script.py

# Crear el código completo para un espacio de Hugging Face que genera títulos
# usando Llama-3.2-1B-Instruct con interfaz Gradio y API

app_code = '''import gradio as gr
import os
from huggingface_hub import InferenceClient

# Obtener el token de HF desde los secrets
HF_TOKEN = os.environ.get("HF_TOKEN")

# Inicializar el cliente de inferencia con el modelo Llama
client = InferenceClient(
    model="meta-llama/Llama-3.2-1B-Instruct",
    token=HF_TOKEN
)

def generate_title(text_or_history, max_length=50):
    """
    Genera un título a partir de texto o historial de conversación
    
    Args:
        text_or_history: Puede ser texto simple o una lista de mensajes
        max_length: Longitud máxima del título
    
    Returns:
        El título generado
    """
    try:
        # Si es una lista (historial), convertirla a texto
        if isinstance(text_or_history, list):
            # Formatear el historial como conversación
            conversation_text = "\\n".join([
                f"{msg.get('role', 'user')}: {msg.get('content', '')}" 
                for msg in text_or_history
            ])
        else:
            conversation_text = str(text_or_history)
        
        # Crear el prompt para generar título
        prompt = f"""Based on the following conversation or text, generate a short, concise title (maximum 10 words):

{conversation_text}

Title:"""
        
        # Generar el título usando el modelo
        messages = [
            {"role": "user", "content": prompt}
        ]
        
        response = ""
        for message in client.chat_completion(
            messages=messages,
            max_tokens=max_length,
            temperature=0.7,
            stream=True
        ):
            token = message.choices[0].delta.content
            if token:
                response += token
        
        # Limpiar el título (quitar saltos de línea extra, etc.)
        title = response.strip().split("\\n")[0]
        
        return title
    
    except Exception as e:
        return f"Error: {str(e)}"

# Crear la interfaz de Gradio
with gr.Blocks(title="Title Generator with Llama 3.2") as demo:
    gr.Markdown("# 📝 AI Title Generator")
    gr.Markdown("Generate concise titles from text or conversation history using Llama 3.2-1B-Instruct")
    
    with gr.Tab("Text Input"):
        text_input = gr.Textbox(
            label="Enter your text",
            placeholder="Paste your text or conversation here...",
            lines=10
        )
        text_button = gr.Button("Generate Title", variant="primary")
        text_output = gr.Textbox(label="Generated Title", lines=2)
        
        text_button.click(
            fn=generate_title,
            inputs=[text_input],
            outputs=[text_output]
        )
    
    with gr.Tab("History/List Input"):
        gr.Markdown("Enter conversation history as JSON format:")
        gr.Markdown('Example: `[{"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi there!"}]`')
        
        history_input = gr.Textbox(
            label="Conversation History (JSON)",
            placeholder='[{"role": "user", "content": "Your message here"}]',
            lines=10
        )
        history_button = gr.Button("Generate Title", variant="primary")
        history_output = gr.Textbox(label="Generated Title", lines=2)
        
        def process_history(history_json):
            try:
                import json
                history_list = json.loads(history_json)
                return generate_title(history_list)
            except json.JSONDecodeError:
                return "Error: Invalid JSON format"
        
        history_button.click(
            fn=process_history,
            inputs=[history_input],
            outputs=[history_output]
        )
    
    gr.Markdown("---")
    gr.Markdown("### API Usage")
    gr.Markdown("""
    You can use this API with CURL:
    
    ```bash
    curl -X POST "https://YOUR-SPACE-URL/call/generate_title" \\
      -H "Content-Type: application/json" \\
      -d '{"data": ["Your text here"]}'
    ```
    """)

# Lanzar la aplicación con API habilitada
if __name__ == "__main__":
    demo.launch(show_api=True)
'''

# Guardar el código en un archivo
with open('app.py', 'w', encoding='utf-8') as f:
    f.write(app_code)

# Crear el archivo requirements.txt
requirements = '''gradio>=4.0.0
huggingface_hub>=0.19.0
'''

with open('requirements.txt', 'w', encoding='utf-8') as f:
    f.write(requirements)

# Crear el README con instrucciones
readme = '''---
title: Title Generator with Llama 3.2
emoji: 📝
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: mit
---

# Title Generator with Llama 3.2-1B-Instruct

Generate concise titles from text or conversation history using Meta's Llama 3.2-1B-Instruct model.

## Features

- 📝 Generate titles from plain text
- 💬 Generate titles from conversation history
- 🚀 Fast inference with Llama 3.2-1B
- 🔌 RESTful API support for integration

## Setup

1. Go to your Space settings
2. Add a new secret: `HF_TOKEN` with your Hugging Face token
3. Make sure you have access to `meta-llama/Llama-3.2-1B-Instruct` (accept the gated model)

## API Usage

### CURL Example

```bash
curl -X POST "https://YOUR-SPACE-URL/call/generate_title" \\
  -H "Content-Type: application/json" \\
  -d '{"data": ["Your text or conversation here"]}'
```

### Python Example

```python
from gradio_client import Client

client = Client("YOUR-SPACE-URL")
result = client.predict("Your text here", api_name="/generate_title")
print(result)
```

## License

MIT License
'''

with open('README.md', 'w', encoding='utf-8') as f:
    f.write(readme)

print("✅ Archivos generados exitosamente:")
print("- app.py")
print("- requirements.txt") 
print("- README.md")
print("\n📦 Archivos listos para subir a Hugging Face Space")