#!/usr/bin/env python3
import pandas as pd
import numpy as np
from pathlib import Path


def aggregate_llm_share():
    base_dir = Path(__file__).parent

    series_order = [
        (
            "Email Responder",
            ["EmailResponder", "EmailResponder-MCP"],
        ),
        (
            "Recruitment",
            [
                "RecruitmentAssistant-A2A",
                "RecruitmentAssistant-H_A2A",
                "RecruitmentAssistant-MCP",
            ],
        ),
        ("Markdown Val.", ["MarkdownValidator", "MarkdownValidator-MCP"]),
        ("Game Builder", ["GameBuilder", "GameBuilder-MCP"]),
        (
            "SQL Asst.",
            ["SQLAssistant-A2A", "SQLAssistant-H_A2A", "SQLAssistant-MCP"],
        ),
        (
            "Landing Pg.",
            [
                "LandingPageGenerator-A2A",
                "LandingPageGenerator-H_A2A",
                "LandingPageGenerator-MCP",
            ],
        ),
        (
            "Book Writer",
            [
                "BookWriter-A2A",
                "BookWriter-H_A2A",
                "BookWriter-MCP",
            ],
        ),
        (
            "Social M. M.",
            [
                "SocialMediaManager-A2A",
                "SocialMediaManager-H_A2A",
                "SocialMediaManager-MCP",
            ],
        ),
    ]

    projects = []
    csv_files = []

    project_to_display_name = {}

    for series_name, project_list in series_order:
        for project_name in project_list:
            project_dir = base_dir / project_name
            if project_dir.is_dir():
                csv_file = project_dir / "performance_breakdown_summary_by_model.csv"
                if csv_file.exists():
                    if "-A2A_mix" in project_name:
                        display_name = f"{series_name} (A2A_mix)"
                    elif "-A2A" in project_name:
                        display_name = f"{series_name} (A2A)"
                    elif "-MCP" in project_name:
                        display_name = f"{series_name} (MCP)"
                    else:
                        # Projects without a suffix are Pure CrewAI baselines
                        display_name = f"{series_name} (CrewAI)"

                    projects.append(project_name)
                    project_to_display_name[project_name] = display_name
                    csv_files.append(csv_file)
                else:
                    print(f"Warning: {project_name} missing CSV file")

    print(f"Found {len(projects)} projects with CSV files")

    models = [
        "GPT-5",
        "GPT-4o-mini",
        "DeepSeek-V3-1",
        "DeepSeek-R1",
        "Gemini-2.5-flash",
        "Gemini-2.5-flash-nothinking",
        "Qwen3-235b",
    ]

    data_dict = {model: [] for model in models}
    weight_dict = {model: [] for model in models}
    total_weighted_sum = 0.0
    total_weight = 0.0

    for project, csv_file in zip(projects, csv_files):
        try:
            df = pd.read_csv(csv_file)

            for model in models:
                model_data = df[df["model"] == model]
                if not model_data.empty:
                    llm_share = model_data["LLM_share"].values[0]
                    llm_share = min(llm_share, 1.0)
                    llm_share = round(llm_share, 4)
                    data_dict[model].append(llm_share)

                    comp_time = model_data["total_components_time"].values[0]
                    weight = float(comp_time) if pd.notna(comp_time) else 0.0
                    weight_dict[model].append(weight)

                    if pd.notna(llm_share) and weight > 0:
                        total_weighted_sum += llm_share * weight
                        total_weight += weight
                else:
                    data_dict[model].append(None)
                    weight_dict[model].append(0.0)
        except Exception as e:
            print(f"Error reading {csv_file}: {e}")
            for model in models:
                data_dict[model].append(None)
                weight_dict[model].append(0.0)

    result_df = pd.DataFrame(data_dict, index=projects)
    result_df.index = result_df.index.map(project_to_display_name)
    result_df.index.name = "Model"
    result_df = result_df.T

    # Time-weighted overall average across all models and projects
    overall_avg = (
        round(total_weighted_sum / total_weight, 4) if total_weight > 0 else np.nan
    )
    overall_row_name = "Overall Average (time-weighted, all models & projects)"
    overall_row = pd.Series(
        {col: overall_avg for col in result_df.columns}, name=overall_row_name
    )
    result_df = pd.concat([result_df, overall_row.to_frame().T])

    output_file = base_dir / "llm_share_summary.csv"
    result_df.to_csv(output_file, float_format="%.4f")
    print(f"\nCSV saved to: {output_file}")
    print(f"\nShape: {result_df.shape[0]} models × {result_df.shape[1]} projects")

    return result_df


def generate_latex_table(df):
    num_cols = len(df.columns)

    latex = []
    latex.append("\\begin{table*}[htbp]")
    latex.append("\\centering")
    latex.append("\\small")
    latex.append(f"\\begin{{tabular}}{{l{'c' * num_cols}}}")
    latex.append("\\toprule")

    header = "Model & " + " & ".join(df.columns) + " \\\\"
    latex.append(header)
    latex.append("\\midrule")

    for idx, row in df.iterrows():
        row_str = (
            str(idx)
            + " & "
            + " & ".join([f"{v:.4f}" if pd.notna(v) else "-" for v in row])
            + " \\\\"
        )
        latex.append(row_str)

    latex.append("\\bottomrule")
    latex.append("\\end{tabular}")
    latex.append("\\caption{LLM Share by Model and Project}")
    latex.append("\\label{tab:llm_share}")
    latex.append("\\end{table*}")

    return "\n".join(latex)


if __name__ == "__main__":
    aggregate_llm_share()