"""
Model Information Page for Vietnamese Sentiment Analysis
"""

import gradio as gr
import time

def create_model_info_page(app_instance):
    """Create the model information tab"""

    def update_memory_info():
        """Update memory usage information"""
        if app_instance and app_instance.model_loaded:
            memory_usage = app_instance.get_memory_usage()
            return f"Memory usage: {memory_usage:.1f}MB used"
        return "Memory usage: 0MB used"

    def manual_memory_cleanup():
        """Manual memory cleanup"""
        if app_instance and app_instance.model_loaded:
            app_instance.cleanup_memory()
            memory_usage = app_instance.get_memory_usage()
            return f"Memory cleaned. Current usage: {memory_usage:.1f}MB"
        return "App not initialized"

    # Model Info Tab
    with gr.Tab("ℹ️ Model Information"):
        gr.Markdown(f"""
        ## 🤖 Model Details

        **Model Architecture:** Transformer-based sequence classification
        **Base Model:** {app_instance.finetuned_model}
        **Languages:** Vietnamese (optimized)
        **Labels:** Negative, Neutral, Positive

        ## 📊 Performance Metrics

        - **Processing Speed:** ~100ms per text
        - **Max Sequence Length:** 512 tokens
        - **Memory Limit:** 8GB

        ## 💡 Usage Tips

        - Enter clear, grammatically correct Vietnamese text
        - Longer texts (20-200 words) work best
        - The model handles various Vietnamese dialects
        - Confidence scores indicate prediction certainty

        ## 🛡️ Memory Management

        - **Automatic Cleanup:** Memory is cleaned after each prediction
        - **Batch Limits:** Maximum 10 texts per batch to prevent overflow
        - **Memory Monitoring:** Real-time memory usage tracking
        - **GPU Optimization:** CUDA cache clearing when available

        ## ⚠️ Performance Notes

        - If you encounter memory errors, try reducing batch size
        - Use the Memory Cleanup button if needed
        - Monitor memory usage in the Batch Analysis tab
        - Model loaded directly from Hugging Face Hub (no local training required)
        """)

        with gr.Row():
            memory_info = gr.Textbox(
                label="Memory Usage",
                value="Memory usage: 0MB used",
                interactive=False
            )

        memory_cleanup_btn = gr.Button("🧹 Memory Cleanup", variant="secondary")

        # Connect memory cleanup event
        memory_cleanup_btn.click(
            fn=manual_memory_cleanup,
            outputs=[memory_info]
        )

    return memory_cleanup_btn, memory_info