app.py

import gradio as gr
import pandas as pd
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity

title = "🍀고민 해결 도서 추천 챗봇🍀"
description = "고민이 무엇인가요? 고민 해결을 도와줄 책을 추천해드립니다"
examples = [["요즘 잠이 안 온다"]]


model = SentenceTransformer('jhgan/ko-sroberta-multitask')

df = pd.read_pickle('BookData_emb.pkl')
df_emb = df[['서평임베딩']].copy()


def recommend(message):
  embedding = model.encode(message)
  df_emb['거리'] = df_emb['서평임베딩'].map(lambda x: cosine_similarity([embedding], [x]).squeeze())
  answer = df.loc[df_emb['거리'].idxmax()]
  Book_title = answer['제목']
  Book_author = answer['작가']
  Book_publisher = answer['출판사']
  Book_comment = answer['서평']
  return Book_title

gr.ChatInterface(
        fn=recommend,
        textbox=gr.Textbox(placeholder="말걸어주세요..", container=False, scale=7),
        title="어떤 챗봇을 원하심미까?",
        description="물어보면 답하는 챗봇임미다.",
        theme="soft",
        examples=[["안뇽"], ["요즘 덥다 ㅠㅠ"], ["점심메뉴 추천바람, 짜장 짬뽕 택 1"]],
        retry_btn="다시보내기 ↩",
        undo_btn="이전챗 삭제 ❌",
        clear_btn="전챗 삭제 💫").launch()

# gr.Interface(
#     fn=response,
#     title=title,
#     description=description,
#     examples=examples,
#     inputs=["text", "state"],
#     outputs=["chatbot", "state"],
#     theme="finlaymacklon/boxy_violet",
# ).launch()