import os
import sys

# Completely disable experimental Gradio 5 SSR sidecar
os.environ["GRADIO_SSR"] = "0"
os.environ["GRADIO_SSR_MODE"] = "0"
os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"

import json
import gradio as gr
import pandas as pd
from gradio_leaderboard import Leaderboard, SelectColumns

# Add current directory and src subdirectories to Python path
CURRENT_DIR = os.path.abspath(os.path.dirname(__file__))
sys.path.insert(0, CURRENT_DIR)
for sub in ["src", "src/display", "src/submission", "src/leaderboard", "src/audit"]:
    p = os.path.join(CURRENT_DIR, sub)
    if os.path.exists(p):
        sys.path.insert(0, p)

# DB Integration
try:
    from src.db import mongo_get_all_certificates, mongo_save_certificate
except ImportError:
    from db import mongo_get_all_certificates, mongo_save_certificate

# About
try:
    from src.about import CITATION_BUTTON_LABEL, CITATION_BUTTON_TEXT, INTRODUCTION_TEXT, TITLE
except ImportError:
    from about import CITATION_BUTTON_LABEL, CITATION_BUTTON_TEXT, INTRODUCTION_TEXT, TITLE

# CSS
try:
    from src.display.css_html_js import custom_css
except ImportError:
    try:
        from src.css_html_js import custom_css
    except ImportError:
        try:
            from display.css_html_js import custom_css
        except ImportError:
            from css_html_js import custom_css

# Formatting
try:
    from src.display.formatting import build_top_3_cards_html, render_audit_details_panel
except ImportError:
    try:
        from src.formatting import build_top_3_cards_html, render_audit_details_panel
    except ImportError:
        try:
            from display.formatting import build_top_3_cards_html, render_audit_details_panel
        except ImportError:
            from formatting import build_top_3_cards_html, render_audit_details_panel

# Utils
try:
    from src.display.utils import BENCHMARK_COLS, COLS, AutoEvalColumn, fields
except ImportError:
    try:
        from src.utils import BENCHMARK_COLS, COLS, AutoEvalColumn, fields
    except ImportError:
        try:
            from display.utils import BENCHMARK_COLS, COLS, AutoEvalColumn, fields
        except ImportError:
            from utils import BENCHMARK_COLS, COLS, AutoEvalColumn, fields

# Envs
try:
    from src.envs import EVAL_RESULTS_PATH
except ImportError:
    from envs import EVAL_RESULTS_PATH

# Populate
try:
    from src.populate import get_leaderboard_df, get_top_3_eval_cards
except ImportError:
    from populate import get_leaderboard_df, get_top_3_eval_cards

# Submit
try:
    from src.submission.submit import audit_or_search_model, clean_model_name, get_certificate_by_model_name
except ImportError:
    try:
        from src.submit import audit_or_search_model, clean_model_name, get_certificate_by_model_name
    except ImportError:
        try:
            from submission.submit import audit_or_search_model, clean_model_name, get_certificate_by_model_name
        except ImportError:
            from submit import audit_or_search_model, clean_model_name, get_certificate_by_model_name


def seed_demo_results():
    if not os.path.exists(EVAL_RESULTS_PATH):
        os.makedirs(EVAL_RESULTS_PATH, exist_ok=True)

    existing_docs = mongo_get_all_certificates()
    if not existing_docs and not os.listdir(EVAL_RESULTS_PATH):
        mock_cert = {
            "status": "ok",
            "config": {
                "model_name": "Qwen/Qwen2.5-0.5B-Instruct",
                "model_sha": "main",
                "architecture": "Qwen2ForCausalLM",
                "params": 0.5,
                "precision": "bfloat16",
                "license": "apache-2.0",
            },
            "audited_at": "2026-08-16 12:00:00",
            "spectral": {
                "stable_rank_mean": 86.74,
                "effective_rank_mean": 430.55,
                "condition_number_mean": 41650.41,
                "matrices_sampled": 48,
            },
            "observer": {
                "tau": 0.95,
                "D": 896,
                "d": 428,
                "blind_fraction": 0.7718,
                "calibration_tokens": 10500,
                "calibration_texts": 200,
                "token_to_dim_ratio": 11.7,
                "sample_adequate": True,
                "min_ratio_floor": 5.0,
            },
            "behavioral": {
                "n_probes": 18,
                "correct_count": 11,
                "factual_accuracy": 0.6111,
                "paraphrase_fidelity": 0.9058,
                "per_item": [],
            },
            "composite": {
                "structural_risk": 43.3,
                "behavioral_risk": 38.9,
                "unvalidated_composite_score": 40.7,
                "verdict": "MODERATE RISK",
                "disclaimer": "Diagnostic signal only.",
            },
        }
        with open(os.path.join(EVAL_RESULTS_PATH, "Qwen__Qwen2.5-0.5B-Instruct_main.json"), "w", encoding="utf-8") as f:
            json.dump(mock_cert, f, indent=2)
        mongo_save_certificate(mock_cert)


seed_demo_results()

LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, "", COLS, BENCHMARK_COLS)
top_3_records = get_top_3_eval_cards(EVAL_RESULTS_PATH)
initial_cert = get_certificate_by_model_name("Qwen/Qwen2.5-0.5B-Instruct") if top_3_records else {}


def on_select_model(evt: gr.SelectData, df: pd.DataFrame):
    """Triggered whenever any cell or row is clicked in the Leaderboard."""
    try:
        if evt.value:
            cleaned = clean_model_name(str(evt.value))
            if cleaned:
                cert = get_certificate_by_model_name(cleaned)
                if cert and cert.get("status") == "ok":
                    return render_audit_details_panel(cert)

        if isinstance(df, pd.DataFrame) and evt.index is not None:
            row_idx = evt.index[0] if isinstance(evt.index, (list, tuple)) else evt.index
            if row_idx < len(df):
                model_col = AutoEvalColumn.model.name
                if model_col in df.columns:
                    raw_model = str(df.iloc[row_idx][model_col])
                    cleaned = clean_model_name(raw_model)
                    cert = get_certificate_by_model_name(cleaned)
                    if cert:
                        return render_audit_details_panel(cert)

        return render_audit_details_panel({})
    except Exception as e:
        print(f"Selection error: {e}")
        return render_audit_details_panel({})


def on_top_card_click(model_name: str):
    """Triggered whenever one of the Top 3 Cards is clicked."""
    cleaned = clean_model_name(model_name)
    cert = get_certificate_by_model_name(cleaned)
    if cert:
        return render_audit_details_panel(cert)
    return render_audit_details_panel({})


def init_leaderboard(dataframe):
    return Leaderboard(
        value=dataframe,
        datatype=[c.type for c in fields(AutoEvalColumn)],
        select_columns=SelectColumns(
            default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default],
            cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden],
            label="Select Columns to Display:",
        ),
        search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
        hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
        interactive=False,
    )


def refresh_dashboard():
    """Re-pulls leaderboard + top-3 data. Bound to demo.load() so that if
    the module-import-time fetch raced ahead of a still-connecting Mongo
    client on a cold start, the UI corrects itself once the page mounts."""
    df = get_leaderboard_df(EVAL_RESULTS_PATH, "", COLS, BENCHMARK_COLS)
    top3 = get_top_3_eval_cards(EVAL_RESULTS_PATH)
    return df, build_top_3_cards_html(top3)


custom_head_js = """
<script>
window.selectTopModel = function(modelName) {
    if (!modelName) return;
    var el = document.querySelector('#hidden_card_model_input textarea') || document.querySelector('#hidden_card_model_input input');
    var btn = document.querySelector('#hidden_card_trigger_btn');
    if (el && btn) {
        el.value = modelName;
        el.dispatchEvent(new Event('input', { bubbles: true }));
        el.dispatchEvent(new Event('change', { bubbles: true }));
        btn.click();
    }
};
</script>
"""

combined_css = custom_css + """
.hidden-bridge {
  position: absolute !important;
  opacity: 0 !important;
  pointer-events: none !important;
  height: 0px !important;
  width: 0px !important;
  overflow: hidden !important;
  margin: 0 !important;
  padding: 0 !important;
}
"""

demo = gr.Blocks(
    theme=gr.themes.Soft(primary_hue="sky", secondary_hue="slate"),
    css=combined_css,
    head=custom_head_js,
    title="LLM-X-RAY Observatory",
)

with demo:
    gr.HTML(TITLE)
    gr.Markdown(INTRODUCTION_TEXT)

    hidden_card_input = gr.Textbox(elem_id="hidden_card_model_input", elem_classes=["hidden-bridge"])
    hidden_card_btn = gr.Button(elem_id="hidden_card_trigger_btn", elem_classes=["hidden-bridge"])

    top_cards_display = gr.HTML(build_top_3_cards_html(top_3_records))

    with gr.Row():
        with gr.Column(scale=3):
            search_or_audit_input = gr.Textbox(
                label="🔍 Search Registry or Enter Hugging Face Model URL to Audit",
                placeholder="e.g. openbmb/MiniCPM-1B-sft-bf16 or SupraLabs/Supra2-Nano",
                lines=1,
                max_lines=1,
                show_label=True,
            )
            trust_remote_code_checkbox = gr.Checkbox(
                label="Trust Remote Code (enabled for custom architectures like MiniCPM / Supra / Nanbeige)",
                value=True,
            )
            audit_button = gr.Button("🔬 SCAN & AUDIT MODEL", variant="primary")
            status_box = gr.HTML()

        with gr.Column(scale=4):
            results_display_panel = gr.HTML(render_audit_details_panel(initial_cert))

    leaderboard_table = init_leaderboard(LEADERBOARD_DF)

    # Audit & Search events
    audit_button.click(
        fn=audit_or_search_model,
        inputs=[search_or_audit_input, trust_remote_code_checkbox],
        outputs=[status_box, leaderboard_table, top_cards_display, results_display_panel],
    )
    search_or_audit_input.submit(
        fn=audit_or_search_model,
        inputs=[search_or_audit_input, trust_remote_code_checkbox],
        outputs=[status_box, leaderboard_table, top_cards_display, results_display_panel],
    )

    # Leaderboard row/cell click -> updates results display
    leaderboard_table.select(
        fn=on_select_model,
        inputs=[leaderboard_table],
        outputs=[results_display_panel],
    )

    # Top 3 card click -> updates results display
    hidden_card_btn.click(
        fn=on_top_card_click,
        inputs=[hidden_card_input],
        outputs=[results_display_panel],
    )

    # Page mount -> re-pull leaderboard + top-3 cards.
    # Self-healing refresh: guards against a cold-start race where the
    # module-level fetch above ran before the Mongo client finished
    # connecting, which would otherwise freeze a stale/incomplete
    # snapshot for every visitor until the container restarts.
    demo.load(fn=refresh_dashboard, outputs=[leaderboard_table, top_cards_display])

    with gr.Row():
        with gr.Accordion("📙 Methodology & Limitations", open=False):
            gr.Textbox(value=CITATION_BUTTON_TEXT, label=CITATION_BUTTON_LABEL, lines=8, show_copy_button=True)

if __name__ == "__main__":
    # demo.queue() routes all events and generator streams through WebSockets to eliminate SvelteKit 405 errors
    demo.queue(default_concurrency_limit=5).launch()
# if __name__ == "__main__":
#     demo.launch(server_name="0.0.0.0", server_port=7860)
# if __name__ == "__main__":
#     demo.launch(server_name="0.0.0.0", server_port=7860)