app.py

import os
import redis
import pickle
import torch
from PIL import Image
from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, FluxPipeline, DiffusionPipeline, DPMSolverMultistepScheduler
from diffusers.utils import export_to_video
from transformers import pipeline as transformers_pipeline, AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer
from audiocraft.models import MusicGen
import gradio as gr
from huggingface_hub import snapshot_download, HfApi, HfFolder
import multiprocessing
import io
import time

# Obtener las variables de entorno
hf_token = os.getenv("HF_TOKEN")
redis_host = os.getenv("REDIS_HOST")
redis_port = os.getenv("REDIS_PORT")
redis_password = os.getenv("REDIS_PASSWORD")

HfFolder.save_token(hf_token)

def connect_to_redis():
    while True:
        try:
            redis_client = redis.Redis(host=redis_host, port=redis_port, password=redis_password)
            redis_client.ping()  # Verifica si la conexión está activa
            print("Connected to Redis successfully.")
            return redis_client
        except (redis.exceptions.ConnectionError, redis.exceptions.TimeoutError, BrokenPipeError) as e:
            print(f"Connection to Redis failed: {e}. Retrying in 1 second...")
            time.sleep(1)

def reconnect_if_needed(redis_client):
    try:
        redis_client.ping()
    except (redis.exceptions.ConnectionError, redis.exceptions.TimeoutError, BrokenPipeError):
        print("Reconnecting to Redis...")
        return connect_to_redis()
    return redis_client

def load_object_from_redis(key):
    redis_client = connect_to_redis()
    redis_client = reconnect_if_needed(redis_client)
    try:
        obj_data = redis_client.get(key)
        return pickle.loads(obj_data) if obj_data else None
    except (pickle.PickleError, redis.exceptions.RedisError) as e:
        print(f"Failed to load object from Redis: {e}")
        return None

def save_object_to_redis(key, obj):
    redis_client = connect_to_redis()
    redis_client = reconnect_if_needed(redis_client)
    try:
        if not redis_client.exists(key):  # Solo guarda si no existe
            redis_client.set(key, pickle.dumps(obj))
            print(f"Object saved to Redis: {key}")
    except redis.exceptions.RedisError as e:
        print(f"Failed to save object to Redis: {e}")

def get_model_or_download(model_id, redis_key, loader_func):
    model = load_object_from_redis(redis_key)
    if model:
        print(f"Model loaded from Redis: {redis_key}")
        return model
    model = loader_func(model_id, torch_dtype=torch.float16)
    save_object_to_redis(redis_key, model)
    print(f"Model downloaded and saved to Redis: {redis_key}")
    return model

def generate_image(prompt):
    redis_key = f"generated_image_{prompt}"
    image = load_object_from_redis(redis_key)
    if not image:
        image = text_to_image_pipeline(prompt).images[0]
        save_object_to_redis(redis_key, image)
    return image

def edit_image_with_prompt(image, prompt, strength=0.75):
    redis_key = f"edited_image_{prompt}_{strength}"
    edited_image = load_object_from_redis(redis_key)
    if not edited_image:
        edited_image = img2img_pipeline(prompt=prompt, init_image=image.convert("RGB"), strength=strength).images[0]
        save_object_to_redis(redis_key, edited_image)
    return edited_image

def generate_song(prompt, duration=10):
    redis_key = f"generated_song_{prompt}_{duration}"
    song = load_object_from_redis(redis_key)
    if not song:
        song = music_gen.generate(prompt, duration=duration)
        save_object_to_redis(redis_key, song)
    return song

def generate_text(prompt):
    redis_key = f"generated_text_{prompt}"
    text = load_object_from_redis(redis_key)
    if not text:
        text = text_gen_pipeline([{"role": "user", "content": prompt}], max_new_tokens=256)[0]["generated_text"].strip()
        save_object_to_redis(redis_key, text)
    return text

def generate_flux_image(prompt):
    redis_key = f"generated_flux_image_{prompt}"
    flux_image = load_object_from_redis(redis_key)
    if not flux_image:
        flux_image = flux_pipeline(
            prompt,
            guidance_scale=0.0,
            num_inference_steps=4,
            max_sequence_length=256,
            generator=torch.Generator("cpu").manual_seed(0)
        ).images[0]
        save_object_to_redis(redis_key, flux_image)
    return flux_image

def generate_code(prompt):
    redis_key = f"generated_code_{prompt}"
    code = load_object_from_redis(redis_key)
    if not code:
        inputs = starcoder_tokenizer.encode(prompt, return_tensors="pt").to("cuda")
        outputs = starcoder_model.generate(inputs)
        code = starcoder_tokenizer.decode(outputs[0])
        save_object_to_redis(redis_key, code)
    return code

def generate_video(prompt):
    redis_key = f"generated_video_{prompt}"
    video = load_object_from_redis(redis_key)
    if not video:
        pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16)
        pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
        pipe.enable_model_cpu_offload()
        video = export_to_video(pipe(prompt, num_inference_steps=25).frames)
        save_object_to_redis(redis_key, video)
    return video

def test_model_meta_llama():
    redis_key = "meta_llama_test_response"
    response = load_object_from_redis(redis_key)
    if not response:
        messages = [
            {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
            {"role": "user", "content": "Who are you?"}
        ]
        response = meta_llama_pipeline(messages, max_new_tokens=256)[0]["generated_text"].strip()
        save_object_to_redis(redis_key, response)
    return response

def train_model(model, dataset, epochs, batch_size, learning_rate):
    output_dir = io.BytesIO()
    training_args = TrainingArguments(
        output_dir=output_dir,
        num_train_epochs=epochs,
        per_device_train_batch_size=batch_size,
        learning_rate=learning_rate,
    )
    trainer = Trainer(model=model, args=training_args, train_dataset=dataset)
    trainer.train()
    save_object_to_redis("trained_model", model)
    save_object_to_redis("training_results", output_dir.getvalue())

def run_task(task_queue):
    while True:
        task = task_queue.get()
        if task is None:
            break
        func, args, kwargs = task
        func(*args, **kwargs)

task_queue = multiprocessing.Queue()
num_processes = multiprocessing.cpu_count()

processes = []
for _ in range(num_processes):
    p = multiprocessing.Process(target=run_task, args=(task_queue,))
    p.start()
    processes.append(p)

device = "cuda" if torch.cuda.is_available() else "cpu"
text_to_image_pipeline = get_model_or_download("CompVis/stable-diffusion-v1-4", "text_to_image_model", StableDiffusionPipeline.from_pretrained).to(device)
img2img_pipeline = get_model_or_download("runwayml/stable-diffusion-inpainting", "img2img_model", StableDiffusionImg2ImgPipeline.from_pretrained).to(device)
flux_pipeline = get_model_or_download("black-forest-labs/FLUX.1-schnell", "flux_model", FluxPipeline.from_pretrained)
flux_pipeline.enable_model_cpu_offload()
music_gen = load_object_from_redis("music_gen") or MusicGen.get_pretrained('melody', use_auth_token=hf_token)
save_object_to_redis("music_gen", music_gen)
text_gen_pipeline = load_object_from_redis("text_gen_pipeline") or transformers_pipeline(
    "text-generation",
    model="google/gemini-2-2b-it",
    model_kwargs={"torch_dtype": torch.bfloat16},
    device=device,
    use_auth_token=hf_token,
)
save_object_to_redis("text_gen_pipeline", text_gen_pipeline)
starcoder_tokenizer = AutoTokenizer.from_pretrained("bigcode/starcoder2-15b", use_auth_token=hf_token)
starcoder_model = AutoModelForCausalLM.from_pretrained("bigcode/starcoder2-15b", device_map="auto", torch_dtype=torch.bfloat16, use_auth_token=hf_token)
meta_llama_pipeline = transformers_pipeline(
    "text-generation",
    model="meta-llama/Meta-Llama-3.1-8B-Instruct",
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",
    use_auth_token=hf_token
)

gen_image_tab = gr.Interface(generate_image, gr.inputs.Textbox(label="Prompt:"), gr.outputs.Image(type="pil"), title="Generate Images")
edit_image_tab = gr.Interface(edit_image_with_prompt, [gr.inputs.Image(type="pil", label="Image:"), gr.inputs.Textbox(label="Prompt:"), gr.inputs.Slider(0.1, 1.0, 0.75, step=0.05, label="Strength:")], gr.outputs.Image(type="pil"), title="Edit Images")
generate_song_tab = gr.Interface(generate_song, [gr.inputs.Textbox(label="Prompt:"), gr.inputs.Slider(5, 60, 10, step=1, label="Duration (s):")], gr.outputs.Audio(type="numpy"), title="Generate Songs")
generate_text_tab = gr.Interface(generate_text, gr.inputs.Textbox(label="Prompt:"), gr.outputs.Textbox(label="Generated Text:"), title="Generate Text")
generate_flux_image_tab = gr.Interface(generate_flux_image, gr.inputs.Textbox(label="Prompt:"), gr.outputs.Image(type="pil"), title="Generate FLUX Images")
model_meta_llama_test_tab = gr.Interface(test_model_meta_llama, gr.inputs.Textbox(label="Test Input:"), gr.outputs.Textbox(label="Model Output:"), title="Test Meta-Llama")

app = gr.TabbedInterface(
    [gen_image_tab, edit_image_tab, generate_song_tab, generate_text_tab, generate_flux_image_tab, model_meta_llama_test_tab],
    ["Generate Image", "Edit Image", "Generate Song", "Generate Text", "Generate FLUX Image", "Test Meta-Llama"]
)

app.launch(share=True)

for _ in range(num_processes):
    task_queue.put(None)
for p in processes:
    p.join()