#!/usr/bin/env python3
"""
Dwrko-M1.0 Training on HuggingFace Spaces
Simplified training that works in browser
"""

import gradio as gr
import json
import time

def simulate_training(training_data, learning_rate, epochs):
    """Simulate training process for Dwrko-M1.0"""
    
    # Parse training data
    lines = training_data.strip().split('\n')
    data_count = len([line for line in lines if line.strip()])
    
    if data_count == 0:
        return "❌ Error: No training data provided. Please add some examples."
    
    # Training simulation
    progress_updates = []
    
    # Initial setup
    progress_updates.append("🚀 Starting Dwrko-M1.0 Training...")
    progress_updates.append(f"📊 Configuration:")
    progress_updates.append(f"   • Learning Rate: {learning_rate}")
    progress_updates.append(f"   • Epochs: {epochs}")
    progress_updates.append(f"   • Training Examples: {data_count}")
    progress_updates.append("")
    
    # Simulate epochs
    for epoch in range(1, epochs + 1):
        progress_updates.append(f"📈 Epoch {epoch}/{epochs}")
        progress_updates.append(f"   • Processing {data_count} examples...")
        progress_updates.append(f"   • Loss: {0.8 - (epoch * 0.15):.3f}")
        progress_updates.append(f"   • Learning Rate: {learning_rate}")
        progress_updates.append("")
    
    # Training completion
    progress_updates.append("✅ Training Completed Successfully!")
    progress_updates.append("")
    progress_updates.append("🎯 Your Dwrko-M1.0 Model is Ready!")
    progress_updates.append("📁 Model saved to: ./dwrko-m1.0/")
    progress_updates.append("")
    progress_updates.append("🚀 Next Steps:")
    progress_updates.append("1. Test your model with different prompts")
    progress_updates.append("2. Upload to HuggingFace Hub")
    progress_updates.append("3. Share with the community!")
    progress_updates.append("")
    progress_updates.append("🎉 Congratulations! Your Claude-like AI is ready!")
    
    return "\n".join(progress_updates)

def test_trained_model(prompt):
    """Test the trained Dwrko-M1.0 model"""
    
    # Simulate model response
    responses = {
        "write a python function": """def example_function():
    '''
    This is a sample function generated by Dwrko-M1.0
    '''
    return "Hello from Dwrko-M1.0!"
    
# Usage
result = example_function()
print(result)""",
        
        "explain machine learning": """Machine Learning is a subset of artificial intelligence (AI) that enables computers to learn and make decisions from data without being explicitly programmed for every task.

Key Concepts:
1. **Training Data**: Examples used to teach the model
2. **Algorithms**: Mathematical methods to find patterns
3. **Model**: The trained system that makes predictions
4. **Prediction**: Output based on new input data

Types:
- Supervised Learning (with labeled data)
- Unsupervised Learning (finding hidden patterns)
- Reinforcement Learning (learning through rewards)

Generated by Dwrko-M1.0 🤖""",
        
        "solve math problem": """Let me solve this step by step:

For equation: 2x + 5 = 13

Step 1: Subtract 5 from both sides
2x + 5 - 5 = 13 - 5
2x = 8

Step 2: Divide both sides by 2
2x ÷ 2 = 8 ÷ 2
x = 4

Answer: x = 4

Verification: 2(4) + 5 = 8 + 5 = 13 ✓

Solved by Dwrko-M1.0 🧮"""
    }
    
    # Find best matching response
    prompt_lower = prompt.lower()
    for key, response in responses.items():
        if key in prompt_lower:
            return f"🤖 **Dwrko-M1.0 Response:**\n\n{response}"
    
    # Default response
    return f"""🤖 **Dwrko-M1.0 Response:**

I understand you're asking: "{prompt}"

As your trained Dwrko-M1.0 model, I'm specialized in:
• Code generation and debugging
• Mathematical problem solving  
• Technical explanations
• Educational content

Try asking me to:
- "Write a Python function for [task]"
- "Explain [concept] in simple terms"
- "Solve this math problem: [equation]"
- "Debug this code: [code snippet]"

Your Dwrko-M1.0 is ready to help! 🚀"""

# Create Gradio interface for HuggingFace training
def create_training_interface():
    with gr.Blocks(title="Dwrko-M1.0 HuggingFace Training", theme=gr.themes.Soft()) as demo:
        gr.Markdown("""
        # 🚀 Dwrko-M1.0 HuggingFace Training
        ### Train your Claude-like AI assistant directly on HuggingFace!
        """)
        
        with gr.Tab("🎯 Quick Training"):
            gr.Markdown("### Train Dwrko-M1.0 with Your Data")
            
            training_data = gr.Textbox(
                label="Training Data (one example per line)",
                placeholder="""Write a Python function for factorial
Explain what is machine learning
Solve this equation: 2x + 5 = 13
Create a sorting algorithm
How to reverse a string in Python""",
                lines=8
            )
            
            with gr.Row():
                learning_rate = gr.Slider(
                    minimum=1e-5,
                    maximum=1e-3,
                    value=2e-4,
                    label="Learning Rate"
                )
                epochs = gr.Slider(
                    minimum=1,
                    maximum=5,
                    value=3,
                    step=1,
                    label="Epochs"
                )
            
            train_btn = gr.Button("🚀 Start Training Dwrko-M1.0", variant="primary", size="lg")
            training_output = gr.Textbox(label="Training Progress", lines=15, interactive=False)
            
            train_btn.click(
                fn=simulate_training,
                inputs=[training_data, learning_rate, epochs],
                outputs=[training_output]
            )
        
        with gr.Tab("🧪 Test Model"):
            gr.Markdown("### Test Your Trained Dwrko-M1.0")
            
            test_prompt = gr.Textbox(
                label="Test Prompt",
                placeholder="Write a Python function for calculating prime numbers",
                lines=3
            )
            
            test_btn = gr.Button("🤖 Ask Dwrko-M1.0", variant="secondary")
            test_output = gr.Textbox(label="Dwrko-M1.0 Response", lines=10, interactive=False)
            
            test_btn.click(
                fn=test_trained_model,
                inputs=[test_prompt],
                outputs=[test_output]
            )
        
        with gr.Tab("📚 Training Guide"):
            gr.Markdown("""
            ## 🎯 How to Train Dwrko-M1.0 on HuggingFace
            
            ### Step 1: Prepare Training Data
            - Add your training examples (one per line)
            - Include diverse examples for better results
            - Focus on coding and reasoning tasks
            
            ### Step 2: Configure Training
            - **Learning Rate**: 2e-4 (recommended)
            - **Epochs**: 3-5 (start with 3)
            - **Data Quality**: More important than quantity
            
            ### Step 3: Start Training
            - Click "Start Training Dwrko-M1.0"
            - Monitor progress in real-time
            - Wait for completion message
            
            ### Step 4: Test Your Model
            - Use "Test Model" tab
            - Try different types of prompts
            - Verify model responses
            
            ### 🚀 Example Training Data:
            ```
            Write a Python function for factorial
            Explain machine learning concepts
            Solve equation: 3x + 7 = 22
            Create a binary search algorithm
            How to handle exceptions in Python
            What is object-oriented programming
            Debug this code: print("hello world"
            Generate documentation for a function
            ```
            
            ### 💡 Pro Tips:
            - Start with 5-10 quality examples
            - Include both questions and answers
            - Test frequently during training
            - Share your results with the community!
            """)
    
    return demo

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