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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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model_name = "distilbert-base-uncased-finetuned-sst-2-english" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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def classify_text(text): |
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inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits = outputs.logits |
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predicted_class = torch.argmax(logits, dim=1).item() |
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return f"Predicted class: {predicted_class}" |
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import gradio as gr |
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interface = gr.Interface( |
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fn=classify_text, |
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inputs="text", |
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outputs="text", |
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title="BERT Text Classifier" |
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) |
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interface.launch() |
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