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import streamlit as st
from sentence_transformers import SentenceTransformer
import datasets
import time
import faiss
if "initialized" not in st.session_state:
st.session_state.dataset = datasets.load_dataset('A-Roucher/english_historical_quotes', download_mode="force_redownload")['train']
st.session_state.all_authors = list(set(st.session_state.dataset['author']))
model_name = "BAAI/bge-small-en-v1.5" # "Cohere/Cohere-embed-english-light-v3.0" # "sentence-transformers/all-MiniLM-L6-v2"
st.session_state.encoder = SentenceTransformer(model_name)
st.session_state.index = faiss.read_index('index_alone.faiss')
st.session_state.initialized=True
def search(query):
start = time.time()
if len(query.strip()) == 0:
return ""
query_embedding = st.session_state.encoder.encode([query])
_, samples = st.session_state.index.search(
query_embedding, k=10
)
quotes = st.session_state.dataset.select(samples[0])
result = "\n\n"
for i in range(len(quotes)):
result += f"###### {quotes['author'][i]}\n> {quotes['quote'][i]}\n----\n"
delay = "%.3f" % (time.time() - start)
return f"_Computation time: **{delay} seconds**_{result}"
st.markdown(
"""
<style>
div[data-testid="column"]
{
align-self:flex-end;
}
</style>
""",unsafe_allow_html=True
)
st.markdown("# 🏛 Quotes 🪶\n\n_Great mind thinks alike_: who had the same ideas as you?\n\nType your idea below, and find similar thoughts from famous historical figures.")
col1, col2 = st.columns([8, 2])
text_input = col1.text_input("Type your idea here:", placeholder="Knowledge of history is power.")
submit_button = col2.button("_Search quotes!_")
if submit_button:
st.markdown(search(text_input)) |