| import torch |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| import gradio as gr |
|
|
| |
| model_name = "sansanmaw/ssm_model" |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
|
|
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) |
| model.to(device) |
| model.eval() |
|
|
| |
| id2lbl = {0: "fake", 1: "true"} |
|
|
| |
| def predict_news(text): |
| inputs = tokenizer( |
| text, |
| padding=True, |
| truncation=True, |
| max_length=512, |
| return_tensors="pt" |
| ) |
| inputs = {k: v.to(device) for k, v in inputs.items()} |
|
|
| with torch.no_grad(): |
| outputs = model(**inputs) |
| logits = outputs.logits |
| predictions = torch.argmax(logits, dim=-1).cpu().numpy() |
| predicted_label = id2lbl[predictions[0]] |
| |
| return predicted_label |
|
|
| |
| interface = gr.Interface( |
| fn=predict_news, |
| inputs=gr.Textbox(lines=8, placeholder="Paste news article here..."), |
| outputs="text", |
| title="Fake News Detector (Roberta)", |
| description="Paste a news article to check if it's real or fake." |
| ) |
|
|
| |
| if __name__ == "__main__": |
| interface.launch() |
|
|
