nmarinnn commited on
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9e5cede
1 Parent(s): 2fe4e18

Create app.py

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  1. app.py +34 -0
app.py ADDED
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+ return result
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+
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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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+
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+ iface.launch()