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import streamlit as st
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import os
from transformers import AutoTokenizer, AutoModel
# Assuming you have set the HF_TOKEN environment variable with your Hugging Face token
huggingface_token = os.getenv('HF_TOKEN')
# Set up the token to use with the Hugging Face API
if huggingface_token is not None:
os.environ['HUGGINGFACE_CO_API_TOKEN'] = huggingface_token
tokenizer = AutoTokenizer.from_pretrained("Tokymin/Mood_Anxiety_Disorder_Classify_Model")
else:
print("error, no token")
exit(0)
model = AutoModelForSequenceClassification.from_pretrained("Tokymin/Mood_Anxiety_Disorder_Classify_Model",
num_labels=8)
model.eval()
def predict(text):
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probabilities = torch.softmax(logits, dim=1).squeeze()
# 假设每个类别（SAS_Class和SDS_Class）都有4个概率值
sas_probs = probabilities[:4] # 获取SAS_Class的概率
sds_probs = probabilities[4:] # 获取SDS_Class的概率
return sas_probs, sds_probs
# 创建Streamlit应用
st.title("Multi-label Classification App")
# 用户输入文本
user_input = st.text_area("Enter text here", "Type something...")
if st.button("Predict"):
# 显示预测结果
sas_probs, sds_probs = predict(user_input)
st.write("SAS_Class probabilities:", sas_probs.numpy())
st.write("SDS_Class probabilities:", sds_probs.numpy())
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