| import gradio as gr | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
| import torch | |
| model_name = "nmarinnn/bert-bregman" | |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| def predict(text): | |
| inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512) | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1) | |
| predicted_class = torch.argmax(probabilities, dim=-1).item() | |
| class_labels = {0: "negativo", 1: "neutro", 2: "positivo"} | |
| predicted_label = class_labels[predicted_class] | |
| predicted_probability = probabilities[0][predicted_class].item() | |
| result = f"Clase predicha: {predicted_label} (probabilidad = {predicted_probability:.2f})\n" | |
| result += f"Probabilidades: Negativo: {probabilities[0][0]:.2f}, Neutro: {probabilities[0][1]:.2f}, Positivo: {probabilities[0][2]:.2f}" | |
| return result | |
| iface = gr.Interface( | |
| fn=predict, | |
| inputs=gr.Textbox(lines=2, placeholder="Ingrese el texto aquí..."), | |
| outputs="text", | |
| title="Clasificador de Sentimientos", | |
| description="Este modelo clasifica el sentimiento del texto como negativo, neutro o positivo." | |
| ) | |
| iface.launch() |