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