Create app.py

app.py

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+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()