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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 = "πκ³ λ―Ό ν΄κ²° λμ μΆμ² μ±λ΄π"
description = "κ³ λ―Όμ΄ λ¬΄μμΈκ°μ? κ³ λ―Ό ν΄κ²°μ λμμ€ μ±
μ μΆμ²ν΄λ립λλ€"
examples = [["μμ¦ μ μ΄ μ μ¨λ€"], ["νλΆμ΄ μ μλΌμ§ μμ"]]
# model = SentenceTransformer('jhgan/ko-sroberta-multitask')
df = pd.read_pickle('BookData_emb.pkl')
df_emb = df[['μνμλ² λ©']].copy()
def recommend(message):
answer = df.loc[df_emb['μνμλ² λ©'][0]]
# embedding = model.encode(message)
# df_emb['거리'] = df_emb['μνμλ² λ©'].map(lambda x: cosine_similarity([embedding], [x]).squeeze())
# answer = df.loc[df_emb['거리'].idxmax()]
# Book_title = answer['μ λͺ©']
# Book_author = answer['μκ°']
# Book_publisher = answer['μΆνμ¬']
# Book_comment = answer['μν']
return answer
gr.ChatInterface(
fn=recommend,
textbox=gr.Textbox(placeholder="λ§κ±Έμ΄μ£ΌμΈμ..", container=False, scale=7),
title=title,
description=description,
theme="soft",
examples=examples,
retry_btn="λ€μ보λ΄κΈ° β©",
undo_btn="μ΄μ μ± μμ β",
clear_btn="μ μ± μμ π«").launch() |