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import gradio as gr
import os
import subprocess
import sys
import threading
import time
from pathlib import Path
# Import your training code
from fine_tune_cove import train_cove_model, test_cove_inference
def install_requirements():
"""Install required packages"""
try:
subprocess.check_call([sys.executable, "-m", "pip", "install", "-r", "requirements.txt"])
return "✅ Requirements installed successfully!"
except Exception as e:
return f"❌ Error installing requirements: {str(e)}"
def start_training(hf_token, model_name="codellama/CodeLlama-7b-Instruct-hf"):
"""Start the CoVe training process"""
if not hf_token:
return "❌ Please provide a HuggingFace token"
# Set environment variables
os.environ["HF_TOKEN"] = hf_token
os.environ["SPACE_ID"] = "1" # Indicates running in Spaces
try:
# Start training in a separate thread
training_thread = threading.Thread(target=train_cove_model)
training_thread.daemon = True
training_thread.start()
return "🚀 Training started! Check the logs below for progress."
except Exception as e:
return f"❌ Error starting training: {str(e)}"
def get_training_logs():
"""Get training logs"""
log_file = "training.log"
if os.path.exists(log_file):
with open(log_file, 'r') as f:
return f.read()
return "No logs available yet. Training may not have started."
def test_model():
"""Test the trained model"""
try:
result = test_cove_inference()
return result
except Exception as e:
return f"❌ Error testing model: {str(e)}"
# Create Gradio interface
with gr.Blocks(title="CoVe Fine-tuning on HuggingFace Spaces") as demo:
gr.Markdown("# 🔗 Chain of Verification (CoVe) Fine-tuning")
gr.Markdown("Fine-tune CodeLlama with Chain of Verification for better code reasoning")
with gr.Tab("Setup & Training"):
with gr.Row():
with gr.Column():
gr.Markdown("### 1. Setup")
install_btn = gr.Button("Install Requirements", variant="secondary")
install_output = gr.Textbox(label="Installation Status", lines=2)
gr.Markdown("### 2. Training Configuration")
hf_token = gr.Textbox(
label="HuggingFace Token (Write Access)",
type="password",
placeholder="hf_xxxxxxxxxxxxxxxxxxxx"
)
model_name = gr.Textbox(
label="Base Model",
value="codellama/CodeLlama-7b-Instruct-hf",
interactive=True
)
start_btn = gr.Button("Start Training", variant="primary")
training_status = gr.Textbox(label="Training Status", lines=2)
with gr.Column():
gr.Markdown("### Training Progress")
logs = gr.Textbox(
label="Training Logs",
lines=15,
max_lines=20,
value="Logs will appear here once training starts..."
)
refresh_logs_btn = gr.Button("Refresh Logs", variant="secondary")
with gr.Tab("Model Testing"):
gr.Markdown("### Test the Fine-tuned Model")
test_btn = gr.Button("Test CoVe Model", variant="primary")
test_output = gr.Textbox(label="Model Output", lines=10)
with gr.Tab("Instructions"):
gr.Markdown("""
## How to Use This Space
### Step 1: Get Your HuggingFace Token
1. Go to [HuggingFace Settings](https://huggingface.co/settings/tokens)
2. Create a new token with **Write** access
3. Copy the token (starts with `hf_`)
### Step 2: Install Requirements
- Click "Install Requirements" button
- Wait for installation to complete
### Step 3: Start Training
1. Paste your HuggingFace token
2. Click "Start Training"
3. Monitor progress in the logs
### Step 4: Test the Model
- Once training is complete, test the model in the "Model Testing" tab
### Important Notes:
- **GPU Required**: This requires a paid GPU Space (T4 minimum)
- **Training Time**: Approximately 2-4 hours depending on GPU
- **Storage**: The model will be saved to your HuggingFace account
- **Monitoring**: Check logs regularly for progress updates
### Troubleshooting:
- If training fails, check the logs for error messages
- Ensure your HuggingFace token has write permissions
- Make sure you have sufficient GPU memory
""")
# Event handlers
install_btn.click(install_requirements, outputs=install_output)
start_btn.click(start_training, inputs=[hf_token, model_name], outputs=training_status)
refresh_logs_btn.click(get_training_logs, outputs=logs)
test_btn.click(test_model, outputs=test_output)
# Auto-refresh logs every 30 seconds during training
# Instead of demo.load(..., every=30)
gr.Timer(30, lambda: logs.update(value=get_training_logs()))
if __name__ == "__main__":
demo.launch(share=False) |