Update app.py

app.py

@@ -1,42 +1,89 @@
-import gradio as gr
+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
 
 # Paths
-image_folder = "Images/"  # Folder containing the images
-metadata_file = "descriptions.json"  # JSON file with image descriptions
+image_folder = "Images/"
+metadata_file = "descriptions.json"
 
-# Load metadata
-with open(metadata_file, "r") as f:
-    metadata = json.load(f)
-    print(f"Loaded metadata: {len(metadata)} items")  # Print the number of descriptions
+# 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]),
+        ])
 
-# Placeholder function for training LoRA
+    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(pretrained=True)  # Using a pre-trained ResNet18
+        self.fc = nn.Linear(self.backbone.fc.in_features, 100)  # Placeholder output layer
+
+    def forward(self, x):
+        x = self.backbone(x)
+        x = self.fc(x)
+        return x
+
+# Function to train LoRA
 def train_lora(image_folder, metadata):
-    print("Starting training process...")  # Log the start of the training
-    # Prepare a dataset of image paths and descriptions
-    dataset = []
-    for image_name, description in metadata.items():
-        image_path = os.path.join(image_folder, image_name)
-        if os.path.exists(image_path):  # Ensure the image file exists
-            dataset.append({"image": image_path, "description": description})
-            print(f"Added {image_name} to dataset")  # Log each added image
-        else:
-            print(f"Warning: {image_name} not found in {image_folder}")  # Log missing images
-
-    # Log how many images were successfully added
-    num_images = len(dataset)
-    print(f"Dataset prepared with {num_images} images.")
+    print("Starting training process...")
     
-    # Placeholder for training logic
-    # Replace this with your actual training code
-    print("Training LoRA with the prepared dataset...")
+    # Create dataset and dataloaders
+    dataset = ImageDescriptionDataset(image_folder, metadata)
+    dataloader = DataLoader(dataset, batch_size=8, shuffle=True)
     
-    # For now, just return a message
-    return f"Training LoRA with {num_images} images and their descriptions."
+    # 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.")
 
 # Define Gradio app
 def start_training():
+    print("Preparing dataset...")
     return train_lora(image_folder, metadata)
 
 # Gradio interface
@@ -48,5 +95,4 @@ demo = gr.Interface(
     description="Click below to start training with the uploaded images and metadata."
 )
 
-# Launch the Gradio interface
 demo.launch()