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import gradio as gr
from keras.models import load_model
import pickle
import torch
import numpy as np
import pandas as pd
from utils import test_sentences

model = torch.load('bert_classification(0.87).pt')

# Gradio ์ธํ„ฐํŽ˜์ด์Šค์—์„œ ์‚ฌ์šฉํ•  ํ•จ์ˆ˜ ์ •์˜
def predict_sentiment(text):
  logit = test_sentences([text], model)
  sent = np.argmax(logit)

  classes = {0: "์ค‘๋ฆฝ", 1:"๊ธ์ •", 2:"๋ถ€์ •"}
  sentiment = classes[sent]

  return sentiment

title = "๐Ÿคทโ€โ™€๏ธ๐Ÿคทโ€โ™‚๏ธWhat is the EMOTION of TWEETโ”"
description = "โ–ถํŠธ์œ„ํ„ฐ ๊ฐ์ • ์•Œ์•„๋ณด๊ธฐ"


tweet = gr.Interface(
    fn=predict_sentiment,
    inputs=gr.inputs.Textbox(placeholder="Enter tweet here..."),
    outputs=gr.outputs.Textbox(label="Sentiment Prediction"),
    title=title,
    theme="finlaymacklon/boxy_violet",
    description=description
)
tweet.launch(share=True)