app.py

import torch
from torch import nn, optim
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms, datasets, models
from PIL import Image
import json
import os
import gradio as gr

# Paths
image_folder = "Images/"
metadata_file = "descriptions.json"

# Define the function to load metadata
def load_metadata(metadata_file):
    with open(metadata_file, 'r') as f:
        metadata = json.load(f)
    return metadata

# Custom Dataset Class
class ImageDescriptionDataset(Dataset):
    def __init__(self, image_folder, metadata):
        self.image_folder = image_folder
        self.metadata = metadata
        self.image_names = list(metadata.keys())  # List of image filenames
        self.transform = transforms.Compose([
            transforms.Resize((512, 512)),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
        ])

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

    def __getitem__(self, idx):
        image_name = self.image_names[idx]
        image_path = os.path.join(self.image_folder, image_name)
        image = Image.open(image_path).convert("RGB")
        description = self.metadata[image_name]
        image = self.transform(image)
        return image, description

# LoRA Layer Implementation
class LoRALayer(nn.Module):
    def __init__(self, original_layer, rank=4):
        super(LoRALayer, self).__init__()
        self.original_layer = original_layer
        self.rank = rank
        self.lora_up = nn.Linear(original_layer.in_features, rank, bias=False)
        self.lora_down = nn.Linear(rank, original_layer.out_features, bias=False)

    def forward(self, x):
        return self.original_layer(x) + self.lora_down(self.lora_up(x))

# LoRA Model Class
class LoRAModel(nn.Module):
    def __init__(self):
        super(LoRAModel, self).__init__()
        self.backbone = models.resnet18(pretrained=True)  # Base model
        self.backbone.fc = LoRALayer(self.backbone.fc)  # Replace the final layer with LoRA

    def forward(self, x):
        return self.backbone(x)

# Training Function
def train_lora(image_folder, metadata):
    print("Starting LoRA training process...")

    # Create dataset and dataloader
    dataset = ImageDescriptionDataset(image_folder, metadata)
    dataloader = DataLoader(dataset, batch_size=8, shuffle=True)

    # Initialize model, loss function, and optimizer
    model = LoRAModel()
    criterion = nn.CrossEntropyLoss()  # Update this if your task changes
    optimizer = optim.Adam(model.parameters(), lr=0.001)

# Training loop
num_epochs = 5  # Adjust the number of epochs based on your needs
for epoch in range(num_epochs):
    print(f"Epoch {epoch + 1}/{num_epochs}")
    for batch_idx, (images, descriptions) in enumerate(dataloader):
        # Convert descriptions to a numerical format (if applicable)
        labels = torch.randint(0, 100, (images.size(0),))  # Placeholder labels

        # Forward pass
        outputs = model(images)
        loss = criterion(outputs, labels)
        
        # Backward pass
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        if batch_idx % 10 == 0:  # Log every 10 batches
            print(f"Batch {batch_idx}, Loss: {loss.item()}")

# Save the trained model
torch.save(model.state_dict(), "lora_model.pth")
print("Model saved as lora_model.pth")

print("Training completed.")

# Gradio App
def start_training_gradio():
    print("Loading metadata and preparing dataset...")
    metadata = load_metadata(metadata_file)
    train_lora(image_folder, metadata)
    return "Training completed. Check the model outputs!"

demo = gr.Interface(
    fn=start_training_gradio,
    inputs=None,
    outputs="text",
    title="Train LoRA Model",
    description="Fine-tune a model using LoRA for consistent image generation."
)

demo.launch()