app.py

from pathlib import Path
from PIL import Image
import streamlit as st
import insightface
from insightface.app import FaceAnalysis
from huggingface_hub import InferenceClient, AsyncInferenceClient
import asyncio
import os
import random
import numpy as np
import yaml

try:
    with open("config.yaml", "r") as file:
        credentials = yaml.safe_load(file)
except Exception as e:
    st.error(f"Error al cargar el archivo de configuración: {e}")
    credentials = {"username": "", "password": ""}

MAX_SEED = np.iinfo(np.int32).max
client = AsyncInferenceClient()
llm_client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
DATA_PATH = Path("./data")
DATA_PATH.mkdir(exist_ok=True)
PREDEFINED_SEED = random.randint(0, MAX_SEED)
HF_TOKEN_UPSCALER = os.environ.get("HF_TOKEN_UPSCALER")

def get_upscale_finegrain(prompt, img_path, upscale_factor):
    try:
        upscale_client = Client("finegrain/finegrain-image-enhancer", hf_token=HF_TOKEN_UPSCALER)
        result = upscale_client.predict(input_image=handle_file(img_path), prompt=prompt, upscale_factor=upscale_factor)
        return result[1] if isinstance(result, list) and len(result) > 1 else None
    except Exception:
        return None

def authenticate_user(username, password):
    return username == credentials["username"] and password == credentials["password"]

def prepare_face_app():
    app = FaceAnalysis(name='buffalo_l')
    app.prepare(ctx_id=0, det_size=(640, 640))
    swapper = insightface.model_zoo.get_model('onix.onnx')
    return app, swapper

app, swapper = prepare_face_app()

def sort_faces(faces):
    return sorted(faces, key=lambda x: x.bbox[0])

def get_face(faces, face_id):
    if not faces or len(faces) < face_id:
        raise ValueError("Rostro no disponible.")
    return faces[face_id - 1]

def swap_faces(source_image, source_face_index, destination_image, destination_face_index):
    faces = sort_faces(app.get(source_image))
    source_face = get_face(faces, source_face_index)
    
    res_faces = sort_faces(app.get(destination_image))
    if destination_face_index > len(res_faces) or destination_face_index < 1:
        raise ValueError("Índice de rostro de destino no válido.")
    
    res_face = get_face(res_faces, destination_face_index)
    result = swapper.get(destination_image, res_face, source_face, paste_back=True)
    return result

async def generate_image(prompt, width, height, seed, model_name):
    if seed == -1:
        seed = random.randint(0, MAX_SEED)
    image = await client.text_to_image(prompt=prompt, height=height, width=width, model=model_name)
    return image, seed

async def gen(prompt, width, height, model_name):
    seed = PREDEFINED_SEED
    image, seed = await generate_image(prompt, width, height, seed, model_name)
    image_path = save_image(image, f"generated_image_{seed}.jpg", prompt)
    return str(image_path)

def list_saved_images():
    return list(DATA_PATH.glob("*.jpg"))

def display_gallery():
    st.header("Galería de Imágenes Guardadas")
    images = list_saved_images()
    if images:
        cols = st.columns(8)  
        for i, image_file in enumerate(images):
            with cols[i % 8]:  
                st.image(str(image_file), caption=image_file.name, use_column_width=True)
                prompt = get_prompt_for_image(image_file.name)  
                st.write(prompt[:300])  

                if st.button(f"Usar", key=f"select_{i}_{image_file.name}"):
                    st.session_state['generated_image_path'] = str(image_file)
                    st.success("Imagen seleccionada")

                if st.button(f"Borrar", key=f"delete_{i}_{image_file.name}"):
                    os.remove(image_file)
                    st.success("Imagen borrada")
                    display_gallery()
    else:
        st.info("No hay imágenes guardadas.")

def save_prompt(prompt):
    with open(DATA_PATH / "prompts.txt", "a") as f:
        f.write(prompt + "\n")
        st.success("Prompt guardado.")

def run_async(func, *args):
    return asyncio.run(func(*args))

async def improve_prompt(prompt):
    try:
        instruction = ("With this idea, describe in English a detailed txt2img prompt in 500 characters at most, add illumination, atmosphere, cinematic elements, and characters if needed...")
        formatted_prompt = f"{prompt}: {instruction}"
        response = llm_client.text_generation(formatted_prompt, max_new_tokens=500)
        return response['generated_text'][:500] if 'generated_text' in response else response.strip()
    except Exception as e:
        return f"Error mejorando el prompt: {e}"

def save_image(image, file_name, prompt=None):
    image_path = DATA_PATH / file_name
    if image_path.exists():
        st.warning(f"La imagen '{file_name}' ya existe en la galería. No se guardó.")
        return None 
    else:
        image.save(image_path, format="JPEG")
        if prompt:
            save_prompt(f"{file_name}: {prompt}")  
        return image_path

def get_prompt_for_image(image_name):
    prompts = {}
    try:
        with open(DATA_PATH / "prompts.txt", "r") as f:
            for line in f:
                if line.startswith(image_name):
                    prompts[image_name] = line.split(": ", 1)[1].strip()
    except FileNotFoundError:
        return "No hay prompt asociado."
    
    return prompts.get(image_name, "No hay prompt asociado.")

def login_form():
    st.title("Iniciar Sesión")
    username = st.text_input("Usuario", value="admin")
    password = st.text_input("Contraseña", value="flux3x", type="password") 
    if st.button("Iniciar Sesión"):
        if authenticate_user(username, password):
            st.success("Autenticación exitosa.")
            st.session_state['authenticated'] = True
        else:
            st.error("Credenciales incorrectas. Intenta de nuevo.")

def upload_image_to_gallery():
    uploaded_image = st.file_uploader("Sube una imagen a la galería", type=["jpg", "jpeg", "png"])
    if uploaded_image:
        image = Image.open(uploaded_image)
        image_path = save_image(image, f"{uploaded_image.name}")
        if image_path:
            save_prompt("uploaded by user")
            st.success(f"Imagen subida: {image_path}")

def main():
    st.set_page_config(layout="wide")

    if 'authenticated' not in st.session_state or not st.session_state['authenticated']:
        login_form()
        return

    st.title("Generador Flux")
    generated_image_path = st.session_state.get('generated_image_path')
    st.header("Generador de Imágenes")
    prompt = st.sidebar.text_area("Descripción de la imagen", height=150, max_chars=500)
    format_option = st.sidebar.selectbox("Formato", ["9:16", "16:9"])
    model_option = st.sidebar.selectbox("Modelo", ["black-forest-labs/FLUX.1-schnell", "black-forest-labs/FLUX.1-dev"])
    prompt_checkbox = st.sidebar.checkbox("Prompt Enhancer")
    upscale_checkbox = st.sidebar.checkbox("Escalar imagen")
    width, height = (720, 1280) if format_option == "9:16" else (1280, 720)
    upload_image_to_gallery()

    if prompt_checkbox:
        with st.spinner("Mejorando el prompt..."):
            try:
                improved_prompt = run_async(improve_prompt, prompt)
            except Exception as e:
                st.error(f"Error al mejorar el prompt: {str(e)}")
                improved_prompt = prompt
    else:
        improved_prompt = prompt

    if st.sidebar.button("Generar Imagen"):
        with st.spinner("Generando imagen..."):
            try:
                result = run_async(gen, improved_prompt, width, height, model_option)  # Usar el improved_prompt
                st.session_state['generated_image_path'] = result
                st.image(result, caption="Imagen Generada")
            except Exception as e:
                st.error(f"Error al generar la imagen: {str(e)}")

    if generated_image_path:
        if upscale_checkbox:
            with st.spinner("Escalando imagen..."):
                try:
                    upscale_image_path = get_upscale_finegrain("Upscale", generated_image_path, 2)
                    if upscale_image_path:
                        st.image(upscale_image_path, caption="Imagen Escalada")
                except Exception as e:
                    st.error(f"Error al escalar la imagen: {str(e)}")

        st.header("Intercambio de Rostros")
        source_image_file = st.file_uploader("Imagen de Origen", type=["jpg", "jpeg", "png"])

        if source_image_file is not None:
            try:
                source_image = Image.open(source_image_file)
            except Exception as e:
                st.error(f"Error al cargar la imagen de origen: {str(e)}")
                source_image = None
        else:
            source_image = Image.open("face.jpg")

        source_face_index = st.number_input('Posición del Rostro', min_value=1, value=1, key="source_face_index")
        destination_face_index = st.number_input('Posición del Rostro de Destino', min_value=1, value=1, key="destination_face_index")

        if st.button("Intercambiar Rostros"):
            try:
                destination_image = Image.open(generated_image_path)
                result_image = swap_faces(np.array(source_image), source_face_index, np.array(destination_image), destination_face_index)
                swapped_image = Image.fromarray(result_image)
                
                swapped_image_path = save_image(swapped_image, f"swapped_image_{PREDEFINED_SEED}.jpg")
                if swapped_image_path:
                    st.image(swapped_image, caption="Intercambio de Rostro")
                    os.remove(generated_image_path)
                else:
                    st.warning("La imagen intercambiada ya existe en la galería.")
            except Exception as e:
                st.error(f"Ocurrió un error al intercambiar rostros: {str(e)}")

    display_gallery()

if __name__ == "__main__":
    main()