app.py

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import transforms
from datasets import load_dataset
from huggingface_hub import Repository
import gradio as gr
from PIL import Image
import os

from small_256_model import UNet as small_UNet
from big_1024_model import UNet as big_UNet

# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

big = False if device == torch.device('cpu') else True

# Parameters
IMG_SIZE = 1024 if big else 256
BATCH_SIZE = 16 if big else 1
EPOCHS = 12
LR = 0.0002
dataset_id = "K00B404/pix2pix_flux_set"
model_repo_id = "K00B404/pix2pix_flux"

# Create dataset and dataloader
class Pix2PixDataset(torch.utils.data.Dataset):
    def __init__(self, ds):
        # Filter dataset for 'original' and 'target' images
        self.originals = [x for x in ds["train"] if x['label'] == 'original']
        self.targets = [x for x in ds["train"] if x['label'] == 'target']

        # Ensure the number of original and target images match
        assert len(self.originals) == len(self.targets), "Mismatch in number of original and target images."

        # Debug: Print dataset size
        print(f"Number of original images: {len(self.originals)}")
        print(f"Number of target images: {len(self.targets)}")

    def __len__(self):
        return len(self.originals)

    def __getitem__(self, idx):
        # Load original and target images for the given index
        original_img = self.originals[idx]['image']
        target_img = self.targets[idx]['image']

        # Apply the necessary transforms
        original = Image.open(original_img).convert('RGB')
        target = Image.open(target_img).convert('RGB')

        # Return transformed original and target images
        return transform(original), transform(target)
        
# Training function
def train_model(epochs):
    # Load the dataset
    ds = load_dataset(dataset_id)

    # Transform function to resize and convert to tensor
    transform = transforms.Compose([
        transforms.Resize((IMG_SIZE, IMG_SIZE)),
        transforms.ToTensor(),
    ])


    dataset = Pix2PixDataset(ds)
    dataloader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True)

    # Initialize model, loss function, and optimizer
    try:
        model = UNet2DModel.from_pretrained(model_repo_id).to(device)
    except Exception:
        model = big_UNet().to(device) if big else small_UNet().to(device)

    criterion = nn.L1Loss()
    optimizer = optim.Adam(model.parameters(), lr=LR)

    # Training loop
    for epoch in range(epochs):
        for i, (original, target) in enumerate(dataloader):
            original, target = original.to(device), target.to(device)
            optimizer.zero_grad()

            # Forward pass
            output = model(target)  # Generate cutout image
            loss = criterion(output, original)  # Compare with original image

            # Backward pass
            loss.backward()
            optimizer.step()

            if i % 100 == 0:
                print(f"Epoch [{epoch}/{epochs}], Step [{i}/{len(dataloader)}], Loss: {loss.item():.4f}")

    # Return trained model
    return model

# Push model to Hugging Face Hub
def push_model_to_hub(model, repo_name):
    # Push the model to the Hugging Face hub
    model.push_to_hub(repo_name)

# Gradio interface function
def gradio_train(epochs):
    model = train_model(int(epochs))
    push_model_to_hub(model, model_repo_id)
    return f"Model trained for {epochs} epochs on the {dataset_id} dataset and pushed to Hugging Face Hub {model_repo_id} repository."

# Gradio Interface
gr_interface = gr.Interface(
    fn=gradio_train,
    inputs=gr.Number(label="Number of Epochs"),
    outputs="text",
    title="Pix2Pix Model Training",
    description="Train the Pix2Pix model and push it to the Hugging Face Hub repository."
)

if __name__ == '__main__':
    # Create or clone the repository if necessary
    repo = Repository(local_dir=model_repo_id, clone_from=model_repo_id)
    repo.git_pull()

    # Launch the Gradio app
    gr_interface.launch()