Black@Stanford Exhibit")
st.sidebar.header("You can ask questions of interviews with Black Stanford students and faculty from the University "
"Archives")
st.sidebar.info(
'''This is a web application that allows you to interact with
the Stanford Archives.
Enter a **Question** in the **text box** and **press enter** to receive
a **response** from our ChatBot.
'''
)
# create Vectorstore
pinecone.init(
api_key=PINECONE_API_KEY, # find at app.pinecone.io
environment=PINECONE_ENVIRONMENT # next to api key in console
)
index = pinecone.Index(index_name=PINECONE_INDEX_NAME)
embed = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
text_field = "text"
vectorStore = Pinecone(
index, embed.embed_query, text_field
)
# create chain
qa_chain = get_chain(vectorStore)
def main():
global query
user_query = st.text_input("Enter your question here")
if user_query != ":q" and user_query != "":
# Pass the query to the ChatGPT function
query = user_query.strip().replace('\n', ' ')
response = qa_chain(
{
'query': query,
}
)
st.write(f"{response['result']}")
st.write("Sources: ")
documents = response['source_documents']
for document in documents:
page_content = document.page_content
source_url = document.metadata['source']
st.write("Page Content")
st.write(page_content)
st.write("Source URL:")
st.write(source_url)
st.markdown("""---""")
try:
main()
except Exception as e:
st.write("An error occurred while running the application: ", e)
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