app.py

import os
import shutil
import zipfile
from pathlib import Path
import gradio as gr
import mne

# Import your pipeline
from ica_xtra import run_preprocessing_pipeline

# Fixed montage file (must be in repo root)
BUILT_IN_GPSC = Path("ghw280_from_egig.gpsc")
if not BUILT_IN_GPSC.is_file():
    raise FileNotFoundError(f"Required montage file not found: {BUILT_IN_GPSC}")

# Output directory
OUTPUT_DIR = Path("outputs")
OUTPUT_DIR.mkdir(exist_ok=True)

# Fixed subject ID — no user input
SUBJECT_ID = "sub"

def preprocess_eeg(
    eeg_file,
    input_format: str = "fif",
    apply_highpass: bool = True,
    apply_lowpass: bool = True,
    apply_notch: bool = True,
    line_freq: float = 60.0,
    pre_ica_mad: float = 3.5,
    post_ica_mad: float = 5.0,
    interpolate_before_ica: bool = False,
    use_artifact_detection: bool = True,
):
    # Clean output dir for this fixed subject
    subject_dir = OUTPUT_DIR / SUBJECT_ID
    if subject_dir.exists():
        shutil.rmtree(subject_dir)
    subject_dir.mkdir(parents=True)
    (subject_dir / "plots").mkdir(exist_ok=True)

    uploaded_file = Path(eeg_file.name)

    if input_format == "mff":
        if not zipfile.is_zipfile(uploaded_file):
            raise ValueError("MFF input must be a ZIP file containing the .mff folder.")
        eeg_path = subject_dir / f"{SUBJECT_ID}.mff"
        with zipfile.ZipFile(uploaded_file, 'r') as zf:
            zf.extractall(eeg_path)
        if not (eeg_path / "info.xml").exists():
            raise ValueError("Invalid MFF: missing 'info.xml' in the ZIP.")
    else:
        eeg_path = subject_dir / uploaded_file.name
        shutil.copy(uploaded_file, eeg_path)

    try:
        # Run pipeline
        run_preprocessing_pipeline(
            subject=SUBJECT_ID,
            input_path=str(eeg_path),
            gpsc_file=str(BUILT_IN_GPSC),
            base_output_path=str(OUTPUT_DIR),
            input_format=input_format,
            apply_highpass=apply_highpass,
            apply_lowpass=apply_lowpass,
            apply_notch=apply_notch,
            line_freq=line_freq,
            pre_ica_mad_threshold=pre_ica_mad,
            post_ica_mad_threshold=post_ica_mad,
            interpolate_before_ica=interpolate_before_ica,
            use_artifact_detection_channels=use_artifact_detection,
            append_subject_to_output=True,
            plot=True,
            log_to_file=True,
            random_state=99,
        )

        # 🔧 FIX: Handle multiple cleaned FIF files
        cleaned_fifs = list(subject_dir.glob("*_eeg_ica_cleaned_raw.fif"))
        if not cleaned_fifs:
            raise FileNotFoundError("No cleaned FIF file found after preprocessing.")

        # Prefer the one with 'post_ica' in the name (final version)
        final_fif = None
        for f in cleaned_fifs:
            if "post_ica" in f.name:
                final_fif = f
                break
        if final_fif is None:
            # If no post_ica file, use the most recently modified (fallback)
            final_fif = max(cleaned_fifs, key=os.path.getmtime)

        # Delete all cleaned FIFs except the final one
        for f in cleaned_fifs:
            if f != final_fif:
                f.unlink()

        # Rename to standard clean name
        expected_name = subject_dir / f"{SUBJECT_ID}_eeg_ica_cleaned_raw.fif"
        if final_fif != expected_name:
            final_fif.rename(expected_name)
            cleaned_fif = expected_name
        else:
            cleaned_fif = final_fif

        # Load and save info summary
        raw = mne.io.read_raw_fif(str(cleaned_fif), preload=False)
        info_summary = subject_dir / f"{SUBJECT_ID}_info_summary.txt"
        with open(info_summary, "w") as f:
            f.write(f"=== EEG Info Summary for {SUBJECT_ID} ===\n")
            f.write(str(raw.info))

        # Create ZIP
        zip_path = OUTPUT_DIR / f"{SUBJECT_ID}_preprocessing_output.zip"
        with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_DEFLATED) as zf:
            for root, _, files in os.walk(subject_dir):
                for file in files:
                    file_path = Path(root) / file
                    arcname = file_path.relative_to(OUTPUT_DIR)
                    zf.write(file_path, arcname)

        return str(zip_path)

    except Exception as e:
        error_log = subject_dir / "error.log"
        with open(error_log, "w") as f:
            f.write(f"Preprocessing failed:\n{str(e)}")
        zip_path = OUTPUT_DIR / f"{SUBJECT_ID}_error.zip"
        with zipfile.ZipFile(zip_path, "w") as zf:
            zf.write(error_log, error_log.name)
        return str(zip_path)

# Gradio UI
with gr.Blocks(theme=gr.themes.Base(), title="EEG Preprocessing") as demo:
    gr.Markdown("# EEG Preprocessing Pipeline")
    gr.Markdown("Upload an EEG file (.fif or .mff). Uses built-in `ghw280_from_egig.gpsc` montage.")

    with gr.Row():
        with gr.Column():
            eeg_input = gr.File(label="EEG File (.fif or .mff)")
            format_dropdown = gr.Dropdown(choices=["fif", "mff"], value="fif", label="Input Format")

            with gr.Accordion("Advanced Settings", open=False):
                hp = gr.Checkbox(True, label="Apply Highpass (1.0 Hz)")
                lp = gr.Checkbox(True, label="Apply Lowpass (100.0 Hz)")
                notch = gr.Checkbox(True, label="Apply Notch Filter")
                line_freq = gr.Number(60.0, label="Line Frequency (Hz)")
                pre_mad = gr.Slider(2.0, 8.0, value=3.5, label="Pre-ICA MAD Threshold")
                post_mad = gr.Slider(2.0, 8.0, value=5.0, label="Post-ICA MAD Threshold")
                interp_before = gr.Checkbox(False, label="Interpolate Bad Channels Before ICA")
                use_artifact = gr.Checkbox(True, label="Use Artifact Detection Channels")

            run_btn = gr.Button("Run Preprocessing", variant="primary")

        with gr.Column():
            output_file = gr.File(label="Download Preprocessing Results (.zip)")

    run_btn.click(
        fn=preprocess_eeg,
        inputs=[
            eeg_input,
            format_dropdown,
            hp,
            lp,
            notch,
            line_freq,
            pre_mad,
            post_mad,
            interp_before,
            use_artifact,
        ],
        outputs=output_file,
    )

demo.launch()