generation.py

import os
import random
from datetime import datetime
from typing import Optional

from huggingface_hub import InferenceClient

# Directorio donde se guardan las imágenes generadas
OUTPUT_DIR = "generated_images"
os.makedirs(OUTPUT_DIR, exist_ok=True)

# Cliente de inferencia (igual que en Sofia Rivera)
client = InferenceClient()

def generate_image_from_prompt(
    prompt: str,
    negative_prompt: str = "",
    model_name: str = "black-forest-labs/FLUX.1-dev",
    seed: Optional[int] = None,
) -> tuple[Optional[str], str]:
    """
    Genera una imagen usando Hugging Face InferenceClient.text_to_image
    y la guarda en OUTPUT_DIR.

    Devuelve (image_path, status_message).
    Si hay error, image_path = None y status_message contiene el error.
    """
    try:
        if seed is None:
            seed = random.randint(0, 2_147_483_647)

        image = client.text_to_image(
            prompt=prompt,
            negative_prompt=negative_prompt,
            model=model_name,
            guidance_scale=7.5,
            num_inference_steps=50,
        )

        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        filename = f"sofia_{timestamp}_{seed}.png"
        file_path = os.path.join(OUTPUT_DIR, filename)

        image.save(file_path)

        status = f"✅ Imagen generada y guardada: {filename}\nModelo: {model_name}\nSeed: {seed}"
        return file_path, status

    except Exception as e:
        error_msg = f"❌ Error al generar imagen: {str(e)}"
        return None, error_msg