app.py

from langchain.vectorstores import FAISS
from langchain.embeddings import SentenceTransformerEmbeddings
import gradio as gr
import reranking

embeddings = SentenceTransformerEmbeddings(model_name="multi-qa-MiniLM-L6-cos-v1")
db = FAISS.load_local('faiss_qa2', embeddings)

def main(query):
    result_docs = db.similarity_search_with_score(query, k=20)
    sentences = [doc[0].page_content for doc in result_docs]
    score, index = reranking.search(query, sentences)
    return result_docs[index][0].metadata['answer'], score, result_docs[index][0].page_content

demo = gr.Interface(fn=main, inputs="text", outputs=[
  gr.Textbox(label="Ответ, который будет показан клиенту"),
  gr.Textbox(label="Score"),
  gr.Textbox(label="Вопрос, по которому был найден ответ"),
])

demo.launch()