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import gradio as gr
from transformers import pipeline
import torch
id2label = {0: "NEGATIVE", 1: "POSITIVE"}
label2id = {"NEGATIVE": 0, "POSITIVE": 1}
# 导入在 HuggingFace 我们刚刚训练好而且上传成功的模型 chjun/my_awesome_model
classifier = pipeline("sentiment-analysis", model="chjun/my_awesome_model")
# inputs:输入文本
def predict(inputs):
label_score = classifier(inputs)
scaled = 0
if label_score[0]["label"] == "NEGATIVE":
scaled = 1 - label_score[0]["score"]
else:
scaled = label_score[0]["score"]
# 解码返回值得到输出
return round(scaled * 5)
with gr.Blocks() as demo:
review = gr.Textbox(label="用户评论。注:此模型只使用了英文数据 Finetune")
output = gr.Textbox(label="颗星")
submit_btn = gr.Button("提交")
submit_btn.click(fn=predict, inputs=review, outputs=output)
demo.launch(debug=True) |