import json
import os
from typing import Optional

import gradio as gr
import numpy as np
import pandas as pd

from data_loader import (
    ARTIFACT_LABEL_MAP,
    get_annotations,
    get_annotations_in_window,
    get_channel_names,
    get_recording_display_list,
    get_recording_info,
    get_store_metadata,
    get_tuar_recordings,
    preload_all_annotations,
    read_signal_window,
    reset_s3fs,
)
from visualizer import (
    build_annotation_summary,
    build_artifact_legend,
    build_eeg_figure,
    build_metadata_html,
)

WINDOW_PADDING_S = 2.0
MAX_WINDOW_S = 15.0
MIN_WINDOW_S = 2.0

ALL_ARTIFACT_TYPES = [
    "Eye Movement", "Muscle", "Electrode Pop",
    "Chewing", "Shiver", "Artifact (Generic)", "Background",
    "Eye Movement + Muscle", "Muscle + Electrode Pop",
    "Eye Movement + Electrode Pop", "Eye Movement + Chewing",
    "Chewing + Electrode Pop", "Chewing + Muscle",
    "Eye Movement + Shiver", "Shiver + Electrode Pop",
]

recordings_df: pd.DataFrame = pd.DataFrame()
# Maps recording_label -> list of annotations
annotations_index: dict[str, list[dict]] = {}
# Maps recording_label -> info dict
info_index: dict[str, dict] = {}
# Maps recording_label -> store metadata
meta_index: dict[str, dict] = {}


def check_aws_credentials() -> bool:
    return bool(os.environ.get("AWS_ACCESS_KEY_ID") and os.environ.get("AWS_SECRET_ACCESS_KEY"))


def save_credentials(access_key: str, secret_key: str, region: str) -> str:
    if not access_key.strip() or not secret_key.strip():
        return '<div style="color:#f88;">Both Access Key and Secret Key are required.</div>'
    os.environ["AWS_ACCESS_KEY_ID"] = access_key.strip()
    os.environ["AWS_SECRET_ACCESS_KEY"] = secret_key.strip()
    os.environ["AWS_DEFAULT_REGION"] = region.strip() or "us-east-1"
    reset_s3fs()
    env_path = os.path.join(os.path.dirname(__file__), ".env")
    with open(env_path, "w") as f:
        f.write(f"AWS_ACCESS_KEY_ID={access_key.strip()}\n")
        f.write(f"AWS_SECRET_ACCESS_KEY={secret_key.strip()}\n")
        f.write(f"AWS_DEFAULT_REGION={region.strip() or 'us-east-1'}\n")
    return '<div style="color:#8f8;">Credentials saved.</div>'


_annotations_loaded = False


def init_recordings():
    global recordings_df
    try:
        recordings_df = get_tuar_recordings()
        if len(recordings_df) == 0:
            return '<div style="color:#f88;">No TUAR recordings found.</div>'
        return (
            f'<div style="color:#8f8;">Loaded <b>{len(recordings_df)}</b> TUAR recordings. '
            f'Select an artifact type to begin.</div>'
        )
    except Exception as e:
        return f'<div style="color:#f88;">Error loading manifest: {e}</div>'


def _ensure_annotations_loaded(progress=None):
    global _annotations_loaded
    if _annotations_loaded:
        return

    if progress:
        progress(0.1, desc="Fetching artifact annotations from S3...")
    preload_all_annotations(recordings_df)

    if progress:
        progress(0.7, desc="Building index...")
    for _, row in recordings_df.iterrows():
        info = get_recording_info(row)
        canonical_uri = info.get("canonical_uri", "")
        if not canonical_uri:
            continue
        anns = get_annotations(canonical_uri, source_uri=info.get("archival_uri", ""))
        if not anns:
            continue
        rec_key = info["recording_id"]
        annotations_index[rec_key] = anns
        info_index[rec_key] = info

    _annotations_loaded = True
    if progress:
        progress(1.0, desc="Done!")


def on_artifact_type_selected(artifact_type: str, progress=gr.Progress()):
    """Fetch annotations on first use, then filter by type."""
    if not artifact_type or recordings_df.empty:
        return (
            gr.Dropdown(choices=[], value=None),
            '<div style="color:#888;">No recordings loaded.</div>',
        )

    _ensure_annotations_loaded(progress)

    if not annotations_index:
        return (
            gr.Dropdown(choices=[], value=None),
            '<div style="color:#f88;">No annotations found. Check AWS credentials.</div>',
        )

    matching = []
    for rec_key, anns in annotations_index.items():
        type_anns = [a for a in anns if a["label"] == artifact_type]
        if type_anns:
            info = info_index[rec_key]
            label = (
                f"{rec_key[:8]}… | "
                f"subj={info.get('subject','?')} | "
                f"ses={info.get('session','?')} | "
                f"{len(type_anns)} instance(s) | "
                f"dur={info.get('duration_s',0):.0f}s"
            )
            matching.append((label, rec_key))

    if not matching:
        return (
            gr.Dropdown(choices=[], value=None),
            f'<div style="color:#f88;">No recordings contain <b>{artifact_type}</b>.</div>',
        )

    choices = [m[0] for m in matching]
    return (
        gr.Dropdown(
            choices=choices, value=choices[0],
            label=f"Recordings with {artifact_type} ({len(choices)} found)",
        ),
        f'<div style="color:#8f8;"><b>{len(choices)}</b> recordings with <b>{artifact_type}</b>.</div>',
    )


def _find_rec_key(recording_label: str) -> Optional[str]:
    prefix = recording_label.split("…")[0] if "…" in recording_label else recording_label[:8]
    for key in annotations_index:
        if key.startswith(prefix):
            return key
    return None


def on_recording_selected(artifact_type: str, recording_label: str):
    """Show artifact instances for the selected recording + type."""
    if not recording_label or not artifact_type:
        return (
            gr.Dropdown(choices=[], value=None),
            '<div style="color:#888;"></div>',
            None,
            '<div></div>',
            gr.CheckboxGroup(choices=[], value=[]),
        )

    rec_key = _find_rec_key(recording_label)
    if not rec_key:
        return (
            gr.Dropdown(choices=[], value=None),
            '<div style="color:#f88;">Recording not found in index.</div>',
            None,
            '<div></div>',
            gr.CheckboxGroup(choices=[], value=[]),
        )

    anns = annotations_index.get(rec_key, [])
    info = info_index.get(rec_key, {})
    type_anns = [a for a in anns if a["label"] == artifact_type]

    if not type_anns:
        return (
            gr.Dropdown(choices=[], value=None),
            build_metadata_html(info),
            None,
            '<div style="color:#888;">No instances found.</div>',
            gr.CheckboxGroup(choices=[], value=[]),
        )

    canonical_uri = info.get("canonical_uri", "")
    channel_names = info.get("channel_names", [])
    if isinstance(channel_names, str):
        import json as _json
        try:
            channel_names = _json.loads(channel_names)
        except Exception:
            channel_names = []
    if not isinstance(channel_names, list):
        channel_names = list(channel_names)
    store_meta = {
        "channel_names": channel_names,
        "sampling_rate_hz": info.get("sampling_rate_hz", 250),
        "duration_s": info.get("duration_s", 0),
    }
    meta_index[rec_key] = store_meta

    choices = []
    for i, inst in enumerate(type_anns):
        ch = inst.get("channel", "all")
        choices.append(
            f"#{i+1} | {inst['onset_s']:.1f}s – {inst['end_s']:.1f}s | "
            f"dur={inst['duration_s']:.1f}s | ch={ch}"
        )

    all_channels = store_meta.get("channel_names", [])

    return (
        gr.Dropdown(choices=choices, value=choices[0],
                    label=f"{artifact_type} instances ({len(choices)})"),
        build_metadata_html(info),
        None,
        build_annotation_summary(type_anns),
        gr.CheckboxGroup(choices=all_channels, value=[], label=f"Channels ({len(all_channels)})"),
    )


def on_instance_selected(artifact_type: str, recording_label: str, instance_label: str, selected_channels: list[str]):
    """Render the EEG plot for the selected artifact instance."""
    if not instance_label or not recording_label:
        return (
            build_eeg_figure(np.zeros((1, 100)), ["Pick an instance"], 256.0, title="Select an artifact instance"),
            '<div></div>',
            gr.CheckboxGroup(),
        )

    try:
        idx = int(instance_label.split("|")[0].strip().replace("#", "")) - 1
    except (ValueError, IndexError):
        return (
            build_eeg_figure(np.zeros((1, 100)), ["Error"], 256.0, title="Parse error"),
            '<div></div>',
            gr.CheckboxGroup(),
        )

    rec_key = _find_rec_key(recording_label) if recording_label else None
    anns = annotations_index.get(rec_key, []) if rec_key else []
    info = info_index.get(rec_key, {}) if rec_key else {}
    store_meta = meta_index.get(rec_key, {}) if rec_key else {}
    type_anns = [a for a in anns if a["label"] == artifact_type]

    if idx < 0 or idx >= len(type_anns):
        return (
            build_eeg_figure(np.zeros((1, 100)), ["Error"], 256.0, title="Instance not found"),
            '<div></div>',
            gr.CheckboxGroup(),
        )

    artifact = type_anns[idx]
    canonical_uri = info.get("canonical_uri", "")
    all_channels = store_meta.get("channel_names", [])
    sfreq = store_meta.get("sampling_rate_hz", 250.0)
    duration = store_meta.get("duration_s", 0)

    art_duration = artifact["end_s"] - artifact["onset_s"]
    padding = max(WINDOW_PADDING_S, art_duration * 0.3)
    win_start = max(0, artifact["onset_s"] - padding)
    win_end = min(duration, artifact["end_s"] + padding)
    win_end = min(win_end, win_start + MAX_WINDOW_S)
    if win_end - win_start < MIN_WINDOW_S:
        win_end = min(win_start + MIN_WINDOW_S, duration)

    if not selected_channels:
        selected_channels = _get_relevant_channels(artifact_type, artifact.get("channel", ""), all_channels, anns)

    channel_indices = [i for i, name in enumerate(all_channels) if name in selected_channels]
    if not channel_indices:
        channel_indices = list(range(min(8, len(all_channels))))
        selected_channels = [all_channels[i] for i in channel_indices]

    start_sample = int(win_start * sfreq)
    end_sample = int(win_end * sfreq)

    try:
        signal = read_signal_window(canonical_uri, start_sample, end_sample, channel_indices)
        ch_names = [all_channels[i] for i in channel_indices]
    except Exception as e:
        return (
            build_eeg_figure(np.zeros((1, 100)), ["S3 Error"], 256.0, title=str(e)[:80]),
            f'<div style="color:#f88;">{e}</div>',
            gr.CheckboxGroup(choices=all_channels, value=selected_channels),
        )

    source_uri = info.get("archival_uri", "")
    window_anns = get_annotations_in_window(canonical_uri, win_start, win_end, source_uri=source_uri)

    fig = build_eeg_figure(
        signal, ch_names, sfreq,
        start_time_s=win_start,
        annotations=window_anns,
        title=f"{artifact_type} | {info.get('subject', '?')} | {win_start:.1f}–{win_end:.1f}s",
    )

    return (
        fig,
        build_annotation_summary(window_anns),
        gr.CheckboxGroup(choices=all_channels, value=selected_channels),
    )


def _get_relevant_channels(artifact_type: str, art_channel: str, all_channels: list[str], anns: list[dict]) -> list[str]:
    relevant = [a for a in anns if a["label"] == artifact_type]
    ann_channels = set(a.get("channel", "") for a in relevant if a.get("channel"))

    matched = []
    for name in all_channels:
        name_clean = name.upper().replace("EEG ", "").replace("-REF", "").replace("-", "").replace(" ", "")
        for ann_ch in ann_channels:
            parts = ann_ch.upper().replace("-", "")
            if parts in name_clean or name_clean in parts:
                matched.append(name)
                break

    if matched:
        return list(dict.fromkeys(matched))[:12]

    channel_map = {
        "eye": ["FP1", "FP2", "F7", "F8", "F3", "F4"],
        "muscle": ["T3", "T4", "T5", "T6", "F7", "F8"],
        "chew": ["T3", "T4", "T5", "T6", "F7", "F8"],
    }
    target = next((v for k, v in channel_map.items() if k in artifact_type.lower()),
                  ["FP1", "FP2", "F3", "F4", "C3", "C4", "P3", "P4", "O1", "O2"])

    result = [name for name in all_channels if any(t in name.upper() for t in target)]
    return result[:12] if result else all_channels[:8]


CSS = """
.gradio-container {max-width: 1600px !important;}
footer {display: none !important;}
"""

with gr.Blocks(title="TUAR EEG Artifact Explorer") as app:

    gr.Markdown(
        "# TUAR EEG Artifact Explorer\n"
        "Browse EEG artifacts by type. Select artifact > recording > instance. "
        "Everything streams from S3."
    )

    with gr.Accordion(
        "AWS Credentials" + (" (configured)" if check_aws_credentials() else " (required)"),
        open=not check_aws_credentials(),
    ):
        with gr.Row():
            aws_key = gr.Textbox(label="Access Key ID", type="password", placeholder="AKIA...", scale=2)
            aws_secret = gr.Textbox(label="Secret Access Key", type="password", scale=2)
            aws_region = gr.Textbox(label="Region", value="us-east-1", scale=1)
        save_btn = gr.Button("Save Credentials", variant="secondary", size="sm")
        creds_status = gr.HTML("")
        save_btn.click(fn=save_credentials, inputs=[aws_key, aws_secret, aws_region], outputs=[creds_status])

    gr.Markdown("---")

    status_html = gr.HTML('<div style="color:#888;">Loading TUAR recordings…</div>')

    gr.Markdown("### Step 1: Select artifact type")
    artifact_type_dropdown = gr.Dropdown(
        choices=ALL_ARTIFACT_TYPES, value=None,
        label="What artifact are you looking for?", interactive=True,
    )
    scan_status = gr.HTML("")

    gr.Markdown("### Step 2: Select recording")
    recording_dropdown = gr.Dropdown(
        choices=[], label="Recordings containing this artifact", interactive=True,
    )

    gr.Markdown("### Step 3: Select specific artifact instance")
    instance_dropdown = gr.Dropdown(
        choices=[], label="Artifact instances in this recording", interactive=True,
    )

    gr.Markdown("---")

    with gr.Row():
        with gr.Column(scale=4):
            eeg_plot = gr.Plot(label="EEG Signal")
        with gr.Column(scale=1):
            gr.Markdown("### Recording Info")
            metadata_html = gr.HTML('<div style="color:#888;">No recording loaded.</div>')
            gr.Markdown("### Artifacts in View")
            annotation_html = gr.HTML('<div style="color:#888;"></div>')
            gr.Markdown("### Legend")
            gr.HTML(build_artifact_legend())

    with gr.Accordion("Channel Selection (auto-selected, or pick manually)", open=False):
        channel_selector = gr.CheckboxGroup(choices=[], value=[], label="Channels")

    # --- Events ---

    app.load(fn=init_recordings, inputs=[], outputs=[status_html])

    artifact_type_dropdown.change(
        fn=on_artifact_type_selected,
        inputs=[artifact_type_dropdown],
        outputs=[recording_dropdown, scan_status],
    )

    recording_dropdown.change(
        fn=on_recording_selected,
        inputs=[artifact_type_dropdown, recording_dropdown],
        outputs=[instance_dropdown, metadata_html, eeg_plot, annotation_html, channel_selector],
    )

    instance_dropdown.change(
        fn=on_instance_selected,
        inputs=[artifact_type_dropdown, recording_dropdown, instance_dropdown, channel_selector],
        outputs=[eeg_plot, annotation_html, channel_selector],
    )

    channel_selector.change(
        fn=on_instance_selected,
        inputs=[artifact_type_dropdown, recording_dropdown, instance_dropdown, channel_selector],
        outputs=[eeg_plot, annotation_html, channel_selector],
    )


if __name__ == "__main__":
    port = int(os.environ.get("PORT", 7860))
    app.launch(
        server_name="0.0.0.0",
        server_port=port,
        share=False,
        theme=gr.themes.Base(primary_hue="blue", secondary_hue="slate", neutral_hue="slate"),
        css=CSS,
    )