app.py

# 1. IMPORTACIÓN PREVENTIVA: Si 'spaces' existe, debe ir primero
try:
    import spaces
except ImportError:
    pass

import sys
import os
import gradio as gr
import torch
import cv2
import numpy as np
from PIL import Image
from diffusers import StableDiffusionPipeline
from huggingface_hub import hf_hub_download

# --- FORZAR RUTA PARA QUE ENCUENTRE EL MÓDULO ---
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from ip_adapter.ip_adapter_faceid import IPAdapterFaceID
from insightface.app import FaceAnalysis

# Configuración
device = "cpu"
model_id = "runwayml/stable-diffusion-v1-5"

# Descarga automática del modelo
print("Descargando/Verificando el modelo IP-Adapter...")
ip_ckpt = hf_hub_download(repo_id="h94/IP-Adapter-FaceID", filename="ip-adapter-faceid_sd15.bin")

# Carga de modelos
# NOTA: Al usar 'onnxruntime' (versión CPU) en requirements.txt, esto debería ir fluido
app = FaceAnalysis(name="buffalo_l", providers=['CPUExecutionProvider'])
app.prepare(ctx_id=0, det_size=(640, 640))

pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32).to(device)
ip_model = IPAdapterFaceID(pipe, ip_ckpt, device)

def generate(image, prompt):
    img_cv = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
    faces = app.get(img_cv)
    if not faces: 
        return None
    
    # Generar
    results = ip_model.generate(
        pil_image=image, face_embed=faces[0].embedding, 
        prompt=prompt, width=512, height=512
    )
    return results[0]

# Lanzar con opciones optimizadas
demo = gr.Interface(fn=generate, inputs=[gr.Image(type="pil"), gr.Textbox()], outputs="image")
demo.launch()