"""
Hugging Face Spaces Deployment Entry Point
Lightweight dashboard entry point without heavy dependencies
"""
import os
import sys
# Set up environment
os.environ['GRADIO_SERVER_NAME'] = '0.0.0.0'
os.environ['GRADIO_SERVER_PORT'] = '7860'
# Import Gradio
import gradio as gr
from dashboard_simple import (
load_metrics,
get_summary_info,
create_comparison_charts,
format_metrics_text,
get_artifacts_info
)
# ----------- CSS (works on all Gradio versions) -----------
custom_css = """
"""
if __name__ == "__main__":
metrics = load_metrics()
r2_chart, rmse_chart = create_comparison_charts(metrics)
artifacts = get_artifacts_info()
rf_r2 = metrics.get('train', {}).get('r2_rf', 'N/A')
rf_rmse = metrics.get('train', {}).get('rmse_rf', 'N/A')
lr_r2 = metrics.get('train', {}).get('r2_lr', 'N/A')
lr_rmse = metrics.get('train', {}).get('rmse_lr', 'N/A')
# Dropdown logic
def show_model_details(choice):
if choice == "Random Forest":
return f"### 🌳 Random Forest Selected\n- R² Score: {rf_r2}\n- RMSE: {rf_rmse}"
else:
return f"### 📏 Linear Regression Selected\n- R² Score: {lr_r2}\n- RMSE: {lr_rmse}"
# ----------- FIXED: No css= argument -----------
with gr.Blocks(title="CPU Optimization Dashboard") as demo:
# Inject CSS safely
gr.HTML(custom_css)
gr.HTML("🚀 CPU Optimization ML Dashboard
")
gr.Markdown("Real-time visualization of machine learning models for CPU usage prediction")
# ------------ DROPDOWN ------------
with gr.Row():
model_selector = gr.Dropdown(
["Random Forest", "Linear Regression"],
label="Select Model",
value="Random Forest"
)
model_output = gr.Markdown()
model_selector.change(show_model_details, inputs=model_selector, outputs=model_output)
# ---------------------- TABS ----------------------
with gr.Tabs():
with gr.Tab("📋 Overview"):
gr.HTML(f"""
Dashboard Overview
✓ Dashboard is running and all models are loaded successfully
Key Metrics
- Best Model: Random Forest
- Best R² Score: {rf_r2}
- Best RMSE: {rf_rmse}
- Models Available: 2
Quick Stats
- ✓ Random Forest Model: Loaded
- ✓ Linear Regression Model: Loaded
- ✓ Training Metrics: Available
- ✓ Evaluation Metrics: Available
""")
# COMPARISON
with gr.Tab("📈 Comparison"):
gr.Markdown(f"""
Model Performance Comparison
Random Forest:
R² = {rf_r2}, RMSE = {rf_rmse}
Linear Regression:
R² = {lr_r2}, RMSE = {lr_rmse}
🎯 Winner: Random Forest 🏆
- ✓ Higher R² Score
- ✓ Lower RMSE
""")
gr.HTML(r2_chart)
gr.HTML(rmse_chart)
# METRICS
with gr.Tab("📊 Metrics"):
gr.Markdown(f"""
Model Metrics Detail
🌳 Random Forest
- R² Score: {rf_r2}
- RMSE: {rf_rmse}
- Model Type: Ensemble (100 trees)
- Performance: Excellent
📏 Linear Regression
- R² Score: {lr_r2}
- RMSE: {lr_rmse}
- Model Type: Linear (Baseline)
- Performance: Baseline
""")
# ARTIFACTS
with gr.Tab("📦 Artifacts"):
gr.Markdown(f"\n
Model Artifacts
\n{artifacts}\n")
# INFO
with gr.Tab("ℹ️ Information"):
gr.Markdown("""
System Information
Dashboard Configuration
- Dashboard Version: 2.0.0
- Gradio Version: 3.50.0
- Python Version: 3.11+
- Deployment: Hugging Face Spaces
Deployment Ready
- ✓ Works in HF Spaces
- ✓ Docker Compatible
- ✓ Cloud-Deployable
""")
gr.Markdown("---")
gr.Markdown("**CPU Optimization ML Dashboard** | Built with Gradio & Scikit-learn")
demo.launch(server_name="0.0.0.0", server_port=7860, share=False)