Update tuner.py

tuner.py

@@ -0,0 +1,56 @@
+import os
+import gradio as gr
+import matplotlib.pyplot as plt
+from transformers import ViTForImageClassification, TrainingArguments, Trainer
+from datasets import load_dataset
+
+def finetune_model(epochs, save_at_num_epoch, learning_rate):
+    # Load the dataset
+    dataset = load_dataset("imagenet")
+
+    # Initialize the model
+    model = ViTForImageClassification.from_pretrained("google/vit-base-patch16-224")
+
+    # Define the training arguments
+    training_args = TrainingArguments(
+        output_dir="vit-finetuned",
+        num_train_epochs=epochs,
+        save_strategy="steps",
+        save_steps=save_at_num_epoch,
+        learning_rate=learning_rate,
+    )
+
+    # Create the trainer and fine-tune the model
+    trainer = Trainer(model=model, args=training_args, train_dataset=dataset["train"])
+    train_metrics = trainer.train()
+
+    # Save the fine-tuned model
+    model.save_pretrained("vit-finetuned")
+
+    # Plot the loss graph
+    plt.figure(figsize=(8, 6))
+    plt.plot(train_metrics.history["loss"])
+    plt.title("Model Loss")
+    plt.xlabel("Epoch")
+    plt.ylabel("Loss")
+    plt.savefig("loss_graph.png")
+
+    return "Fine-tuning complete!"
+
+# Create the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Fine-Tune a Model")
+
+    with gr.Column():
+        epochs = gr.Slider(label="Epochs", minimum=1, maximum=10, value=3)
+        save_at_num_epoch = gr.Slider(label="Save Model Every X Epochs", minimum=1, maximum=epochs, value=1)
+        learning_rate = gr.Slider(label="Learning Rate", minimum=1e-5, maximum=1e-3, value=2e-5)
+        run_button = gr.Button("Fine-Tune Model")
+
+    status = gr.Textbox(label="Fine-Tuning Status")
+    loss_graph = gr.Image(label="Loss Graph")
+
+    run_button.click(finetune_model, inputs=[epochs, save_at_num_epoch, learning_rate], outputs=[status, loss_graph])
+
+if __name__ == "__main__":
+    demo.launch()