app.py

import streamlit as st
import os
from pinecone import Pinecone
# Set up the Streamlit app
st.set_page_config(page_title="Hotel Search", page_icon=":hotel:", layout="wide")

# Set up the Streamlit app title and search bar
st.title("Hotel Search")
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)


from sentence_transformers import SentenceTransformer
# import torch

# device = 'cuda' if torch.cuda.is_available() else 'cpu'

model = SentenceTransformer('intfloat/e5-small')


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']}")