File size: 1,666 Bytes
513fc07 ba02453 ffcc5fa ba02453 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 |
import streamlit as st
import pandas as pd
import numpy as np
from sentence_transformers import SentenceTransformer, util
from huggingface_hub import hf_hub_download
import os
st.set_page_config(page_title="ArXiv Expert Finder", page_icon="🔬", layout="wide")
st.title("ArXiv Expert Finder")
@st.cache_resource
def load_model():
return SentenceTransformer("intfloat/multilingual-e5-large-instruct", trust_remote_code=True)
@st.cache_data
def load_data():
parquet_path = hf_hub_download(
repo_id="jadenhoch/jina-embeddings-v4",
filename="arxiv_2025_zstd.parquet",
repo_type="space"
)
npy_path = hf_hub_download(
repo_id="jadenhochh/multilingual-e5-large-instruct_2",
filename="corpus_embeddings_multilingual-e5-large-instruct_2.npy",
repo_type="dataset"
)
return pd.read_parquet(parquet_path), np.load(npy_path)
model = load_model()
df, corpus_embeddings = load_data()
top_k = st.sidebar.slider("Number of results", 1, 20, 6)
query = st.text_area("🔍 Text eingeben:", height=200)
if st.button("Suchen") and query:
query_emb = model.encode(query, convert_to_tensor=True, normalize_embeddings=True)
results = util.semantic_search(query_emb, corpus_embeddings, top_k=top_k)[0]
for rank, hit in enumerate(results, 1):
idx = hit["corpus_id"]
st.markdown(f"### {rank} | Similarity Score: {hit['score']:.4f} | Index: {idx}")
st.write(f"**Autoren:** {df.iloc[idx]['authors']}")
st.write(f"**Titel:** {df.iloc[idx]['title']}")
with st.expander("Abstract"):
st.write(df.iloc[idx]['abstract'])
st.divider() |