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import gradio as gr |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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import torch |
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model_name = "ahmetyaylalioglu/text-emotion-classifier" |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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def predict_emotion(text): |
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inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512) |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1) |
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prediction = torch.argmax(probabilities, dim=-1).item() |
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emotion = model.config.id2label[prediction] |
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confidence = probabilities[0][prediction].item() |
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return f"Emotion: {emotion}\nConfidence: {confidence:.2f}" |
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iface = gr.Interface( |
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fn=predict_emotion, |
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inputs=gr.Textbox(lines=2, placeholder="Enter text here..."), |
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outputs="text", |
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title="Emotion Classifier", |
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description="Enter some text and click 'Submit' to predict the emotion." |
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) |
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iface.launch() |