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| """ | |
| Basic similarity search example. | |
| """ | |
| import os | |
| import streamlit as st | |
| from sentence_transformers import SentenceTransformer, util | |
| # Write directly to the app | |
| st.title("IRIS - User Experience: : Getting the end-user to choose a similar cached question ") | |
| st.write( | |
| """Type your question! | |
| System will display **most similar questions** | |
| . | |
| """ | |
| ) | |
| st.text_input("Type your question here", key="userquery") | |
| #model = SentenceTransformer("all-MiniLM-L6-v2") | |
| model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2') | |
| listofCachedItems = ["what was the revenue for FIFA 23", "what was the revenue for ApexLegends", "What was the revenue for FIFA 23 in Aug 2023", "What was the revenue for ApexLegends in Aug 2023"] | |
| emb1 = model.encode(st.session_state.userquery ) | |
| maxscore = 0 | |
| bestmatch = "" | |
| for i in listofCachedItems: | |
| emb2 = model.encode(i) | |
| cos_sim = util.cos_sim(emb1, emb2) | |
| #print("Cosine-Similarity:" + str(cos_sim) + "\t\t Sentance " + str(i) ) | |
| if cos_sim > maxscore : | |
| maxscore = cos_sim | |
| bestmatch = i | |
| #print("Final Result:-") | |
| #print(bestmatch) | |
| #print(maxscore) | |
| #print(type(maxscore)) | |
| numericscore = maxscore[0].tolist() | |
| numericscore = numericscore[0] | |
| #print(numericscore) | |
| listofanswer = [] | |
| if numericscore > 0.95: | |
| # print(bestmatch) | |
| # print(maxscore) | |
| listofanswer.append(bestmatch) | |
| st.write("Found a similar question that is already precomputed") | |
| option = st.selectbox( 'We identified something similar. Try this?', listofanswer) | |
| else: | |
| st.write("That is a new question. There is no similar questions that is cached") | |