app.py

import os
import re
import functools
import pandas as pd
import gradio as gr

DATA_PATH = os.environ.get("LB_DATA_PATH", "gradio (2).xlsx")
BANNER_PATH = os.environ.get("LB_BANNER_PATH", "banner.png")
pd.options.mode.copy_on_write = True

TAB_ORDER = [
    "Main Leader Board",
    "TTS Difficultly Level Per Model",
    "Performance On Codecs",
    "Best Model per Attack",
]

SHEET_DESCRIPTIONS = {
    "Main Leader Board": "<b>Overview.</b> Comparison of models across all datasets. Lower <b>EER</b> is better.",
    "TTS Difficultly Level Per Model": "<b>TTS stress-test.</b> Lower TNR means harder; higher TNR means easier.",
    "Performance On Codecs": "<b>Codec robustness.</b> Compare models under compression/bitrates.",
    "Best Model per Attack": "<b>Per-attack winners.</b> Shows top-performing model per attack.",
}

try:
    import openpyxl  # better engine for xlsx on Windows
    READ_KW = {"engine": "openpyxl"}
except Exception:
    READ_KW = {}


# ─────────────────────────────────────────────────────────────────────────────
# Excel helpers
# ─────────────────────────────────────────────────────────────────────────────
def _ensure_file():
    if not os.path.exists(DATA_PATH):
        raise FileNotFoundError(f"Excel file not found at '{DATA_PATH}'")

@functools.lru_cache(maxsize=1)
def load_sheet(name: str):
    _ensure_file()
    return pd.read_excel(DATA_PATH, sheet_name=name, **READ_KW)

def _find_model_col(headers: list[str]) -> str:
    for cand in headers:
        if str(cand).lower().strip() in ("ssl model", "ssl_model", "model"):
            return cand
    return headers[0]

def _base_dataset_name(colname: str) -> str:
    return re.sub(r"\.\d+$", "", str(colname)).strip()

@functools.lru_cache(maxsize=1)
def read_tts_header():
    """
    From 'TTS all' read:
      - column headers (Excel header row)
      - first data row (contains labels per column: attacks/TTS names/means)
      - the model column name
    """
    df_cols = pd.read_excel(DATA_PATH, sheet_name="TTS all", nrows=0, **READ_KW)
    columns = df_cols.columns.tolist()
    df_head = pd.read_excel(DATA_PATH, sheet_name="TTS all", nrows=1, header=0, **READ_KW)
    first_row = df_head.iloc[0] if len(df_head) else pd.Series(index=columns, dtype=object)
    model_col = _find_model_col(columns)
    return model_col, columns, first_row

@functools.lru_cache(maxsize=1)
def build_dataset_to_labels():
    """
    Build mapping WITHOUT filtering to Axx only:
      dataset -> { label -> wide_column_name }
    Includes any non-empty string labels (e.g., 'A07', 'mean TNR', 'MeloTTS', etc.).
    If duplicate labels occur within the same dataset, make them unique (label, label#2, ...).
    """
    model_col, columns, first_row = read_tts_header()
    mapping: dict[str, dict[str, str]] = {}
    per_dataset_seen: dict[str, set[str]] = {}

    for col in columns:
        if col == model_col:
            continue
        dataset = _base_dataset_name(col)
        raw_label = first_row.get(col, "")
        label = str(raw_label).strip() if pd.notna(raw_label) else ""
        if not label:  # skip empty header labels
            continue

        # Ensure uniqueness within a dataset
        if dataset not in per_dataset_seen:
            per_dataset_seen[dataset] = set()
        unique_label = label
        idx = 2
        while unique_label in per_dataset_seen[dataset]:
            unique_label = f"{label}#{idx}"
            idx += 1
        per_dataset_seen[dataset].add(unique_label)

        mapping.setdefault(dataset, {})[unique_label] = col

    return model_col, columns, mapping

def _indices_for_columns(columns: list[str], wanted: list[str]) -> list[int]:
    name_to_pos = {name: i for i, name in enumerate(columns)}
    positions = []
    for w in wanted:
        if w not in name_to_pos:
            raise KeyError(f"Requested column '{w}' not found in header.")
        positions.append(name_to_pos[w])
    return positions

def read_tts_subset_for_dataset(dataset: str, label: str | None) -> pd.DataFrame:
    """
    Efficiently read only the model column + chosen dataset labels (or a single label).
    Steps:
      - Map dataset -> labels (wide column names)
      - Read via positional indices (stable even with dup headers)
      - Drop the first data row (label row)
      - Rename columns to ['Model', <labels...>]
      - Convert numeric columns
    """
    model_col, columns, mapping = build_dataset_to_labels()
    if dataset not in mapping:
        return pd.DataFrame({"Info": [f"No columns found for dataset '{dataset}'."]})

    if label and label != "(All)":
        if label not in mapping[dataset]:
            return pd.DataFrame({"Info": [f"Label '{label}' not found in dataset '{dataset}'."]})
        selected_cols = [mapping[dataset][label]]
        labels = [label]
    else:
        # include ALL labels for this dataset (attacks, means, TTS names…)
        labels = sorted(mapping[dataset].keys(), key=lambda s: s.lower())
        selected_cols = [mapping[dataset][lab] for lab in labels]

    try:
        use_positions = _indices_for_columns(columns, [model_col] + selected_cols)
    except KeyError as e:
        return pd.DataFrame({"Info": [f"{e}"]})

    df = pd.read_excel(
        DATA_PATH,
        sheet_name="TTS all",
        usecols=use_positions,
        header=0,
        **READ_KW
    )

    # First data row holds the labels; drop it.
    if len(df) > 0:
        df = df.iloc[1:].reset_index(drop=True)

    # Rename columns
    df.columns = ["Model"] + labels
    df["Model"] = df["Model"].astype(str)

    # Coerce numeric where possible
    for lab in labels:
        df[lab] = pd.to_numeric(df[lab], errors="coerce")

    return df


# ─────────────────────────────────────────────────────────────────────────────
# Build Gradio app
# ─────────────────────────────────────────────────────────────────────────────
def build_app():
    with gr.Blocks(title="🎙️ Benchmarking Spoof-SUPERB Classifiers") as demo:
        if os.path.exists(BANNER_PATH):
            gr.Image(value=BANNER_PATH, show_label=False)
        gr.Markdown("<h1>🎙️ Benchmarking Spoof-SUPERB Classifiers Built on S3PRL Embeddings</h1>")

        with gr.Tabs():
            # 1–4: Auto-load the four main tabs
            for sheet in TAB_ORDER:
                try:
                    df = load_sheet(sheet)
                except Exception as e:
                    df = pd.DataFrame({"Error": [str(e)]})
                with gr.TabItem(sheet):
                    gr.Markdown(f"<div class='sheet-card'>{SHEET_DESCRIPTIONS.get(sheet, '')}</div>")
                    gr.Dataframe(value=df, interactive=False)

            # Explorer (all labels, not just Axx)
            with gr.TabItem("Explorer for Attacks"):
                gr.Markdown(
                    "<div class='sheet-card'>"
                    "<b>Pick a Dataset</b> then (optionally) pick a specific label.<br>"
                    "Rows = Models, Columns = ALL labels for that dataset (attacks, means, TTS names, etc.)."
                    "</div>"
                )
                try:
                    _, _, mapping = build_dataset_to_labels()
                    datasets = sorted(mapping.keys())
                except Exception:
                    mapping, datasets = {}, []

                if not datasets:
                    gr.Markdown("❗ <i>No valid 'TTS all' sheet detected (first row must contain labels; first column models).</i>")
                else:
                    default_ds = datasets[0]
                    default_labels = ["(All)"] + sorted(mapping[default_ds].keys(), key=lambda s: s.lower())

                    with gr.Row():
                        ds_dd = gr.Dropdown(datasets, value=default_ds, label="Dataset")
                        lab_dd = gr.Dropdown(default_labels, value="(All)", label="Label (optional)")

                    out = gr.Dataframe(interactive=False)

                    def on_ds_change(ds):
                        new_labels = ["(All)"] + sorted(mapping.get(ds, {}).keys(), key=lambda s: s.lower())
                        table = read_tts_subset_for_dataset(ds, None)
                        return gr.update(choices=new_labels, value="(All)"), table

                    ds_dd.change(on_ds_change, ds_dd, [lab_dd, out])

                    def on_label_change(ds, lab):
                        return read_tts_subset_for_dataset(ds, lab if lab != "(All)" else None)

                    lab_dd.change(on_label_change, [ds_dd, lab_dd], out)

                    # initial render
                    out.value = read_tts_subset_for_dataset(default_ds, None)

    return demo


demo = build_app()

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860, share=True)