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

title = "πŸ€κ³ λ―Ό ν•΄κ²° λ„μ„œ μΆ”μ²œ μ±—λ΄‡πŸ€"
description = "고민이 λ¬΄μ—‡μΈκ°€μš”? κ³ λ―Ό 해결을 도와쀄 책을 μΆ”μ²œν•΄λ“œλ¦½λ‹ˆλ‹€"
examples = [["μš”μ¦˜ 잠이 μ•ˆ μ˜¨λ‹€"], ["화뢄이 잘 μžλΌμ§€ μ•Šμ•„"]]


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

df = pd.read_pickle('BookData_emb.pkl')
df_emb = df[['μ„œν‰μž„λ² λ”©']].copy()


def recommend(message):
    answer = df.loc[df_emb['μ„œν‰μž„λ² λ”©'][0]]
  # embedding = model.encode(message)
  # df_emb['거리'] = df_emb['μ„œν‰μž„λ² λ”©'].map(lambda x: cosine_similarity([embedding], [x]).squeeze())
  # answer = df.loc[df_emb['거리'].idxmax()]
  # Book_title = answer['제λͺ©']
  # Book_author = answer['μž‘κ°€']
  # Book_publisher = answer['μΆœνŒμ‚¬']
  # Book_comment = answer['μ„œν‰']
    return answer

gr.ChatInterface(
        fn=recommend,
        textbox=gr.Textbox(placeholder="λ§κ±Έμ–΄μ£Όμ„Έμš”..", container=False, scale=7),
        title=title,
        description=description,
        theme="soft",
        examples=examples,
        retry_btn="λ‹€μ‹œλ³΄λ‚΄κΈ° ↩",
        undo_btn="이전챗 μ‚­μ œ ❌",
        clear_btn="μ „μ±— μ‚­μ œ πŸ’«").launch()