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import gradio as gr
import torch
import pandas as pd
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
title = "๐๊ณ ๋ฏผ ํด๊ฒฐ ๋์ ์ถ์ฒ ์ฑ๋ด๐"
description = "๊ณ ๋ฏผ์ด ๋ฌด์์ธ๊ฐ์? ๊ณ ๋ฏผ ํด๊ฒฐ์ ๋์์ค ์ฑ
์ ์ถ์ฒํด๋๋ฆฝ๋๋ค"
examples = [["์์ฆ ์ ์ด ์ ์จ๋ค"]]
model = SentenceTransformer('jhgan/ko-sroberta-multitask')
def response(message):
embedding = model.encode(message)
df['distance'] = df['embedding'].map(lambda x: cosine_similarity([embedding], [x]).squeeze())
answer = df.loc[df['distance'].idxmax()]
Book_title = answer['์ ๋ชฉ']
Book_author = answer['์๊ฐ']
Book_publisher = answer['์ถํ์ฌ']
Book_comment = answer['์ํ']
return print(message)
gr.Interface(
fn=predict,
title=title,
description=description,
examples=examples,
inputs=["text", "state"],
outputs=["chatbot", "state"],
theme="finlaymacklon/boxy_violet",
).launch() |