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import streamlit as st
import os
from pinecone import Pinecone
from sentence_transformers import SentenceTransformer
# import torch
# device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = SentenceTransformer('intfloat/e5-small')
# Set up the Streamlit app
st.set_page_config(page_title="Search Engine", layout="wide")
# Set up the Streamlit app title and search bar
st.title("Search Engine")
with st.sidebar:
with st.form("my_form"):
st.write("Login to Search Engine")
index_name = st.text_input("Enter a database name:", "")
key = st.text_input("Enter a key:", "")
namespace = st.text_input("Enter a table name:", "")
# slider_val = st.slider("Form slider")
# checkbox_val = st.checkbox("Form checkbox")
# Every form must have a submit button.
submitted = st.form_submit_button("Connect to My Search Engine")
if submitted:
# if st.button("Connect to Search Engine Database", type="primary"):
# index_name = st.text_input("Enter a database name:", "")
# key = st.text_input("Enter a key:", "")
# namespace = st.text_input("Enter a table name:", "")
# # initialize connection to pinecone (get API key at app.pinecone.io)
api_key = os.environ.get('PINECONE_API_KEY') or key
# configure client
pc = Pinecone(api_key=api_key)
from pinecone import ServerlessSpec
cloud = os.environ.get('PINECONE_CLOUD') or 'aws'
region = os.environ.get('PINECONE_REGION') or 'us-east-1'
spec = ServerlessSpec(cloud=cloud, region=region)
# connect to index
index = pc.Index(index_name)
st.write('Successfully connected to your Search Engine DB!')
st.write('Start searching...')
query = st.text_input("Enter a search query:", "")
# If the user has entered a search query, search the Pinecone index with the query
if query:
# Upsert the embeddings for the query into the Pinecone index
query_embeddings = model.encode(query).tolist()
# now query
xc = index.query(vector=query_embeddings, top_k=10, namespace=namespace, include_metadata=True)
# Display the search results
st.write(f"Search results for '{query}':")
for result in xc['matches']:
st.write(f"{round(result['score'], 2)}: {result['metadata']['meta_text']}") |