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

model_path = "你的模型名稱或資料夾路徑"  # 根據你模型的位置來填
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)
model.eval()

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

def detect_ai(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).squeeze()
        human_prob = probs[0].item()
        ai_prob = probs[1].item()
        label = "AI 生成" if ai_prob > human_prob else "人類撰寫"
        return f"預測：{label}\nAI 機率：{ai_prob:.2%}\n人類機率：{human_prob:.2%}"

demo = gr.Interface(
    fn=detect_ai,
    inputs=gr.Textbox(label="請貼上要分析的文字", lines=10, placeholder="貼上或輸入內容..."),
    outputs="text",
    title="AI vs 人類文字偵測器",
    description="這個工具可以幫助你判斷輸入的文字是 AI 生成還是人類撰寫。",
)

demo.launch()