app.py

from pathlib import Path
import streamlit as st
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import os
from transformers import AutoTokenizer, AutoModel
import requests

# 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
    API_URL = "https://api-inference.huggingface.co/models/Tokymin/Mood_Anxiety_Disorder_Classify_Model"
    headers = {"Authorization": f"Tokymin {huggingface_token}"}
else:
    print("error, no token")
    exit(0)

# def query(payload):
#     response = requests.post(API_URL, headers=headers, json=payload)
#     return response.json()
# data = query("Can you please let us know more details about your ")
path: Path = Path('Tokymin/Mood_Anxiety_Disorder_Classify_Model')
tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=path, cache_dir='/home/user', token=huggingface_token)

# tokenizer = AutoTokenizer.from_pretrained('Tokymin/Mood_Anxiety_Disorder_Classify_Model')
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())