Update app.py to process training data

app.py

@@ -1,13 +1,41 @@
-import gradio as gr
+import torch
+from torch.utils.data import Dataset, DataLoader
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from peft import get_peft_model, LoraConfig, TaskType
-import torch
+from PIL import Image
+import torchvision.transforms as transforms
+import os
 
 FLUX_MODEL_NAME = "black-forest-labs/FLUX"
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+class FluxDataset(Dataset):
+    def __init__(self, image_dir, prompt_file):
+        self.image_dir = image_dir
+        with open(prompt_file, 'r') as f:
+            self.prompts = [line.strip() for line in f if line.strip()]
+        self.image_files = [f"image_{i+1}.jpg" for i in range(len(self.prompts))]
+        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.prompts)
 
-def train_lora(alpha, r, lora_dropout, bias):
+    def __getitem__(self, idx):
+        img_path = os.path.join(self.image_dir, self.image_files[idx])
+        image = Image.open(img_path).convert('RGB')
+        image = self.transform(image)
+        return {
+            'image': image,
+            'prompt': self.prompts[idx]
+        }
+
+def train_lora(alpha, r, lora_dropout, bias, batch_size, num_epochs, learning_rate):
     # Load the FLUX model and tokenizer
-    model = AutoModelForCausalLM.from_pretrained(FLUX_MODEL_NAME)
+    model = AutoModelForCausalLM.from_pretrained(FLUX_MODEL_NAME).to(DEVICE)
     tokenizer = AutoTokenizer.from_pretrained(FLUX_MODEL_NAME)
 
     # Define LoRA Config
@@ -23,7 +51,44 @@ def train_lora(alpha, r, lora_dropout, bias):
     # Get the PEFT model
     model = get_peft_model(model, peft_config)
 
-    return f"LoRA model created for {FLUX_MODEL_NAME} with parameters: alpha={alpha}, r={r}, dropout={lora_dropout}, bias={bias}"
+    # Prepare dataset and dataloader
+    dataset = FluxDataset('path/to/image/directory', 'prompts.txt')
+    dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
+
+    # Optimizer
+    optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
+
+    # Training loop
+    model.train()
+    for epoch in range(num_epochs):
+        total_loss = 0
+        for batch in dataloader:
+            images = batch['image'].to(DEVICE)
+            prompts = batch['prompt']
+
+            # Tokenize prompts
+            inputs = tokenizer(prompts, return_tensors="pt", padding=True, truncation=True).to(DEVICE)
+
+            # Forward pass
+            outputs = model(input_ids=inputs.input_ids, attention_mask=inputs.attention_mask, labels=inputs.input_ids)
+            loss = outputs.loss
+
+            # Backward pass and optimize
+            loss.backward()
+            optimizer.step()
+            optimizer.zero_grad()
+
+            total_loss += loss.item()
+
+        print(f"Epoch {epoch+1}/{num_epochs}, Loss: {total_loss/len(dataloader)}")
+
+    # Save the LoRA model
+    model.save_pretrained("path/to/save/lora_model")
+
+    return "LoRA training completed and model saved."
+
+# Gradio interface
+import gradio as gr
 
 iface = gr.Interface(
     fn=train_lora,
@@ -31,7 +96,10 @@ iface = gr.Interface(
         gr.Slider(1, 100, value=32, label="LoRA Alpha"),
         gr.Slider(1, 64, value=8, label="LoRA r"),
         gr.Slider(0, 1, value=0.1, label="LoRA Dropout"),
-        gr.Checkbox(label="LoRA Bias")
+        gr.Checkbox(label="LoRA Bias"),
+        gr.Number(value=4, label="Batch Size"),
+        gr.Number(value=5, label="Number of Epochs"),
+        gr.Number(value=1e-4, label="Learning Rate")
     ],
     outputs="text"
 )