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File size: 4,597 Bytes
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import gradio as gr
import os
import json
import torch
from dotenv import load_dotenv
import logging
# Configure logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
handlers=[
logging.StreamHandler(),
logging.FileHandler("app.log")
]
)
logger = logging.getLogger(__name__)
# Load environment variables
load_dotenv()
# Load config file
def load_config(config_path="transformers_config.json"):
try:
with open(config_path, 'r') as f:
config = json.load(f)
return config
except Exception as e:
logger.error(f"Error loading config: {str(e)}")
return {}
# Load configuration
config = load_config()
model_config = config.get("model_config", {})
# Model details from config
MODEL_NAME = model_config.get("model_name_or_path", "unsloth/DeepSeek-R1-Distill-Qwen-14B-bnb-4bit")
SPACE_NAME = os.getenv("HF_SPACE_NAME", "phi4training")
TRAINING_ACTIVE = os.path.exists("TRAINING_ACTIVE")
# Create Gradio interface - training status only, no model outputs
with gr.Blocks(css="footer {visibility: hidden}") as demo:
gr.Markdown(f"# {SPACE_NAME}: Training Status Dashboard")
with gr.Row():
with gr.Column():
status = gr.Markdown(
f"""
## Research Training Phase Active
**Model**: {MODEL_NAME}
**Dataset**: phi4-cognitive-dataset
This is a multidisciplinary research training phase. The model is not available for interactive use.
### Training Configuration:
- **Epochs**: {config.get("training_config", {}).get("num_train_epochs", 3)}
- **Batch Size**: {config.get("training_config", {}).get("per_device_train_batch_size", 2)}
- **Gradient Accumulation Steps**: {config.get("training_config", {}).get("gradient_accumulation_steps", 4)}
- **Learning Rate**: {config.get("training_config", {}).get("learning_rate", 2e-5)}
- **Max Sequence Length**: {config.get("training_config", {}).get("max_seq_length", 2048)}
### Training Status:
{"🟢 Training in progress" if TRAINING_ACTIVE else "⚪ Training not currently active"}
⚠️ **NOTE**: This space does not provide model outputs during the research training phase.
"""
)
# Add a refresh button to check status
refresh_btn = gr.Button("Refresh Status")
def refresh_status():
# Re-check if training is active
training_active = os.path.exists("TRAINING_ACTIVE")
return f"""
## Research Training Phase Active
**Model**: {MODEL_NAME}
**Dataset**: phi4-cognitive-dataset
This is a multidisciplinary research training phase. The model is not available for interactive use.
### Training Configuration:
- **Epochs**: {config.get("training_config", {}).get("num_train_epochs", 3)}
- **Batch Size**: {config.get("training_config", {}).get("per_device_train_batch_size", 2)}
- **Gradient Accumulation Steps**: {config.get("training_config", {}).get("gradient_accumulation_steps", 4)}
- **Learning Rate**: {config.get("training_config", {}).get("learning_rate", 2e-5)}
- **Max Sequence Length**: {config.get("training_config", {}).get("max_seq_length", 2048)}
### Training Status:
{"🟢 Training in progress" if training_active else "⚪ Training not currently active"}
⚠️ **NOTE**: This space does not provide model outputs during the research training phase.
"""
refresh_btn.click(refresh_status, outputs=status)
gr.Markdown("""
### Research Training Information
This model is being fine-tuned on research-focused datasets and is not available for interactive querying.
Training logs are available to authorized researchers only.
""")
# Launch the interface
if __name__ == "__main__":
# Create an empty TRAINING_ACTIVE file to indicate training is in progress
# This would be managed by the actual training script
if not os.path.exists("TRAINING_ACTIVE"):
with open("TRAINING_ACTIVE", "w") as f:
f.write("Training in progress")
# Start Gradio with minimal features
logger.info("Starting training status dashboard")
demo.launch(share=False, enable_queue=False) |