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requirements.txt

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+# requirements.txt
+gradio[mcp]
+transformers
+torch

server.py

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+from transformers import pipeline
+import gradio as gr
+
+# Cargar modelo de Hugging Face
+sentiment_pipeline = pipeline(
+    "sentiment-analysis",
+    model="distilbert-base-uncased-finetuned-sst-2-english"
+)
+
+def sentiment_analysis(text: str) -> dict:
+    result = sentiment_pipeline(text)[0]
+    return {
+        "sentiment": result["label"],
+        "confidence": round(result["score"], 4)
+    }
+
+demo = gr.Interface(
+    fn=sentiment_analysis,
+    inputs=gr.Textbox(placeholder="Enter text..."),
+    outputs=gr.JSON(),
+    title="LLM-based Sentiment Analysis"
+)
+
+if __name__ == "__main__":
+    demo.launch(mcp_server=True)