app.py

# streamlit_app.py
import streamlit as st
import pandas as pd
import torch
from sentence_transformers import SentenceTransformer, util
import pickle

# Load the first set of sentences & embeddings from disk
with open('clinical_inno_embeddings_masterid_paraphrase-multilingual-mpnet-base-v2.pkl', "rb") as fIn:
    stored_data_1 = pickle.load(fIn)
    stored_masterid_1 = stored_data_1['pro_master_id']
    stored_products_1 = stored_data_1['products']
    stored_embeddings_1 = stored_data_1['embeddings']

# Load the second set of sentences & embeddings from disk
# Replace 'other_embeddings.pkl' with your actual second embeddings file
with open('mean_clinical_inno_embeddings_masterid_paraphrase-multilingual-mpnet-base-v2.pkl', "rb") as fIn:
    stored_data_2 = pickle.load(fIn)
    stored_masterid_2 = stored_data_2['pro_master_id']
    stored_products_2 = stored_data_2['mean_products']
    stored_embeddings_2 = stored_data_2['mean_embeddings']

# Initialize the SentenceTransformer model
embedder = SentenceTransformer('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')

def get_similar_products(query, products, embeddings, top_k=10):
    query_embedding = embedder.encode(query, convert_to_tensor=True)
    cos_scores = util.cos_sim(query_embedding, embeddings)[0]
    top_results = torch.topk(cos_scores, k=top_k)
    
    similar_products = [(products[idx.item()], score.item()) for score, idx in zip(top_results[0], top_results[1])]
    return similar_products

# Streamlit UI
st.title("Product Similarity Finder")

# Embedding selection slider
embedding_option = st.select_slider(
    'Select Search Approach',
    options=['All Products', 'Master Products']
)

# Determine which embeddings to use based on the slider selection
if embedding_option == 'All Products':
    stored_products = stored_products_1
    st.write(len(stored_products))
    stored_embeddings = stored_embeddings_1
else:
    stored_products = stored_products_2
    st.write(len(stored_products))
    stored_embeddings = stored_embeddings_2

# User input
user_input = st.text_input("Enter a product name or description:")

# Search button
if st.button('Search'):
    if user_input:
        # Get and display similar products
        results = get_similar_products(user_input, stored_products, stored_embeddings)
        
        # Convert results to a DataFrame for nicer display
        results_df = pd.DataFrame(results, columns=['Product', 'Score'])
        
        # Use Streamlit's dataframe function to display results in a table with default formatting
        st.dataframe(results_df.style.format({'Score': '{:.4f}'}))
    else:
        st.write("Please enter a product name or description to search.")