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app.py

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+import torch
+import torch.nn.functional as F
+from transformers import RobertaTokenizerFast, RobertaForSequenceClassification
+import gradio as gr
+
+
+MODEL_NAME = "Clement1290/261_DetectionAI_GeminiPlusGPT"  
+
+LABEL_MAP = {
+    0: "Human",
+    1: "AI"
+}
+
+# ===== 加载模型 & tokenizer =====
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+tokenizer = RobertaTokenizerFast.from_pretrained(MODEL_NAME)
+model = RobertaForSequenceClassification.from_pretrained(MODEL_NAME)
+model.to(device)
+model.eval()
+
+
+def classify_text(text: str):
+    if not text.strip():
+        return "N/A", "Please input some text.", 0.0
+
+    inputs = tokenizer(
+        text,
+        return_tensors="pt",
+        truncation=True,
+        max_length=512,
+        padding=True,
+    ).to(device)
+
+    with torch.no_grad():
+        logits = model(**inputs).logits
+        probs = F.softmax(logits, dim=-1)[0]
+
+    pred_label = torch.argmax(probs).item()
+    confidence = probs[pred_label].item()
+
+    label_str = LABEL_MAP.get(pred_label, "Unknown")
+    pred_str = f"{pred_label} ({label_str})"
+
+    return pred_str, f"{label_str}", round(float(confidence), 4)
+
+
+# ===== Gradio 界面 =====
+title = "Human vs AI Text Classifier"
+description = (
+    "RoBERTa-based binary classifier. "
+    "Prediction: 0 = Human, 1 = AI."
+)
+
+demo = gr.Interface(
+    fn=classify_text,
+    inputs=gr.Textbox(
+        lines=8,
+        placeholder="Paste a paragraph here...",
+        label="Input text",
+    ),
+    outputs=[
+        gr.Textbox(label="Raw Prediction (id + label)"),
+        gr.Textbox(label="Label"),
+        gr.Number(label="Confidence score")
+    ],
+    title=title,
+    description=description,
+    examples=[
+        ["This is a short note I wrote myself about my day and my thoughts."],
+        ["As an advanced AI model, I can generate human-like responses across a wide range of tasks."]
+    ]
+)
+
+if __name__ == "__main__":
+    demo.launch()
+