app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# 加载模型和 tokenizer
model_name = "LilithHu/mbert-manipulative-detector"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# 设置为评估模式
model.eval()

# 设置运行设备
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# 标签名
labels = ["Non-manipulative / 非操纵性", "Manipulative / 操纵性"]

# 推理函数
def classify(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device)
    with torch.no_grad():
        outputs = model(**inputs)
        probs = torch.softmax(outputs.logits, dim=1)[0]
        pred = torch.argmax(probs).item()
        confidence = min(probs[pred].item(), 0.95)  # 限制置信度最大为95%
        percent = round(confidence * 100, 2)
        result = f"Prediction / 预测:\n{labels[pred]}\n📊 Confidence /置信度: {percent}%"
        return result


# Gradio 界面
interface = gr.Interface(
    fn=classify,
    inputs=gr.Textbox(
        lines=4, 
        placeholder="Enter a sentence in English or Chinese... / 输入英文或中文句子",
        label="📝 Input Text / 输入文本"
    ),
    outputs=gr.Textbox(label="📊 Prediction / 预测结果"),
    title="🧠 Manipulative Language Detector / 操纵性语言识别器",
    description="""
🔍 This tool detects **emotionally manipulative language** in English or Chinese digital communication.  


---

📌 **Disclaimer / 免责声明：**  
This system is for **research and educational purposes only**.  
It **does not guarantee accuracy** and **should not be used as legal or clinical evidence**.
本工具仅用于**学术研究与教学演示**，不构成法律、医疗或其他正式用途的依据。


---

🤖 **Model Info**:  
- Model: `LilithHu/mbert-manipulative-detector`  
- Base: `mDeBERTa-v3` multilingual pre-trained model  
- Fine-tuned using HuggingFace Transformers on 10,000 labeled Chinese data  

🌐 Built with Gradio and hosted on HuggingFace Spaces.
""",
    theme="default",
    allow_flagging="never"
)


interface.launch()