app.py

import gradio as gr
from processing import md_to_passages
from pg import get_chapters
from vectors import match_query


def find_embedding(query: str):
    top_res = match_query(query, 3)
    # print(top_res)

    chapters = get_chapters(list(map(lambda x: x["metadata"]["chapterId"], top_res)))

    output = ""

    for res, chapter in zip(top_res, chapters):
        passages = md_to_passages(chapter["explanation"])
        output += f"{res['id']}\t| score: {res['score']:.2f}%\n{passages[res['passage_idx']]}\n\n"

    return output


with gr.Blocks() as quesbook_search:
    question = gr.Text(label="question")
    answer = gr.Text(label="answer")
    submit = gr.Button("Submit")
    submit.click(fn=find_embedding, inputs=question, outputs=answer)

quesbook_search.launch()