app.py

import streamlit as st
from transformers import pipeline

pipe1 = pipeline("text-classification", model="Emma0123/fine_tuned_model")
pipe2 = pipeline("text-classification", model="jonas/roberta-base-finetuned-sdg")

st.title("ESG with HuggingFace Spaces")
st.write("Enter a sentence to analyze its ESG")
input_text =st.text_input("")

# 使用第一个模型进行预测
result1 = pipe1(input_text)

# 判断第一个模型的输出结果
if result1[0]['label'] == 'environmental':  # 根据您的模型实际返回的标签进行修改
    result2 = pipe2(input_text)
    st.write(f"The model predicts this text to be related to category '{result2['label']}' with a confidence score of {result2['score']:.2%}.")
else:
    # 如果输出结果为0（或者对应的标签），打印提示信息
    st.write("This content is unrelated to Environment.")