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import streamlit as st
import openai
import pinecone
PINECONE_API_KEY = st.secrets["PINECONE_API_KEY"]
OPENAI_API_KEY = st.secrets["OPENAI_API_KEY"]
INDEX_NAME = 'realvest-data-v2'
EMBEDDING_MODEL = "text-embedding-ada-002" # OpenAI's best embeddings as of Apr 2023
### Pinecone
# initialize connection to pinecone (get API key at app.pinecone.io)
pinecone.init(
api_key=PINECONE_API_KEY,
environment="us-central1-gcp" # may be different, check at app.pinecone.io
)
index = pinecone.Index(INDEX_NAME)
### Main
# Create a text input field
query = st.text_input("What are you looking for?")
# Create a button
if st.button('Submit'):
# Display a response when the button is pressed
# st.text("Hi, {}".format(query))
print('click Submit')
### text-embedding
res = openai.Embedding.create(model=EMBEDDING_MODEL, input=[query], api_key=OPENAI_API_KEY)
st.json(res)
xq = res['data'][0]['embedding']
### query VectorDB
out = index.query(xq, top_k=3, include_metadata=True)
### display
st.json(out)
# st.write(stats)
# from tqdm.autonotebook import tqdm
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