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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 = "nmarinnn/bert-bregman" |
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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(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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probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1) |
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predicted_class = torch.argmax(probabilities, dim=-1).item() |
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class_labels = {0: "negativo", 1: "neutro", 2: "positivo"} |
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predicted_label = class_labels[predicted_class] |
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predicted_probability = probabilities[0][predicted_class].item() |
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result = f"Clase predicha: {predicted_label} (probabilidad = {predicted_probability:.2f})\n" |
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result += f"Probabilidades: Negativo: {probabilities[0][0]:.2f}, Neutro: {probabilities[0][1]:.2f}, Positivo: {probabilities[0][2]:.2f}" |
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return result |
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iface = gr.Interface( |
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fn=predict, |
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inputs=gr.Textbox(lines=2, placeholder="Ingrese el texto aquí..."), |
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outputs="text", |
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title="Clasificador de Sentimientos", |
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description="Este modelo clasifica el sentimiento del texto como negativo, neutro o positivo." |
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) |
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iface.launch() |