from pathlib import Path
from utils import load_json_results
from leaderboard_tab import search_leaderboard, update_columns_to_show, create_leaderboard_tab

# Constants
RERANKER_ABOUT_SECTION = """
## About Reranking Evaluation

The reranking evaluation assesses a model's ability to improve search quality by reordering initially retrieved results. Models are evaluated across multiple unseen Arabic datasets to ensure robust performance.

### Evaluation Metrics
- **MRR@10 (Mean Reciprocal Rank at 10)**: Measures the ranking quality focusing on the first relevant result in top-10
- **NDCG@10 (Normalized DCG at 10)**: Evaluates the ranking quality of all relevant results in top-10
- **MAP (Mean Average Precision)**: Measures the overall precision across all relevant documents

All metrics are averaged across multiple evaluation datasets to provide a comprehensive assessment of model performance.

### Model Requirements
- Must accept query-document pairs as input
- Should output relevance scores for reranking (has cross-attention or similar mechanism for query-document matching)
- Support for Arabic text processing

### Evaluation Process
1. Models are tested on multiple unseen Arabic datasets
2. For each dataset:
   - Initial candidate documents are provided
   - Model reranks the candidates
   - MRR@10, NDCG@10, and MAP are calculated
3. Final scores are averaged across all datasets
4. Models are ranked based on overall performance

### How to Prepare Your Model
- Model should be public on HuggingFace Hub (private models are not supported yet)
- Make sure it works coherently with `sentence-transformers` library
"""

# Global variables
reranking_df = None

def load_reranking_results(prepare_for_display=False, sort_col=None, drop_cols=None):
    dataframe_path = Path(__file__).parent / "results" / "reranking_results.json"
    return load_json_results(
        dataframe_path,
        prepare_for_display=prepare_for_display,
        sort_col=sort_col,
        drop_cols=drop_cols
    )

def load_reranking_leaderboard():
    """Load and prepare the reranking leaderboard data"""
    global reranking_df
    
    # Prepare reranking dataframe
    reranking_df = load_reranking_results(True, sort_col="Average Score", drop_cols=["Revision", "Precision", "Task"])
    reranking_df.insert(0, "Rank", range(1, 1 + len(reranking_df)))
    
    return reranking_df

def reranking_search_leaderboard(model_name, columns_to_show):
    """Search function for reranking leaderboard"""
    return search_leaderboard(reranking_df, model_name, columns_to_show)

def update_reranker_columns_to_show(columns_to_show):
    """Update displayed columns for reranking leaderboard"""
    return update_columns_to_show(reranking_df, columns_to_show)

def create_reranking_tab():
    """Create the complete reranking leaderboard tab"""
    global reranking_df
    
    # Load data if not already loaded
    if (reranking_df is None):
        reranking_df = load_reranking_leaderboard()
    
    # Define default columns to show
    default_columns = ["Rank", "Model", "Average Score", "Model Size (MB)", "Context Length",
                      "Embedding Dimension", "Namaa Global Knowledge", "Navid General Knowledge"]
    
    # Create and return the tab
    return create_leaderboard_tab(
        df=reranking_df,
        initial_columns_to_show=default_columns,
        search_function=reranking_search_leaderboard,
        update_function=update_reranker_columns_to_show,
        about_section=RERANKER_ABOUT_SECTION,
        task_type="Reranker"
    )