import gradio as gr
import os

def main():
    """Main Gradio interface for CELESTIAL Training Notebooks"""
    
    with gr.Blocks(title="CELESTIAL AI Training Notebooks", theme=gr.themes.Soft()) as demo:
        gr.Markdown("""
        # 🌟 CELESTIAL AI Training Notebooks v2.0
        
        ## Complete Spiritual AI Training with Mistral-7B
        
        This Space contains comprehensive Jupyter notebooks for training CELESTIAL's spiritual AI:
        
        ### 📚 **Available Notebooks:**
        
        1. **🤖 Main Training Notebook** - `CELESTIAL_Mistral_7B_Training.ipynb`
           - Complete Mistral-7B fine-tuning with LoRA
           - 9,000+ training examples across 50+ spiritual features
           - Production-ready training pipeline
           
        2. **🎯 Domain Adapters Notebook** - `CELESTIAL_Domain_Adapters_Training.ipynb`
           - Hot-swappable LoRA adapters for specialized expertise
           - 5 domain-specific adapters (Astrology, Krishna, Vastu, Panchang, Remedies)
           - Adapter testing and deployment
        
        ### 🚀 **Quick Start:**
        
        1. **Open in Google Colab**: Click the Colab links below
        2. **Login to Hugging Face**: Authenticate in the notebook
        3. **Run All Cells**: Complete training pipeline
        4. **Download Models**: Get your trained CELESTIAL AI
        
        ### 📥 **Download Notebooks:**

        - [📄 Main Training Notebook](https://huggingface.co/spaces/dp1812/celestial-training-notebooks-v2/blob/main/CELESTIAL_Mistral_7B_Training.ipynb) - Download and open in Colab
        - [📄 Domain Adapters Notebook](https://huggingface.co/spaces/dp1812/celestial-training-notebooks-v2/blob/main/CELESTIAL_Domain_Adapters_Training.ipynb) - Download and open in Colab

        ### 🚀 **How to Use:**
        1. **Download** the notebook files from the links above
        2. **Upload** to Google Colab or open locally in Jupyter
        3. **Run** all cells to complete training
        4. **Download** your trained CELESTIAL AI model
        
        ### 📊 **Training Features:**
        
        - **Base Model**: Mistral-7B-Instruct-v0.3
        - **Training Data**: 9,000+ examples from comprehensive spiritual dataset
        - **Method**: LoRA (Low-Rank Adaptation) for efficient fine-tuning
        - **Domains**: Astrology, Vastu, Numerology, Divine Guidance, Remedies
        - **Output**: Production-ready spiritual AI with hot-swappable adapters
        
        ### 🎯 **Expected Results:**
        
        - **Trained Model**: Fine-tuned Mistral-7B for spiritual guidance
        - **5 Domain Adapters**: Specialized expertise for different spiritual domains
        - **Evaluation Metrics**: Comprehensive testing across all features
        - **Deployment Ready**: Models uploaded to Hugging Face Hub
        
        ### 📈 **Performance Targets:**
        
        - **Groundedness**: >95% factual accuracy using tool results
        - **Usefulness**: >90% actionable spiritual guidance
        - **Latency**: <300ms first token generation
        - **Coverage**: 100% of CELESTIAL's 50+ features
        
        ---
        
        *Built with ❤️ for spiritual AI and ancient wisdom.*
        """)
        
        with gr.Row():
            gr.HTML("""
            <div style="text-align: center; padding: 20px;">
                <h3>🚀 Ready to Train Your Spiritual AI?</h3>
                <p><strong>Step 1:</strong> Download the notebook files above</p>
                <p><strong>Step 2:</strong> Upload to Google Colab</p>
                <p><strong>Step 3:</strong> Run all cells to train your AI!</p>
                <br>
                <p><em>Training takes 2-4 hours and produces a world-class spiritual AI</em></p>
            </div>
            """)
    
    return demo

if __name__ == "__main__":
    demo = main()
    demo.launch()