app.py

import torch
from torch import nn, optim
from torchvision import transforms, datasets, models
from torch.utils.data import DataLoader, Dataset
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((224, 224)),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
        ])

    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")  # Open the image and convert to RGB
        description = self.metadata[image_name]  # Get description for the image
        image = self.transform(image)  # Apply transformations
        return image, description

# LoRA Model Class (This is a placeholder, you'll need to implement the actual LoRA model)
class LoRAModel(nn.Module):
    def __init__(self):
        super(LoRAModel, self).__init__()
        self.backbone = models.resnet18(weights=models.ResNet18_Weights.DEFAULT)  # Use the updated weights argument
        # Adjust the final fully connected layer to match the output of the backbone
        self.fc = nn.Linear(self.backbone.fc.in_features, 100)  # Placeholder output layer

    def forward(self, x):
        x = self.backbone(x)  # Pass through the ResNet18 backbone
        x = self.fc(x)  # Apply the final fully connected layer
        return x


# Function to train LoRA
def train_lora(image_folder, metadata):
    print("Starting training process...")
    
    # Create dataset and dataloaders
    dataset = ImageDescriptionDataset(image_folder, metadata)
    dataloader = DataLoader(dataset, batch_size=8, shuffle=True)
    
    # Initialize model, loss, and optimizer
    model = LoRAModel()
    criterion = nn.CrossEntropyLoss()  # Placeholder loss function, can be adjusted
    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):
            # Here we would convert descriptions to a numerical format
            # Since it's a placeholder, we use random labels for descriptions
            labels = torch.randint(0, 100, (images.size(0),))  # Random labels as a placeholder

            # 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()}")

    print("Training completed.")

# Gradio app function to load metadata and start training
def start_training_gradio():
    print("Preparing dataset...")
    metadata = load_metadata(metadata_file)  # Load metadata
    return train_lora(image_folder, metadata)

# Gradio interface
demo = gr.Interface(
    fn=start_training_gradio,  # Use the new function name here
    inputs=None,
    outputs="text",
    title="Train LoRA on Your Dataset",
    description="Click below to start training with the uploaded images and metadata."
)

demo.launch()