Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import torch
+
+# Cargar modelo y tokenizer (solo una vez)
+model_name = "LiquidAI/LFM2-350M"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Crear pipeline de generación
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    device=0 if torch.cuda.is_available() else -1,  # Usa GPU si está disponible
+    max_new_tokens=128,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9,
+)
+
+def chat_function(message, history):
+    # Formatear historial como prompt (simple concatenación)
+    conversation = ""
+    for human, assistant in history:
+        conversation += f"User: {human}\nAssistant: {assistant}\n"
+    conversation += f"User: {message}\nAssistant:"
+
+    # Generar respuesta
+    outputs = pipe(conversation, return_full_text=False)
+    response = outputs[0]['generated_text'].strip()
+
+    # Limpiar posibles repeticiones o cortes
+    if "User:" in response:
+        response = response.split("User:")[0].strip()
+    if "Assistant:" in response:
+        response = response.split("Assistant:")[0].strip()
+
+    return response
+
+# Interfaz Gradio
+with gr.Blocks(title="LFM2-350M Chat") as demo:
+    gr.Markdown("# 🤖 LFM2-350M Chat (Simple)")
+    gr.Markdown("Un chat simple usando el modelo `LiquidAI/LFM2-350M`. Ideal para pruebas en Hugging Face Spaces Free.")
+
+    chatbot = gr.Chatbot(height=400)
+    msg = gr.Textbox(label="Escribe tu mensaje", placeholder="Escribe algo...")
+    clear = gr.Button("Limpiar")
+
+    def respond(message, chat_history):
+        bot_message = chat_function(message, chat_history)
+        chat_history.append((message, bot_message))
+        return "", chat_history
+
+    msg.submit(respond, [msg, chatbot], [msg, chatbot])
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+if __name__ == "__main__":
+    demo.launch()