app.py

import faiss
import pickle
import datasets
import numpy as np
import requests
import streamlit as st
from vector_engine.utils import vector_search 
from transformers import AutoModel, AutoTokenizer

from datasets import load_dataset

#@st.cache
@st.cache_data
def read_data(dataset_repo='dhmeltzer/asks_validation_embedded'):
    """Read the data from huggingface."""
    return load_dataset(dataset_repo)['validation_asks']

@st.cache_data
def load_faiss_index(path_to_faiss="./faiss_index_small.pickle"):
    """Load and deserialize the Faiss index."""
    with open(path_to_faiss, "rb") as h:
        data = pickle.load(h)
    return faiss.deserialize_index(data)

def main():
    # Load data and models
    data = read_data()
    #model = load_bert_model()
    #tok = load_tokenizer()
    faiss_index = load_faiss_index()

    model_id="sentence-transformers/nli-distilbert-base"
    
    api_url = f"https://api-inference.huggingface.co/pipeline/feature-extraction/{model_id}"
    headers = {"Authorization": f"Bearer {st.secrets['HF_token']}"}

    def query(texts):
        response = requests.post(api_url, headers=headers, json={"inputs": texts, "options":{"wait_for_model":True}})
        return response.json()


    st.title("Vector-based searches with Sentence Transformers and Faiss")

    # User search
    user_input = st.text_area("Search box", "What is spacetime made out of?")

    # Filters
    st.sidebar.markdown("**Filters**")
    num_results = st.sidebar.slider("Number of search results", 1, 50, 1)

    vector = query([user_input])
    # Fetch results
    if user_input:
        # Get paper IDs
        _, I = faiss_index.search(np.array(vector).astype("float32"), k=num_results)
        
        # Get individual results
        for id_ in I.flatten().tolist():
            row = data[id_]
            
            answers=row['answers']['text']
            answers_URLs = row['answers_urls']['url']
            for k in range(len(answers_URLs)):
                answers = [answer.replace(f'_URL_{k}_',answers_URLs[k]) for answer in answers]
            
            
            st.write(
                f"""**Title**: {row['title']}
                \n
                **Score**: {row[answers]['score'][0]}
                \n
                **Top Answer**: {answers[0]}
                """
            )
            st.write("-"*20) 



if __name__ == "__main__":
    main()

#@st.cache(allow_output_mutation=True)
#def load_bert_model(name="nli-distilbert-base"):
#    """Instantiate a sentence-level DistilBERT model."""
#    return AutoModel.from_pretrained(f'sentence-transformers/{name}')
#
#@st.cache(allow_output_mutation=True)
#def load_tokenizer(name="nli-distilbert-base"):
#    return AutoTokenizer.from_pretrained(f'sentence-transformers/{name}')

#@st.cache(allow_output_mutation=True)