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app.py

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+import gradio as gr
+from transformers import TFAutoModelForSequenceClassification, AutoTokenizer
+import tensorflow as tf
+
+# Cargar el modelo y tokenizer desde Hugging Face
+model_name = "albabernal03/mi-modelo-sentimiento"
+model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Función para predecir el sentimiento
+def predict_sentiment(text):
+    inputs = tokenizer(text, return_tensors="tf", truncation=True, padding=True)
+    outputs = model(**inputs)
+    logits = outputs.logits
+    probabilities = tf.nn.softmax(logits, axis=-1).numpy()
+    labels = ["Negative", "Neutral", "Positive"]
+    predicted_label = labels[probabilities.argmax()]
+    confidence = probabilities.max()
+    return f"{predicted_label} ({confidence:.2f})"
+
+# Crear la interfaz con Gradio
+demo = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(lines=2, placeholder="Escribe un texto..."),
+    outputs="text",
+    title="Sentiment Analysis Model",
+    description="Este modelo predice el sentimiento de un texto como Negativo, Neutral o Positivo."
+)
+
+# Ejecutar la demo
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+transformers
+tensorflow
+gradio