Create app.py

app.py

@@ -0,0 +1,63 @@
+import gradio as gr
+import subprocess
+import threading
+import time
+
+def run_training(model_name, progress=gr.Progress()):
+    progress(0, desc="Starting Training...")
+    
+    command = ["python", "train.py", "--model_name", model_name]
+    
+    # Start the training process
+    process = subprocess.Popen(
+        command, 
+        stdout=subprocess.PIPE, 
+        stderr=subprocess.STDOUT, 
+        text=True, 
+        bufsize=1, 
+        universal_newlines=True
+    )
+
+    # Stream the output
+    output = ""
+    for line in process.stdout:
+        output += line
+        # Update progress based on keywords in the output
+        if "Starting training..." in line:
+            progress(0.1, desc="Training Started...")
+        elif "loss" in line:
+             progress(0.5, desc=line.strip())
+        
+        yield output
+    
+    process.wait()
+    
+    if process.returncode == 0:
+        progress(1, desc="Training Complete!")
+        yield output + "\nTraining finished successfully!"
+    else:
+        yield output + f"\nTraining failed with error code {process.returncode}."
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Llama Model Fine-Tuning")
+    gr.Markdown("Select a model and click 'Start Fine-Tuning' to begin.")
+
+    models = [
+        'meta-llama/Meta-Llama-3-8B-Instruct',
+        'meta-llama/Meta-Llama-3-70B-Instruct',
+        'meta-llama/Llama-3.3-70B-Instruct'
+    ]
+    model_dropdown = gr.Dropdown(models, label="Select Model to Fine-Tune", value='meta-llama/Meta-Llama-3-8B-Instruct')
+    
+    start_button = gr.Button("Start Fine-Tuning")
+    
+    output_textbox = gr.Textbox(label="Training Log", lines=20, interactive=False)
+    
+    start_button.click(
+        fn=run_training,
+        inputs=[model_dropdown],
+        outputs=[output_textbox]
+    )
+
+demo.launch()