| import torch
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| from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
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| import gradio as gr
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|
|
|
|
| tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
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| model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=2)
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| model.load_state_dict(torch.load('best_model (4).pth', map_location=torch.device('cpu')))
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| model.eval()
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|
|
|
|
| def classify_news(text):
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| inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True, max_length=512)
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| with torch.no_grad():
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| outputs = model(**inputs)
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| probs = torch.nn.functional.softmax(outputs.logits, dim=1)
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| predicted_class = torch.argmax(probs, dim=1).item()
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| labels = ["Fake", "True"]
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| return {labels[0]: float(probs[0][0]), labels[1]: float(probs[0][1])}
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|
|
|
|
| iface = gr.Interface(
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| fn=classify_news,
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| inputs=gr.Textbox(lines=10, placeholder="Paste a news article here..."),
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| outputs=gr.Label(num_top_classes=2),
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| title="Fake News Detector",
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| description="Detect whether a news article is real or fake using a fine-tuned DistilBERT model."
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| )
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|
|
|
|
| if __name__ == "__main__":
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| iface.launch()
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|
|
