app.py

import concurrent.futures
import gc
import html
import inspect
import os
import re
import threading
import time
import traceback
import unicodedata

import gradio as gr
import sqlparse

import chat_state as chat_core
import intent as intent_core
import model_io as model_core
import sql_tools as sql_core


FINE_TUNED_MODEL_ID = "Shizu0n/phi3-mini-sql-generator-merged"

FINE_TUNED_MODEL_KEY = "fine_tuned"
DEFAULT_MODEL_KEY = FINE_TUNED_MODEL_KEY
FALLBACK_RESPONSE = (
    "Select a schema and ask a SQL question, "
    "or ask to create or edit a table. "
    "Example: 'what is the most expensive product?' or "
    "'create table products with id name price'."
)
UNSUPPORTED_MUTATION_RESPONSE = (
    "This demo does not generate INSERT, UPDATE, DELETE, or DROP statements. "
    "It only supports SELECT/WITH model SQL plus deterministic CREATE TABLE tools."
)
SOURCE_FINE_TUNED_MODEL = "Source: fine-tuned model"
SOURCE_DETERMINISTIC_SQL_TEMPLATE = "Source: deterministic SQL template"
SOURCE_DETERMINISTIC_SCHEMA_PARSER = "Source: deterministic schema parser"
SOURCE_STATIC_FALLBACK = "Source: static fallback"

MODEL_CATALOG = {
    FINE_TUNED_MODEL_KEY: {
        "label": "Fine-tuned QLoRA model",
        "short_label": "Fine-tuned",
        "tag": "Fine-tuned",
        "title": "QLoRA merged",
        "model_id": FINE_TUNED_MODEL_ID,
        "exact_match": "73.5%",
        "trust_remote_code": False,
        "ready_text": "Fine-tuned model ready",
        "metadata": (
            "Model: Shizu0n/phi3-mini-sql-generator-merged\n"
            "Base: microsoft/Phi-3-mini-4k-instruct\n"
            "Fine-tuning data: b-mc2/sql-create-context, 1,000 examples\n"
            "Metric: 73.5% exact match vs 2.0% base (+71.5pp)"
        ),
    },
}

PRESETS = {
    "employees": "CREATE TABLE employees (id INTEGER, name TEXT, department TEXT, salary NUMERIC)",
    "orders": "CREATE TABLE orders (id INTEGER, customer_id INTEGER, product TEXT, amount NUMERIC, date DATE)",
    "students": "CREATE TABLE students (id INTEGER, name TEXT, course TEXT, grade NUMERIC, year INTEGER)",
    "products": "CREATE TABLE products (id INTEGER, name TEXT, category TEXT, price NUMERIC, stock INTEGER)",
    "sales": "CREATE TABLE sales (id INTEGER, product_id INTEGER, quantity INTEGER, total NUMERIC, date DATE)",
}

PROMPT_TEMPLATE = (
    "<|user|>\n"
    "Given the following SQL table, write a SQL query.\n\n"
    "Table: {schema}\n\n"
    "Question: {question}<|end|>\n"
    "<|assistant|>"
)

GENERAL_PROMPT_TEMPLATE = (
    "<|user|>\n"
    "You are a SQL assistant. Answer the user's question.\n\n"
    "Question: {message}<|end|>\n"
    "<|assistant|>"
)

EMPTY_VALIDATOR = '<span class="validator-badge validator-empty">No SQL yet</span>'
CHAT_VALIDATOR = '<span class="validator-badge validator-empty">Chat response</span>'
EMPTY_CHAT_OUTPUT = ""
LOAD_TIMEOUT_SECONDS = 900
GENERATION_MAX_TIME_SECONDS = 285
GENERATION_TIMEOUT_SECONDS = 320
LOCAL_FILES_ONLY_ENV = "PHI3_SQL_LOCAL_FILES_ONLY"
LOAD_SCROLL_JS = """
(selectedKey) => {
  setTimeout(() => {
    document.querySelector("#query-section")?.scrollIntoView({
      behavior: "smooth",
      block: "start"
    });
  }, 50);
  return selectedKey;
}
"""

_current_model_id = None
_model = None
_tokenizer = None
_model_lock = threading.RLock()
_model_activity_lock = threading.Lock()


def import_model_runtime():
    try:
        import torch
        from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
    except ModuleNotFoundError as exc:
        missing = exc.name or "a runtime dependency"
        raise RuntimeError(
            f"Missing dependency: {missing}. Install the Space dependencies with "
            "`python -m pip install -r requirements.txt` in the same Python environment "
            "that runs app.py. HuggingFace Spaces installs requirements.txt during build."
        ) from exc

    return torch, AutoConfig, AutoModelForCausalLM, AutoTokenizer


def log_load_step(model_id, step, started=None):
    elapsed = "" if started is None else f" elapsed={time.time() - started:.1f}s"
    print(f"[LOAD_STEP] model={model_id} step={step}{elapsed}", flush=True)


def cached_model_weights_available(model_id):
    try:
        from huggingface_hub import try_to_load_from_cache
    except ModuleNotFoundError:
        return False

    weight_files = (
        "model.safetensors",
        "model.safetensors.index.json",
        "pytorch_model.bin",
        "pytorch_model.bin.index.json",
    )
    for filename in weight_files:
        try:
            cached_path = try_to_load_from_cache(model_id, filename)
        except Exception:
            cached_path = None
        if isinstance(cached_path, str) and os.path.exists(cached_path):
            return True
    return False


def cached_file_path(model_id, filename):
    try:
        from huggingface_hub import try_to_load_from_cache
    except ModuleNotFoundError:
        return None

    try:
        cached_path = try_to_load_from_cache(model_id, filename)
    except Exception:
        return None
    if isinstance(cached_path, str) and os.path.exists(cached_path):
        return cached_path
    return None


def cached_snapshot_path(model_id):
    config_path = cached_file_path(model_id, "config.json")
    if not config_path or not cached_model_weights_available(model_id):
        return None
    return os.path.dirname(config_path)


def local_files_only_for(model_id):
    explicit_local = os.getenv(LOCAL_FILES_ONLY_ENV, "").strip().lower() in {"1", "true", "yes", "on"}
    offline_mode = bool(os.getenv("HF_HUB_OFFLINE") or os.getenv("TRANSFORMERS_OFFLINE"))
    return explicit_local or offline_mode


def running_on_spaces():
    return bool(os.getenv("SPACE_ID"))


def resolve_model_source(model_id):
    if local_files_only_for(model_id):
        return cached_snapshot_path(model_id) or model_id
    return model_id


def dtype_from_name(torch, dtype_name):
    if not dtype_name:
        return None
    normalized = str(dtype_name).replace("torch.", "")
    return {
        "float16": torch.float16,
        "bfloat16": torch.bfloat16,
        "float32": torch.float32,
    }.get(normalized)


def dtype_from_safetensors(torch, source):
    safetensors_path = os.path.join(source, "model.safetensors")
    if not os.path.exists(safetensors_path):
        return None
    try:
        from safetensors import safe_open

        with safe_open(safetensors_path, framework="pt", device="cpu") as handle:
            keys = list(handle.keys())
            if not keys:
                return None
            return handle.get_tensor(keys[0]).dtype
    except Exception:
        return None


def cpu_model_dtype(torch):
    return torch.bfloat16


def model_load_kwargs(torch, config, source):
    return {
        "attn_implementation": "eager",
        "device_map": {"": "cpu"},
        "low_cpu_mem_usage": True,
        "torch_dtype": "auto",
    }


def force_eager_attention(config):
    for attr in ("attn_implementation", "_attn_implementation"):
        try:
            setattr(config, attr, "eager")
        except Exception:
            pass
    return config


def _run_generation(model, inputs, kwargs):
    if not _model_activity_lock.acquire(blocking=False):
        raise RuntimeError(
            "Another model operation is still running. Wait for it to finish before starting another request."
        )
    torch, _, _, _ = import_model_runtime()
    try:
        with torch.no_grad():
            return model.generate(**inputs, **kwargs)
    finally:
        _model_activity_lock.release()


def _run_model_load(model_id):
    return load_model(model_id)


def patch_phi3_config(config):
    if hasattr(config, "rope_scaling") and config.rope_scaling:
        rope_type = config.rope_scaling.get("rope_type", "longrope")
        if "type" not in config.rope_scaling:
            config.rope_scaling["type"] = rope_type
        if hasattr(config, "rope_parameters") and config.rope_parameters is None:
            config.rope_parameters = dict(config.rope_scaling)
    return config


def unload_model():
    global _current_model_id, _model, _tokenizer
    with _model_lock:
        if _model is not None:
            del _model
        if _tokenizer is not None:
            del _tokenizer
        _model = None
        _tokenizer = None
        _current_model_id = None
        gc.collect()
        try:
            import torch

            if torch.cuda.is_available():
                torch.cuda.empty_cache()
        except ImportError:
            pass


def load_model(model_id):
    global _current_model_id, _model, _tokenizer
    started = time.time()
    log_load_step(model_id, "requested", started)
    if not _model_lock.acquire(blocking=False):
        raise RuntimeError("Another model load is still running. Wait for it to finish before retrying.")
    try:
        if _current_model_id == model_id and _model is not None and _tokenizer is not None:
            log_load_step(model_id, "already_loaded", started)
            return _model, _tokenizer

        if not _model_activity_lock.acquire(blocking=False):
            raise RuntimeError(
                "Another model operation is still running. Wait for it to finish before switching models."
            )
        try:
            log_load_step(model_id, "runtime_import_start", started)
            torch, AutoConfig, AutoModelForCausalLM, AutoTokenizer = import_model_runtime()
            log_load_step(model_id, "runtime_import_done", started)
            local_files_only = local_files_only_for(model_id)
            model_source = resolve_model_source(model_id)
            log_load_step(model_id, f"cache_mode local_files_only={local_files_only}", started)
            log_load_step(model_id, f"model_source {model_source}", started)
            log_load_step(model_id, "unload_previous_start", started)
            unload_model()
            log_load_step(model_id, "unload_previous_done", started)
            model_def = model_by_id(model_id)
            common_kwargs = {
                "trust_remote_code": model_def["trust_remote_code"],
                "local_files_only": local_files_only,
            }
            log_load_step(model_id, "config_start", started)
            config = AutoConfig.from_pretrained(
                model_source,
                **common_kwargs,
            )
            if model_def["trust_remote_code"]:
                config = patch_phi3_config(config)
            config = force_eager_attention(config)
            log_load_step(model_id, "config_done", started)
            load_kwargs = model_load_kwargs(torch, config, model_source)
            log_load_step(model_id, f"model_kwargs {load_kwargs}", started)
            log_load_step(model_id, "tokenizer_start", started)
            tokenizer = AutoTokenizer.from_pretrained(
                model_source,
                **common_kwargs,
            )
            if tokenizer.pad_token_id is None and tokenizer.eos_token is not None:
                tokenizer.pad_token = tokenizer.eos_token
            log_load_step(model_id, "tokenizer_done", started)
            log_load_step(model_id, "weights_start", started)
            model = AutoModelForCausalLM.from_pretrained(
                model_source,
                config=config,
                **common_kwargs,
                **load_kwargs,
            )
            log_load_step(model_id, "weights_done", started)
            log_load_step(model_id, f"loaded_dtype {getattr(model, 'dtype', 'unknown')}", started)
            log_load_step(model_id, "eval_start", started)
            model.config.use_cache = False
            model.eval()
            log_load_step(model_id, "eval_done", started)

            _model = model
            _tokenizer = tokenizer
            _current_model_id = model_id
            log_load_step(model_id, "state_set_done", started)
            return model, tokenizer
        finally:
            _model_activity_lock.release()
    finally:
        _model_lock.release()


def model_by_key(model_key):
    return MODEL_CATALOG.get(model_key, MODEL_CATALOG[DEFAULT_MODEL_KEY])


def model_by_id(model_id):
    for model_def in MODEL_CATALOG.values():
        if model_def["model_id"] == model_id:
            return model_def
    raise ValueError(f"Unknown model id: {model_id}")


def model_key_by_id(model_id):
    for key, model_def in MODEL_CATALOG.items():
        if model_def["model_id"] == model_id:
            return key
    return None


def content_to_text(value):
    if value is None:
        return ""
    if isinstance(value, str):
        return value
    if isinstance(value, dict):
        for key in ("text", "content", "value"):
            if key in value:
                return content_to_text(value[key])
        return " ".join(content_to_text(item) for item in value.values())
    if isinstance(value, (list, tuple)):
        return "\n".join(content_to_text(item) for item in value)
    return str(value)


def normalize_text(value):
    text = content_to_text(value).lower()
    text = unicodedata.normalize("NFKD", text)
    text = "".join(char for char in text if not unicodedata.combining(char))
    return re.sub(r"\s+", " ", text).strip()


def safe_chat_fallback(_message=""):
    return FALLBACK_RESPONSE


def clean_generation(text):
    cleaned = content_to_text(text).strip()
    if cleaned.startswith("```"):
        lines = cleaned.splitlines()
        if lines and lines[0].strip().lower() in {"```", "```sql"}:
            lines = lines[1:]
        if lines and lines[-1].strip() == "```":
            lines = lines[:-1]
        cleaned = "\n".join(lines).strip()
    for marker in ("<|end|>", "<|user|>", "<|assistant|>", "</s>"):
        if marker in cleaned:
            cleaned = cleaned.split(marker, 1)[0].strip()
    if cleaned.upper().startswith("SQL:"):
        cleaned = cleaned[4:].strip()
    return cleaned


def extract_sql_candidate(text):
    cleaned = clean_generation(text)
    match = re.search(r"\b(SELECT|WITH|INSERT|UPDATE|DELETE|CREATE|ALTER|DROP)\b", cleaned, flags=re.IGNORECASE)
    if not match:
        return cleaned
    return cleaned[match.start() :].strip()


def is_sql_like(text):
    text = (text or "").strip()
    if not text:
        return False
    first_word = re.match(r"^\s*([A-Za-z]+)", text)
    if not first_word:
        return False
    return first_word.group(1).upper() in {
        "SELECT",
        "WITH",
        "INSERT",
        "UPDATE",
        "DELETE",
        "CREATE",
        "ALTER",
        "DROP",
    }




def render_theme_detection_style():
    return """
    <style>
      @media (prefers-color-scheme: light) {
        :root,
        body,
        gradio-app,
        .gradio-container,
        .gradio-container .main,
        .gradio-container .wrap,
        .gradio-container .contain {
          color-scheme: light;
          --bg-base: #edf2f7;
          --bg-surface: #ffffff;
          --bg-raised: #f4f7fb;
          --border: #d8e0ea;
          --border-hi: #c5cfdd;
          --text-hi: #172033;
          --text-mid: #667085;
          --text-lo: #8a95a6;
          --text-primary: var(--text-hi);
          --text-secondary: var(--text-mid);
          --text-muted: var(--text-lo);
          --teal: #0d8b67;
          --teal-soft: #dff6ec;
          --teal-text: #076348;
          --amber-soft: #fff3d8;
          --amber-text: #7a4700;
          --overlay-bg: rgba(23, 32, 51, 0.28);
          --chat-bubble-bot-bg: #f4f7fb;
          --chat-bubble-user-bg: #edf6ff;
          --chat-pending-bg: #e8eef8;
          --chat-pending-text: #172033;
          --chat-copy-btn-bg: #ffffff;
          --code-bg: #101828;
          --code-border: #25344d;
          --code-text: #d7e3f3;
          --schema-context-bg: #f3fbf8;
          --disabled-bg: #b7c3cf;
        }

        .gradio-container,
        .gradio-container .main,
        .gradio-container .wrap,
        .gradio-container .contain {
          background: var(--bg-base) !important;
          color: var(--text-primary) !important;
        }
      }
    </style>
    """


def render_header():
    return """
    <section class="top-panel">
      <div>
        <h1>Phi-3 Mini SQL Chatbot</h1>
        <p>Text-to-SQL demo with explicit model and guardrail boundaries.</p>
      </div>
      <div class="top-badges">
        <span class="badge badge-green">Fine-tuned model: SQL_QUERY</span>
        <span class="badge badge-cream">Deterministic guardrails</span>
        <span class="badge badge-light">CPU lazy load</span>
      </div>
    </section>
    """


def render_step(number, title):
    return f"""
    <div class="step-title">
      <span>{number} &mdash; {title}</span>
    </div>
    """


def render_model_card(model_key, selected_key):
    model_def = model_by_key(model_key)
    selected = model_key == selected_key
    state_class = " selected" if selected else ""
    return f"""
    <article class="model-card{state_class}">
      <div class="model-tag">{model_def["tag"]}</div>
      <h3>{model_def["title"]}</h3>
      <code>{model_def["model_id"]}</code>
      <div class="model-score">
        <span>{model_def["exact_match"]}</span>
        <small>exact match</small>
      </div>
      <div class="model-card-footer">
        <span>{model_def["label"]}</span>
      </div>
      {render_baseline_evidence()}
    </article>
    """


def render_status(selected_key=None, loaded_key=None, state="idle"):
    selected_def = model_by_key(selected_key or DEFAULT_MODEL_KEY)
    if state == "loading":
        return (
            '<div class="status-pill status-loading">'
            f'<span></span> {SOURCE_FINE_TUNED_MODEL}. Loading {selected_def["short_label"]} model - first load ~3-5 min'
            "</div>"
        )
    if loaded_key:
        loaded_def = model_by_key(loaded_key)
        return (
            '<div class="status-pill status-ready">'
            f'<span></span> {SOURCE_FINE_TUNED_MODEL}. {loaded_def["short_label"]} model ready - ~3-5 min per query on CPU'
            "</div>"
        )
    return f'<div class="status-pill status-empty"><span></span> {SOURCE_FINE_TUNED_MODEL}. No model loaded</div>'


def render_loading_overlay(model_key=None, visible=False):
    if not visible:
        return '<div class="loading-overlay hidden"></div>'
    model_def = model_by_key(model_key or DEFAULT_MODEL_KEY)
    return (
        '<div class="loading-overlay">'
        '<div class="loading-card">'
        f'<div class="loading-title">Loading {model_def["short_label"]} model</div>'
        '<div class="loading-line"><span></span></div>'
        '<p>First load: ~3-5 min &mdash; cached for session</p>'
        "</div>"
        "</div>"
    )


def model_metadata(model_key=None):
    return f"""
    <section class="metadata-panel">
      <div class="panel-heading">
        <h2>Source contract</h2>
        <span>SQL_QUERY + create/edit/fallback</span>
      </div>
      <div class="metadata-body">
        {render_source_contract_grid()}
      </div>
    </section>
    """


def render_source_contract_grid():
    return """
    <div class="stats-row stats-row-compact source-contract-grid">
      <div class="stat-card"><strong>SQL_QUERY</strong><span>templates first; model only for SELECT/WITH</span></div>
      <div class="stat-card"><strong>Create</strong><span>deterministic CREATE TABLE parser</span></div>
      <div class="stat-card"><strong>Edit</strong><span>deterministic schema updates</span></div>
      <div class="stat-card"><strong>Fallback</strong><span>static non-SQL response</span></div>
    </div>
    """


def render_source_legend(extra_class=""):
    class_name = "source-panel"
    if extra_class:
        class_name = f"{class_name} {extra_class}"
    return f"""
    <section class="{class_name}">
      <div class="panel-heading">
        <h2>Source contract</h2>
        <span>always visible</span>
      </div>
      <div class="metadata-body">
        {render_source_contract_grid()}
      </div>
    </section>
    """


def render_example_prompts():
    return """
    <section class="example-panel">
      <div class="panel-heading">
        <h2>Example prompts</h2>
        <span>by source</span>
      </div>
      <div class="example-list">
        <div class="example-group"><span>fine-tuned model</span><code>show employees in Engineering ordered by salary</code></div>
        <div class="example-group"><span>deterministic SQL template</span><code>what is the most expensive product?</code></div>
        <div class="example-group"><span>deterministic schema parser</span><code>create table animals with id name species weight</code></div>
        <div class="example-group"><span>static fallback</span><code>what can you do?</code></div>
      </div>
    </section>
    """


def render_baseline_evidence():
    return """
    <div class="model-card-evidence">
      <span class="model-card-evidence-heading">Offline evidence</span>
      <div class="model-card-evidence-grid">
        <div class="model-card-evidence-chip">
          <strong>Base</strong>
          <small>2.0%</small>
        </div>
        <div class="model-card-evidence-chip highlighted">
          <strong>Fine-tuned</strong>
          <small>73.5%</small>
        </div>
        <div class="model-card-evidence-chip">
          <strong>Gain</strong>
          <small>+71.5pp</small>
        </div>
      </div>
    </div>
    """


def schema_name_by_value(schema):
    schema = (schema or "").strip()
    for name, value in PRESETS.items():
        if value == schema:
            return name
    table_name, _columns = sql_core.parse_create_table_schema(schema)
    if table_name:
        return table_name
    return "custom"


def is_create_table_intent(message):
    message = (message or "").strip().lower()
    return bool(
        re.search(r"\b(create|make|build|generate|criar|crie|cria|gerar|gere|faz|faça)\b", message)
        and re.search(r"\b(table|schema|tabela)\b", message)
    )


def is_table_edit_intent(message):
    message = (message or "").strip().lower()
    edit_terms = r"\b(edit|update|modify|alter|add|include|remove|delete|drop|edita|editar|altera|altere|alterar|mude|mudar|adicione|adicionar|inclua|incluir|acrescente|remova|remover|delete|deletar|exclua|excluir|novo|nova|troca|trocar|troquecoloque|colocar)\b"
    direct_add_terms = r"\b(add|include|adicione|adicionar|adicionando|inclua|incluir|acrescente)\b"
    direct_remove_terms = r"\b(remove|delete|drop|remova|remover|deletar|exclua|excluir)\b"
    target_terms = r"\b(column|field|element|coluna|campo|elemento|item)\b"
    # SQL aggregation keywords that indicate query, not table edit
    sql_aggregation_terms = {"up", "sum", "total", "count", "average", "avg", "max", "min", "by"}
    words = message.split()
    # For add: require target term OR check if it's clearly a column name list
    # "add up the total" is SQL query; "add email and phone" is table edit
    add_match = re.search(direct_add_terms, message)
    has_target = re.search(target_terms, message)
    if add_match:
        # Find position after "add" keyword
        match_pos = add_match.start()
        after_add = message[match_pos + len(add_match.group()):].strip()
        first_word_after = after_add.split()[0] if after_add.split() else ""
        # If first word after "add" is aggregation term, it's SQL query, not edit
        is_sql_query = first_word_after in sql_aggregation_terms
        is_add_intent = not is_sql_query
    else:
        is_add_intent = False
    return bool(
        is_add_intent
        or re.search(direct_remove_terms, message)
        or is_rename_intent(message)
        or re.search(r"\b(?:altere|alterar|mude|mudar)\b.*\bter\b", message)
        or (re.search(edit_terms, message) and (re.search(target_terms, message) or ":" in message or re.search(r"\bpor\b", message)))
    )


def infer_column_type(column_name):
    name = column_name.strip().lower()
    if name == "id" or name.endswith("_id") or name in {"quantity", "quantidade", "stock", "estoque", "year"}:
        return "INTEGER"
    if name in {
        "salary",
        "price",
        "preco",
        "amount",
        "total",
        "grade",
        "peso",
        "weight",
        "idade",
        "age",
        "altura",
        "height",
        "largura",
        "width",
        "comprimento",
        "length",
        "desconto",
        "discount",
    }:
        return "NUMERIC"
    if name in {"date", "created_at", "updated_at"} or name.endswith("_date"):
        return "DATE"
    return "TEXT"


def normalize_identifier(value):
    identifier = re.sub(r"\W+", "_", normalize_text(value)).strip("_")
    if not identifier:
        return ""
    if identifier[0].isdigit():
        identifier = f"col_{identifier}"
    return identifier


def parse_column_definition(raw_column):
    raw_column = re.sub(r"\b(for me|please|por favor)\b", "", raw_column or "", flags=re.IGNORECASE)
    raw_column = raw_column.strip(" .;:")
    if not raw_column:
        return None

    # P2 fix: look for the type as the FINAL token, not the first match
    # "date DATE" should be interpreted as name="date", type="DATE", not name="" type="date"
    type_matches = list(
        re.finditer(
            r"\b(integer|int|numeric|decimal|real|float|double|text|varchar|char|date|datetime|timestamp|boolean|bool)\b",
            raw_column,
            flags=re.IGNORECASE,
        )
    )
    explicit_type = type_matches[-1] if type_matches else None
    if explicit_type:
        name_part = raw_column[: explicit_type.start()].strip()
        column_type = explicit_type.group(1).upper()
        if column_type == "INT":
            column_type = "INTEGER"
        elif column_type == "BOOL":
            column_type = "BOOLEAN"
        elif column_type == "DECIMAL":
            column_type = "NUMERIC"
        elif column_type in {"FLOAT", "DOUBLE"}:
            column_type = "REAL"
        if not name_part.strip():
            column_type = None
            name_part = raw_column
    else:
        name_part = raw_column
        column_type = None

    name_part = re.sub(r"\b(column|field|coluna|campo)\b", "", name_part, flags=re.IGNORECASE)
    column_name = normalize_identifier(name_part)
    if not column_name:
        return None
    return column_name, column_type or infer_column_type(column_name)


def split_column_list(columns_text):
    columns_text = re.sub(r"\s+(and|e)\s+", ",", columns_text or "", flags=re.IGNORECASE)
    parts = []
    type_pattern = (
        r"\b(integer|int|numeric|decimal|real|float|double|text|varchar|char|date|datetime|timestamp|boolean|bool)\b"
    )
    type_tokens = {
        "integer",
        "int",
        "numeric",
        "decimal",
        "real",
        "float",
        "double",
        "text",
        "varchar",
        "char",
        "date",
        "datetime",
        "timestamp",
        "boolean",
        "bool",
    }
    STOPWORDS = {
        "to", "from", "into", "as", "for",
        "o", "a", "os", "de", "do", "da", "dos", "das",
    }
    for part in (item.strip() for item in columns_text.split(",") if item.strip()):
        tokens = [token.strip() for token in re.split(r"\s+", part) if token.strip()]
        tokens = [t for t in tokens if t.lower() not in STOPWORDS]
        if not tokens:
            continue
        if re.search(type_pattern, part, flags=re.IGNORECASE) and len(tokens) > 2:
            index = 0
            # Column names that could be confused with SQL types when followed by date/datetime/timestamp
            # These should be treated as column names, not as part of type specification
            inferrable_names = {"total", "date", "time", "timestamp", "int", "text", "real", "char"}
            while index < len(tokens):
                current = tokens[index]
                next_token = tokens[index + 1].lower() if index + 1 < len(tokens) else ""
                # If current could be inferred as a different type, don't pair with date/datetime/timestamp
                # This preserves "total date" → "total" (inferred NUMERIC) + "date" (type)
                if next_token in type_tokens and not (current.lower() in inferrable_names and next_token in {"date", "datetime", "timestamp"}):
                    parts.append(f"{current} {tokens[index + 1]}")
                    index += 2
                else:
                    parts.append(current)
                    index += 1
            continue
        if re.search(type_pattern, part, flags=re.IGNORECASE):
            parts.append(part)
            continue
        if len(tokens) > 1 and all(re.match(r"^[A-Za-z_][\wàáâãçèéêíóôõúÀÁÂÃÇÈÉÊÍÓÔÕÚ]*$", token) for token in tokens):
            parts.extend(tokens)
        else:
            parts.append(part)
    return parts


def format_create_table(table_name, columns):
    if not table_name or not columns:
        return ""
    seen = set()
    column_lines = []
    for column_name, column_type in columns:
        if column_name in seen:
            continue
        seen.add(column_name)
        column_lines.append(f"    {column_name} {column_type}")
    if not column_lines:
        return ""
    return f"CREATE TABLE {table_name} (\n" + ",\n".join(column_lines) + "\n);"


def parse_create_table_schema(schema):
    schema = (schema or "").strip()
    match = re.match(
        r"^\s*(?:CREATE\s+TABLE\s+)?([A-Za-z_][\w]*)\s*\((.*?)\)\s*;?\s*$",
        schema,
        flags=re.IGNORECASE | re.DOTALL,
    )
    if not match:
        return "", []
    table_name = normalize_identifier(match.group(1))
    columns = [
        parsed
        for parsed in (parse_column_definition(column) for column in split_column_list(match.group(2)))
        if parsed
    ]
    return table_name, columns


def extract_create_table_statement(text):
    cleaned = extract_sql_candidate(text)
    match = re.search(
        r"\bCREATE\s+TABLE\s+[A-Za-z_][\w]*\s*\(.*?\)\s*;?",
        cleaned,
        flags=re.IGNORECASE | re.DOTALL,
    )
    return clean_generation(match.group(0)) if match else ""


def last_create_table_from_history(chat_history):
    for item in reversed(list(chat_history or [])):
        if not isinstance(item, dict) or item.get("role") != "assistant":
            continue
        statement = extract_create_table_statement(item.get("content", ""))
        if statement:
            return statement
    return ""


def extract_added_columns(message):
    message = (message or "").strip()
    patterns = (
        r":\s*(.+)$",
        r"\b(?:add|include|with|adicionar|adicione|adicionando|inclua|incluir|acrescente|ter)\b\s+(?:um\s+|uma\s+|a\s+|an\s+)?(?:novo\s+|nova\s+|new\s+)?(?:column|field|element|coluna|campo|elemento|item)?\s*(.+)$",
    )
    for pattern in patterns:
        match = re.search(pattern, message, flags=re.IGNORECASE)
        if not match:
            continue
        columns = [
            parsed
            for parsed in (parse_column_definition(column) for column in split_column_list(match.group(1)))
            if parsed
        ]
        if columns:
            return columns
    return []


def extract_removed_columns(message):
    message = (message or "").strip()
    patterns = (
        r"\b(?:remove|delete|drop|remova|remover|deletar|exclua|excluir)\b\s+(?:a\s+|o\s+|the\s+)?(?:column|field|element|coluna|campo|elemento|item)?\s*(.+)$",
    )
    for pattern in patterns:
        match = re.search(pattern, message, flags=re.IGNORECASE)
        if not match:
            continue
        columns = [normalize_identifier(column) for column in split_column_list(match.group(1))]
        columns = [column for column in columns if column]
        if columns:
            return columns
    return []


def is_rename_intent(message):
    message = (message or "").strip().lower()
    return bool(
        re.search(
            r"\b(rename|edit|change|renomeie|renomear|renomeia|renomeia|altere|mude|muda|troca|trocar)\s+\w+\s+(to|para|as|como|por)\s+\w+",
            message,
            flags=re.IGNORECASE,
        )
    )


def extract_renamed_columns(message):
    pattern = (
        r"\b(?:rename|edit|change|renomeie|renomear|altere|mude)\s+"
        r"(\w+)\s+(?:to|para|as|como)\s+(\w+)"
    )
    matches = re.findall(pattern, message or "", flags=re.IGNORECASE)
    # Also handle "troca X por Y" pattern
    troca_matches = re.findall(
        r"\btroca\b\s+(\w+)\s+\bpor\b\s+(\w+)",
        message or "",
        flags=re.IGNORECASE,
    )
    all_matches = matches + troca_matches
    return [
        (normalize_identifier(old), normalize_identifier(new))
        for old, new in all_matches
        if normalize_identifier(old) and normalize_identifier(new)
    ]


def parse_compound_edit(message):
    """Split a compound prompt into segments and extract add/remove/rename."""
    segment_pattern = (
        r"\s+(?:and|e)\s+"
        r"(?=\b(?:add|include|remove|delete|drop|rename|edit|change|"
        r"adicione|adicionar|inclua|acrescente|remova|remover|deletar|"
        r"exclua|renomeie|renomear|altere|mude|troca|trocar)\b)"
    )
    segments = re.split(segment_pattern, message or "", flags=re.IGNORECASE)

    added, removed, renamed = [], [], []
    for seg in segments:
        seg = seg.strip()
        if not seg:
            continue
        if is_rename_intent(seg):
            renamed.extend(extract_renamed_columns(seg))
        elif re.search(
            r"\b(remove|delete|drop|remova|remover|deletar|exclua|excluir)\b",
            seg,
            flags=re.IGNORECASE,
        ):
            removed.extend(extract_removed_columns(seg))
        else:
            cols = extract_added_columns(seg)
            if cols:
                added.extend(cols)
    return added, removed, renamed


def render_schema_context(schema=""):
    schema = (schema or "").strip()
    if not schema:
        return '<div class="schema-context empty"><span>No active schema</span><code>Select a preset or create a table.</code></div>'
    label = schema_name_by_value(schema)
    escaped_schema = html.escape(schema)
    escaped_label = html.escape(label)
    return (
        '<div class="schema-context">'
        f'<span>Context: {escaped_label}</span>'
        f'<code>{escaped_schema}</code>'
        "</div>"
    )


def query_control_updates(can_generate):
    context_updates = [gr.update(interactive=True) for _ in range(6)]
    # Keep submit button enabled - model requirement is checked in generate_response
    return [*context_updates, gr.update(interactive=True), gr.update(interactive=True)]


def render_message(message="", kind="error"):
    if not message:
        return '<div class="message-box message-empty"></div>'
    class_name = "message-ok" if kind == "ok" else "message-error"
    return f'<div class="message-box {class_name}">{html.escape(str(message))}</div>'


def load_selected_model(selected_key=FINE_TUNED_MODEL_KEY):
    selected_key = FINE_TUNED_MODEL_KEY
    model_def = model_by_key(selected_key)
    print(
        f"[LOAD_REQUEST] selected_key={selected_key} model_id={model_def['model_id']}",
        flush=True,
    )
    yield (
        None,
        render_status(selected_key, None, state="loading"),
        render_loading_overlay(selected_key, visible=True),
        model_metadata(selected_key),
        gr.update(interactive=False, visible=False),
        *query_control_updates(False),
        "",
        EMPTY_VALIDATOR,
        render_message(),
    )
    started = time.time()
    try:
        executor = concurrent.futures.ThreadPoolExecutor(max_workers=1)
        future = executor.submit(_run_model_load, model_def["model_id"])
        try:
            result = future.result(timeout=LOAD_TIMEOUT_SECONDS)
        except concurrent.futures.TimeoutError:
            # Timeout reached but cannot truly cancel a running thread.
            # Wait for the operation to complete naturally to avoid race conditions.
            # The UI stays in loading state until the operation finishes.
            result = future.result()
            print(f"[LOAD] Completed after timeout warning ({int(time.time() - started)}s)", flush=True)
        finally:
            executor.shutdown(wait=False, cancel_futures=True)
    except Exception as exc:
        error = f"{SOURCE_FINE_TUNED_MODEL}. Load failed for {model_def['model_id']}: {type(exc).__name__}: {exc}"
        print(f"[LOAD_ERROR] {error}", flush=True)
        traceback.print_exc()
        yield (
            None,
            render_status(selected_key, None),
            render_loading_overlay(visible=False),
            model_metadata(selected_key),
            gr.update(interactive=True, visible=True),
            *query_control_updates(False),
            "",
            EMPTY_VALIDATOR,
            render_message(error),
        )
        return

    elapsed = int(time.time() - started)
    yield (
        selected_key,
        render_status(selected_key, selected_key),
        render_loading_overlay(visible=False),
        model_metadata(selected_key),
        gr.update(interactive=True, visible=True, value="Load fine-tuned model"),
        *query_control_updates(True),
        "",
        EMPTY_VALIDATOR,
            render_message(f"{SOURCE_FINE_TUNED_MODEL}. Loaded {model_def['model_id']} in {elapsed}s.", kind="ok"),
    )


def set_preset(name):
    schema = PRESETS[name]
    return schema, render_schema_context(schema), gr.update(visible=True), chat_core.default_state(schema)


def clear_schema_context():
    return "", render_schema_context(""), gr.update(visible=False), chat_core.default_state("")


def trim_chat_history(chat_history, max_exchanges=10):
    history = list(chat_history or [])
    return history[-max_exchanges * 2 :]


def _append_chat_turn(chat_history, message, assistant_content):
    return trim_chat_history(
        [
            *list(chat_history or []),
            {"role": "user", "content": message},
            {"role": "assistant", "content": assistant_content},
        ]
    )


def _response_tuple(
    chat_history,
    message,
    state,
    assistant_content,
    status_message,
    *,
    sql_text="",
    validator=CHAT_VALIDATOR,
    status_kind="ok",
):
    state = chat_core.ConversationState.from_value(state)
    if sql_text and "CREATE TABLE" in sql_text.upper():
        state = state.with_active_schema(sql_text)
    new_history = _append_chat_turn(chat_history, message, assistant_content)
    return (
        new_history,
        "",
        state.active_schema,
        message,
        sql_text,
        validator,
        render_message(status_message, kind=status_kind),
        state.to_dict(),
        render_schema_context(state.active_schema),
        gr.update(visible=bool(state.active_schema.strip())),
    )


def deterministic_response(
    chat_history,
    message,
    active_schema,
    loaded_key,
    assistant_content,
    status_message,
    *,
    sql_text="",
    validator=CHAT_VALIDATOR,
    status_kind="ok",
    conversation_state=None,
):
    state = chat_core.ConversationState.from_value(conversation_state, active_schema=active_schema)
    return _response_tuple(
        chat_history,
        message,
        state,
        assistant_content,
        status_message,
        sql_text=sql_text,
        validator=validator,
        status_kind=status_kind,
    )


def _model_ready(loaded_key):
    if not loaded_key or _model is None or _tokenizer is None:
        return False, "Load the fine-tuned model before generating SQL."
    model_def = model_by_key(loaded_key)
    if _current_model_id != model_def["model_id"]:
        return False, "Loaded model state is inconsistent. Reload the selected model."
    return True, ""


def _generate_model_text(prompt, generation_kind=model_core.SQL_GENERATION):
    started = time.time()
    import_model_runtime()
    with _model_lock:
        model = _model
        tokenizer = _tokenizer
        if model is None or tokenizer is None:
            raise RuntimeError("Model runtime is not loaded.")

        inputs = tokenizer(prompt, return_tensors="pt")
        input_length = inputs["input_ids"].shape[-1]
        generation_config = getattr(model, "generation_config", None)
        gen_kwargs = {
            "max_new_tokens": model_core.generation_budget(generation_kind),
            "max_time": GENERATION_MAX_TIME_SECONDS,
            "do_sample": False,
            "use_cache": False,
            "repetition_penalty": 1.1,
            "eos_token_id": getattr(generation_config, "eos_token_id", tokenizer.eos_token_id),
            "pad_token_id": tokenizer.pad_token_id or tokenizer.eos_token_id,
        }
        executor = concurrent.futures.ThreadPoolExecutor(max_workers=1)
        future = executor.submit(_run_generation, model, inputs, gen_kwargs)
        try:
            output_ids = future.result(timeout=GENERATION_TIMEOUT_SECONDS)
        except concurrent.futures.TimeoutError:
            executor.shutdown(wait=False, cancel_futures=False)
            raise TimeoutError(f"Generation timed out after {GENERATION_TIMEOUT_SECONDS}s")
        finally:
            executor.shutdown(wait=False, cancel_futures=True)
        generated_ids = output_ids[0][input_length:]
        generated_text = tokenizer.decode(generated_ids, skip_special_tokens=True)
    return generated_text, int(time.time() - started)


def _empty_generation_response(
    chat_history,
    message,
    state,
    status_message,
    *,
    status_kind="error",
    source_label="",
):
    status_text = f"{source_label}. {status_message}" if source_label else status_message
    return (
        chat_history,
        message,
        state.active_schema,
        "",
        "",
        EMPTY_VALIDATOR,
        render_message(status_text, kind=status_kind),
        state.to_dict(),
        render_schema_context(state.active_schema),
        gr.update(visible=bool(state.active_schema.strip())),
    )


def generate_response(message, chat_history, active_schema, loaded_key, conversation_state=None):
    message = (message or "").strip()
    chat_history = list(chat_history or [])
    state = chat_core.ConversationState.from_value(conversation_state, active_schema=(active_schema or ""))
    if not message:
        return (
            chat_history,
            "",
            state.active_schema,
            "",
            "",
            EMPTY_VALIDATOR,
            render_message("Type a message before sending."),
            state.to_dict(),
            render_schema_context(state.active_schema),
            gr.update(visible=bool(state.active_schema.strip())),
        )

    intent_result = intent_core.classify_intent(message, state, chat_history)
    state = state.with_intent(intent_result)
    print(
        f"[ROUTING] \"{message[:60]}\" -> intent={intent_result.intent} "
        f"confidence={intent_result.confidence} reason={intent_result.reason}",
        flush=True,
    )

    if intent_result.intent == intent_core.EDIT_TABLE:
        edited_table = sql_core.edit_create_table_from_message(message, chat_history, state.active_schema)
        if edited_table:
            display_response = f"```sql\n{edited_table}\n```"
            return _response_tuple(
                chat_history,
                message,
                state,
                display_response,
                f"{SOURCE_DETERMINISTIC_SCHEMA_PARSER}. Edited CREATE TABLE without calling the model.",
                sql_text=edited_table,
                validator=sql_core.validate_sql(edited_table),
            )
        if sql_core.last_create_table_from_history(chat_history) or sql_core.create_table_from_schema(state.active_schema):
            return _empty_generation_response(
                chat_history,
                message,
                state,
                "No matching schema column was changed.",
                source_label=SOURCE_DETERMINISTIC_SCHEMA_PARSER,
            )
        return _empty_generation_response(
            chat_history,
            message,
            state,
            "I need an existing CREATE TABLE in the chat or an active schema before editing columns.",
            source_label=SOURCE_DETERMINISTIC_SCHEMA_PARSER,
        )

    if intent_result.intent == intent_core.CREATE_TABLE:
        sql_text = sql_core.create_table_from_message(message) or sql_core.create_table_from_schema(state.active_schema)
        if sql_text:
            display_response = f"```sql\n{sql_text}\n```"
            return _response_tuple(
                chat_history,
                message,
                state,
                display_response,
                f"{SOURCE_DETERMINISTIC_SCHEMA_PARSER}. Generated CREATE TABLE without calling the model.",
                sql_text=sql_text,
                validator=sql_core.validate_sql(sql_text),
            )
        return _empty_generation_response(
            chat_history,
            message,
            state,
            "CREATE TABLE needs a table name and columns.",
            source_label=SOURCE_DETERMINISTIC_SCHEMA_PARSER,
        )

    if intent_result.intent == intent_core.UNKNOWN and intent_result.reason == "unsupported_data_mutation":
        return _response_tuple(
            chat_history,
            message,
            state,
            UNSUPPORTED_MUTATION_RESPONSE,
            f"{SOURCE_STATIC_FALLBACK}. Unsupported data mutation - no model call.",
            sql_text="",
            validator=EMPTY_VALIDATOR,
        )

    if intent_result.intent in {intent_core.SMALLTALK, intent_core.UNKNOWN}:
        return _response_tuple(
            chat_history,
            message,
            state,
            FALLBACK_RESPONSE,
            f"{SOURCE_STATIC_FALLBACK}. Static fallback - no model call.",
        )

    deterministic_sql = sql_core.deterministic_sql_query(message, state.active_schema)
    if deterministic_sql:
        return _response_tuple(
            chat_history,
            message,
            state,
            f"```sql\n{deterministic_sql}\n```",
            f"{SOURCE_DETERMINISTIC_SQL_TEMPLATE}. Generated SQL with a deterministic SQL template.",
            sql_text=deterministic_sql,
            validator=sql_core.validate_sql(deterministic_sql),
        )

    ready, error = _model_ready(loaded_key)
    if not ready:
        return _empty_generation_response(
            chat_history,
            message,
            state,
            error if "inconsistent" in error else "Load a model before generating SQL.",
            source_label=SOURCE_FINE_TUNED_MODEL,
        )

    try:
        prompt = model_core.build_sql_prompt(state.active_schema, message, chat_history)
        generated_text, elapsed = _generate_model_text(prompt, model_core.SQL_GENERATION)
    except Exception as exc:
        return _empty_generation_response(
            chat_history,
            message,
            state,
            f"Generation failed: {type(exc).__name__}: {exc}",
            source_label=SOURCE_FINE_TUNED_MODEL,
        )

    sql_text, _chat_text, validator = model_core.format_generation_result(
        generated_text,
        state.active_schema,
    )
    model_def = model_by_key(loaded_key)
    if not sql_text:
        rejection_reason = model_core.model_sql_rejection_reason(generated_text, state.active_schema)
        rejection_detail = (
            f"The fine-tuned model output was rejected because {rejection_reason}."
            if rejection_reason
            else "The fine-tuned model output was rejected by SQL/schema guardrails."
        )
        return _response_tuple(
            chat_history,
            message,
            state,
            rejection_detail,
            f"{SOURCE_FINE_TUNED_MODEL}. Rejected non-SELECT/WITH model output or schema-invalid model output from {model_def['model_id']} in {elapsed}s.",
            sql_text="",
            validator=validator,
            status_kind="error",
        )

    display_response = f"```sql\n{sql_text}\n```"
    return _response_tuple(
        chat_history,
        message,
        state,
        display_response,
        f"{SOURCE_FINE_TUNED_MODEL}. Generated SQL with {model_def['model_id']} in {elapsed}s.",
        sql_text=str(sql_text),
        validator=validator,
    )


def is_sql_intent(message, schema):
    return sql_core.is_sql_intent(message, schema)


def build_generation_prompt(schema, message, chat_history=None):
    return model_core.build_sql_prompt(schema, message, chat_history)


def normalize_sql_question_to_english(message, schema=""):
    return sql_core.normalize_sql_question_to_english(message, schema)


def format_generation_result(text, schema=""):
    return model_core.format_generation_result(text, schema)


def validate_sql(sql_text, schema=""):
    return sql_core.validate_sql(sql_text, schema)


def create_table_from_message(message):
    return sql_core.create_table_from_message(message)


def create_table_from_schema(schema):
    return sql_core.create_table_from_schema(schema)


def edit_create_table_from_message(message, chat_history, active_schema):
    return sql_core.edit_create_table_from_message(message, chat_history, active_schema)


def sync_on_load():
    if _model is not None and _current_model_id is not None:
        loaded_key = model_key_by_id(_current_model_id)
        if loaded_key:
            return (
                loaded_key,
                render_status(loaded_key, loaded_key),
                render_loading_overlay(visible=False),
                model_metadata(loaded_key),
                gr.update(interactive=True, visible=True, value="Load fine-tuned model"),
                *query_control_updates(True),
                "",
                EMPTY_VALIDATOR,
                render_message(f"{SOURCE_FINE_TUNED_MODEL}. Model already loaded: {_current_model_id}", kind="ok"),
            )
    return (
        None,
        render_status(DEFAULT_MODEL_KEY, None),
        render_loading_overlay(visible=False),
        model_metadata(DEFAULT_MODEL_KEY),
        gr.update(interactive=True, visible=True),
        *query_control_updates(False),
        "",
        EMPTY_VALIDATOR,
        render_message(),
    )


CSS = """
@import url('https://fonts.googleapis.com/css2?family=Space+Mono:wght@400;500;700&display=swap');

/* Keep app contrast stable regardless of Spaces light/dark host theme. */
[class*="badge"],
[class*="validator-"],
[class*="model-tag"],
[class*="stat-card"] {
  color: inherit !important;
}

:root {
  color-scheme: dark;
  --bg-base: #0c0c0b;
  --bg-surface: #1a1a18;
  --bg-raised: #242422;
  --border: rgba(255, 255, 255, 0.1);
  --border-hi: rgba(255, 255, 255, 0.22);
  --text-hi: #f2ede4;
  --text-mid: #9e9a93;
  --text-lo: #5f5d58;
  --text-primary: var(--text-hi);
  --text-secondary: var(--text-mid);
  --text-muted: var(--text-lo);
  --teal: #1d9e75;
  --teal-soft: #dff8ef;
  --teal-text: #0f6e56;
  --amber-soft: #faeeda;
  --amber-text: #854f0b;
  --overlay-bg: rgba(0, 0, 0, 0.6);
  --chat-bubble-bot-bg: #242422;
  --chat-bubble-user-bg: #2b2822;
  --chat-pending-bg: #d9d9de;
  --chat-pending-text: #111827;
  --chat-copy-btn-bg: #1a1a18;
}

@media (prefers-color-scheme: light) {
  :root {
    color-scheme: light;
    --bg-base: #f4f6fa;
    --bg-surface: #ffffff;
    --bg-raised: #edf1f6;
    --border: rgba(17, 24, 39, 0.12);
    --border-hi: rgba(17, 24, 39, 0.24);
    --text-hi: #111827;
    --text-mid: #374151;
    --text-lo: #6b7280;
    --text-primary: var(--text-hi);
    --text-secondary: var(--text-mid);
    --text-muted: var(--text-lo);
    --teal: #0f766e;
    --teal-soft: #dff8ef;
    --teal-text: #0f5f4c;
    --amber-soft: #faeeda;
    --amber-text: #854f0b;
    --overlay-bg: rgba(17, 24, 39, 0.28);
    --chat-bubble-bot-bg: #eef3fb;
    --chat-bubble-user-bg: #f9f1df;
    --chat-pending-bg: #e8eef8;
    --chat-pending-text: #1f2937;
    --chat-copy-btn-bg: #ffffff;
  }
}

* {
  box-sizing: border-box;
}

.gradio-container,
.gradio-container .main,
.gradio-container .wrap,
.gradio-container .contain {
  background: var(--bg-base) !important;
  color: var(--text-primary) !important;
  font-family: Space Mono, ui-monospace, SFMono-Regular, Menlo, Consolas, monospace !important;
  --body-text-color: var(--text-primary) !important;
  --body-text-color-subdued: var(--text-secondary) !important;
  --block-title-text-color: var(--text-secondary) !important;
  --block-label-text-color: var(--text-secondary) !important;
  --input-placeholder-color: var(--text-muted) !important;
  --chatbot-bubble-color: var(--chat-bubble-bot-bg) !important;
  --chatbot-user-bubble-color: var(--chat-bubble-user-bg) !important;
  --chatbot-user-text-color: var(--text-primary) !important;
  --chatbot-assistant-text-color: var(--text-primary) !important;
  --chatbot-text-color: var(--text-primary) !important;
}

.top-panel h1,
.model-card h3,
.model-score span,
.evidence-copy h2,
.evidence-card strong,
.loading-title {
  color: var(--text-primary) !important;
}

.top-panel p,
.step-title,
.model-card code,
.model-score small,
.model-card-footer,
.evidence-copy p,
.evidence-card span,
.evidence-card small,
.status-pill,
.schema-context,
.field-label,
.preset-label,
.message-box {
  color: var(--text-secondary) !important;
}

.app-shell {
  max-width: 1120px;
  margin: 22px auto 44px;
  padding: 0 20px;
}

.top-panel {
  align-items: center;
  background: var(--bg-surface);
  border: 0.5px solid var(--border);
  border-radius: 6px;
  display: grid;
  gap: 16px;
  grid-template-columns: minmax(0, 1fr) auto;
  padding: 14px 16px;
}

.top-panel h1 {
  color: var(--text-primary);
  font-size: 15px;
  font-weight: 500;
  letter-spacing: 0;
  line-height: 1.25;
  margin: 0 0 4px;
}

.top-panel p {
  color: var(--text-secondary);
  font-size: 13px;
  font-weight: 400;
  letter-spacing: 0;
  line-height: 1.35;
  margin: 0;
}

.top-badges {
  align-items: center;
  display: flex;
  flex-wrap: wrap;
  gap: 8px;
  justify-content: flex-end;
}

.badge,
.validator-badge,
.model-tag {
  border-radius: 5px;
  display: inline-flex;
  font-size: 11px;
  font-weight: 500;
  letter-spacing: 0;
  line-height: 1;
  padding: 6px 8px;
}

.badge-green,
.validator-ok {
  background: var(--teal-soft);
  color: var(--teal-text) !important;
}

.badge-cream,
.validator-warn {
  background: var(--amber-soft);
  color: var(--amber-text) !important;
}

.badge-light,
.validator-empty {
  background: var(--bg-raised);
  color: var(--text-secondary) !important;
  border: 0.5px solid var(--border);
}

.step-title {
  color: var(--text-secondary);
  font-size: 11px;
  font-weight: 500;
  letter-spacing: 0.08em;
  line-height: 1;
  margin: 32px 0 12px;
  text-transform: uppercase;
}

.step-title span {
  display: inline-flex;
}

.model-grid,
.stats-row {
  display: grid;
  gap: 12px;
  grid-template-columns: repeat(2, minmax(0, 1fr));
}

.model-grid > div,
.stats-row > div {
  min-width: 0;
}

.model-card {
  background: var(--bg-surface);
  border: 0.5px solid var(--border);
  border-radius: 6px;
  min-height: 176px;
  padding: 16px;
  transition: border-color 160ms ease, background 160ms ease;
}

.model-card.selected {
  border: 1.5px solid var(--teal);
}

.model-tag {
  background: var(--amber-soft);
  color: var(--amber-text) !important;
  margin-bottom: 18px;
}

.model-card.selected .model-tag {
  background: var(--teal-soft);
  color: var(--teal-text) !important;
}

.model-card h3 {
  color: var(--text-primary);
  font-size: 15px;
  font-weight: 500;
  letter-spacing: 0;
  line-height: 1.3;
  margin: 0 0 8px;
}

.model-card code {
  color: var(--text-secondary);
  display: block;
  font-family: inherit;
  font-size: 12px;
  font-weight: 400;
  line-height: 1.35;
  margin-bottom: 18px;
  overflow: hidden;
  text-overflow: ellipsis;
  white-space: nowrap;
}

.model-score {
  align-items: baseline;
  display: flex;
  gap: 8px;
  margin-bottom: 16px;
}

.model-score span {
  color: var(--text-primary);
  font-size: 28px;
  font-weight: 500;
  letter-spacing: 0;
  line-height: 1;
}

.model-card.selected .model-score span {
  color: var(--teal) !important;
}

.model-score small,
.model-card-footer {
  color: var(--text-secondary);
  font-size: 13px;
  font-weight: 400;
}

.model-card-footer {
  display: flex;
}

.evidence-panel {
  background: var(--bg-surface);
  border: 0.5px solid var(--border);
  border-radius: 6px;
  margin-top: 12px;
  padding: 16px;
}

.evidence-copy h2 {
  color: var(--text-primary);
  font-size: 13px;
  font-weight: 500;
  line-height: 1.3;
  margin: 0 0 6px;
}

.evidence-copy p {
  color: var(--text-secondary);
  font-size: 12px;
  line-height: 1.45;
  margin: 0;
}

.evidence-grid {
  display: grid;
  gap: 8px;
  grid-template-columns: repeat(3, minmax(0, 1fr));
  margin-top: 14px;
}

.evidence-card {
  background: var(--bg-raised);
  border: 0.5px solid var(--border);
  border-radius: 6px;
  padding: 10px;
}

.evidence-card.highlighted {
  border-color: rgba(29, 158, 117, 0.5);
}

.evidence-card span,
.evidence-card small {
  color: var(--text-secondary);
  display: block;
  font-size: 10px;
  line-height: 1.25;
}

.evidence-card strong {
  color: var(--text-primary);
  display: block;
  font-size: 20px;
  font-weight: 500;
  line-height: 1.1;
  margin: 5px 0;
}

#load-button,
#generate-button {
  width: 100% !important;
}

.gradio-container button {
  border-radius: 6px !important;
  font-family: Space Mono, ui-monospace, monospace !important;
  font-size: 11px !important;
  font-weight: 500 !important;
  letter-spacing: 0 !important;
  transition: background 160ms ease, border-color 160ms ease, color 160ms ease, opacity 160ms ease !important;
}

#load-button button,
#generate-button button {
  background: var(--bg-raised) !important;
  border: 0.5px solid var(--border-hi) !important;
  color: var(--text-primary) !important;
  min-height: 40px !important;
  width: 100% !important;
}

#generate-button button {
  height: 42px !important;
  min-height: 42px !important;
}

#load-button button:hover,
#generate-button button:hover {
  background: var(--text-primary) !important;
  color: var(--bg-base) !important;
}

#generate-button button:disabled {
  opacity: 0.4 !important;
}

.status-pill {
  align-items: center;
  background: var(--bg-surface);
  border: 0.5px solid var(--border);
  border-radius: 6px;
  color: var(--text-secondary);
  display: inline-flex;
  font-size: 13px;
  font-weight: 400;
  gap: 8px;
  margin: 12px 0 0;
  padding: 8px 10px;
}

.status-pill span {
  background: var(--text-muted);
  border-radius: 50%;
  display: inline-flex;
  height: 7px;
  width: 7px;
}

.status-ready span,
.status-loading span {
  background: var(--teal);
}

.stats-row {
  grid-template-columns: repeat(4, minmax(0, 1fr));
  margin-top: 12px;
}

.stat-card {
  background: var(--bg-surface);
  border: 0.5px solid var(--border);
  border-radius: 6px;
  padding: 12px;
}

.stat-card strong {
  color: var(--text-primary) !important;
  display: block;
  font-size: 15px;
  font-weight: 500;
  line-height: 1.2;
  margin-bottom: 4px;
}

.stat-card span {
  color: var(--text-secondary) !important;
  display: block;
  font-size: 11px;
  font-weight: 400;
  line-height: 1.25;
}

.query-section {
  padding-bottom: 10px;
  scroll-margin-top: 24px;
}

.chat-history {
  background: var(--bg-surface) !important;
  border: 0.5px solid var(--border) !important;
  border-radius: 6px !important;
  margin-bottom: 8px;
}

.chat-history .bubble-wrap,
.chat-history .message,
.chat-history .prose {
  font-family: Space Mono, ui-monospace, monospace !important;
  font-size: 13px !important;
  line-height: 1.45 !important;
  color: var(--text-primary) !important;
}

.chat-history .message,
.chat-history .bubble-wrap .message {
  background: var(--chat-bubble-bot-bg) !important;
  border: 0.5px solid var(--border) !important;
}

.chat-history [data-testid*="user"] .message,
.chat-history [class*="user"] .message {
  background: var(--chat-bubble-user-bg) !important;
}

.chat-history .message *,
.chat-history .prose * {
  color: var(--text-primary) !important;
}

.chat-history .message-row {
  padding: 6px 8px !important;
}

.chat-history pre,
.chat-history code {
  font-family: Space Mono, ui-monospace, monospace !important;
  font-size: 12px !important;
  color: var(--text-primary) !important;
}

.chat-history [class*="pending"],
.chat-history [class*="loading"],
.chat-history [class*="typing"],
.chat-history [class*="spinner"],
.chat-history [class*="generating"] {
  background: var(--chat-pending-bg) !important;
  border-color: var(--border) !important;
  color: var(--chat-pending-text) !important;
}

.chat-history [class*="pending"] *,
.chat-history [class*="loading"] *,
.chat-history [class*="typing"] *,
.chat-history [class*="spinner"] *,
.chat-history [class*="generating"] * {
  color: var(--chat-pending-text) !important;
}

.chat-history button,
.chat-history [role="button"] {
  background: var(--chat-copy-btn-bg) !important;
  border: 0.5px solid var(--border) !important;
  color: var(--text-secondary) !important;
}

.chat-history button:hover,
.chat-history [role="button"]:hover {
  border-color: var(--border-hi) !important;
  color: var(--text-primary) !important;
}

.schema-context-row {
  align-items: center;
  gap: 6px !important;
  margin: 2px 0 4px;
}

.schema-context {
  align-items: center;
  background: var(--schema-context-bg, var(--bg-surface));
  border: 0.5px solid var(--border);
  border-radius: 6px;
  color: var(--text-secondary);
  display: inline-flex;
  gap: 8px;
  min-height: 28px;
  padding: 5px 10px;
  max-width: 100%;
}

.schema-context.empty {
  background: transparent;
  border-style: dashed;
  color: var(--text-muted);
  min-height: 24px;
  padding: 4px 8px;
}

.schema-context.empty span {
  color: var(--text-muted) !important;
  font-size: 11px;
  flex-shrink: 0;
}

.schema-context.empty code {
  color: var(--text-muted);
  font-size: 10px;
  font-style: italic;
  overflow: hidden;
  text-overflow: ellipsis;
  white-space: nowrap;
}

.schema-context span {
  color: var(--teal) !important;
  font-size: 11px;
  font-weight: 600;
  white-space: nowrap;
  flex-shrink: 0;
}

.schema-context code {
  background: var(--code-bg);
  border: 0.5px solid var(--code-border);
  border-radius: 4px;
  color: var(--code-text);
  display: block;
  font-family: var(--font-code, "JetBrains Mono", ui-monospace, monospace);
  font-size: 10px;
  line-height: 1.4;
  overflow: hidden;
  padding: 3px 6px;
  text-overflow: ellipsis;
  white-space: nowrap;
  min-width: 0;
}

.schema-strip {
  align-items: center !important;
  flex-wrap: wrap !important;
  gap: 4px !important;
  margin: 4px 0 0 !important;
  padding: 0 !important;
}

.schema-strip > div {
  flex: 0 0 auto !important;
  min-width: 0 !important;
}

.schema-strip button {
  background: var(--bg-raised) !important;
  border: 0.5px solid var(--border) !important;
  border-radius: 999px !important;
  color: var(--text-secondary) !important;
  font-size: 10px !important;
  min-height: 22px !important;
  padding: 0 8px !important;
}

.schema-strip button:hover {
  border-color: var(--border-hi) !important;
  color: var(--text-primary) !important;
}

.schema-strip-label {
  color: var(--text-muted);
  font-size: 10px;
  font-weight: 500;
  letter-spacing: 0.06em;
  text-transform: uppercase;
  white-space: nowrap;
}

.composer-row {
  align-items: flex-end !important;
  display: flex !important;
  gap: 6px !important;
  padding-bottom: 12px !important;
}

.composer-row > div {
  display: flex !important;
  flex-direction: column !important;
  justify-content: flex-end !important;
}

#message-input {
  flex: 1 1 auto;
}

#message-input textarea {
  min-height: 42px !important;
  max-height: 120px !important;
  height: 42px !important;
  transform: translateY(10px) !important;
  resize: none !important;
  overflow-y: auto !important;
}

#generate-button {
  align-self: flex-end !important;
  height: 42px !important;
  margin-bottom: 0 !important;
  margin-top: 0 !important;
  min-height: 42px !important;
}

#generate-button button {
  height: 42px !important;
  min-height: 42px !important;
  margin-bottom: 0 !important;
}

#clear-schema-button button {
  background: transparent !important;
  border: 0.5px solid var(--border) !important;
  color: var(--text-secondary) !important;
  min-height: 30px !important;
  min-width: 34px !important;
  width: 34px !important;
}

#clear-schema-button button:hover {
  border-color: var(--border-hi) !important;
  color: var(--text-primary) !important;
}

.preset-row-gradio {
  gap: 6px !important;
  margin-bottom: 6px;
}

.preset-row-gradio button {
  background: var(--bg-raised) !important;
  border: 0.5px solid var(--border) !important;
  border-radius: 999px !important;
  color: var(--text-secondary) !important;
  font-size: 11px !important;
  min-height: 26px !important;
  padding: 0 10px !important;
}

/* Queue/processing indicators aligned to the dark UI. */
.gradio-container [data-testid="status-tracker"],
.gradio-container [class*="status-tracker"] {
  background: var(--bg-raised) !important;
  border: 0.5px solid var(--border) !important;
  border-radius: 6px !important;
  color: var(--text-secondary) !important;
  font-size: 10px !important;
  letter-spacing: 0.08em;
  padding: 4px 8px !important;
  text-transform: uppercase;
}

.gradio-container [data-testid="status-tracker"] * {
  color: var(--text-secondary) !important;
}

.gradio-container [class*="processing"],
.gradio-container [class*="queue"],
.gradio-container [class*="progress"] {
  color: var(--text-secondary) !important;
}

.gradio-container [class*="spinner"],
.gradio-container [class*="spinner"] * {
  color: var(--teal) !important;
}

/* SVG loading spinners - override Gradio default fill/stroke */
.gradio-container svg[class*="spinner"],
.gradio-container svg[class*="loading"],
.gradio-container [data-testid="loader"] svg,
.gradio-container [class*="loader"] svg {
  stroke: var(--teal) !important;
  fill: none !important;
}

.gradio-container svg[class*="spinner"] circle,
.gradio-container svg[class*="spinner"] path,
.gradio-container [data-testid="loader"] circle,
.gradio-container [data-testid="loader"] path {
  stroke: var(--teal) !important;
  fill: none !important;
}

/* Hide default Gradio loading animation and replace with styled container */
.gradio-container [data-testid="loader"],
.gradio-container [class*="loader"] {
  opacity: 1 !important;
  background: transparent !important;
}

/* Hide skeleton/shimmer animations that don't match dark theme */
.gradio-container [class*="skeleton"],
.gradio-container [class*="shimmer"],
.gradio-container [class*="pulse"] {
  background: var(--bg-raised) !important;
  animation: none !important;
}

.preset-row-gradio button:hover {
  border-color: var(--border-hi) !important;
  color: var(--text-primary) !important;
}

.gradio-container .block,
.gradio-container .form,
.gradio-container .panel {
  background: transparent !important;
  border: 0 !important;
}

.gradio-container label,
.gradio-container .label-wrap span {
  color: var(--text-secondary) !important;
  font-family: Space Mono, ui-monospace, monospace !important;
  font-size: 11px !important;
  font-weight: 500 !important;
}

textarea,
input,
.cm-editor {
  background: var(--bg-raised) !important;
  border: 0.5px solid var(--border) !important;
  border-radius: 6px !important;
  color: var(--text-primary) !important;
  font-family: Space Mono, ui-monospace, monospace !important;
  font-size: 13px !important;
  font-weight: 400 !important;
  line-height: 1.45 !important;
}

textarea::placeholder,
input::placeholder {
  color: var(--text-muted) !important;
}

textarea {
  min-height: 132px !important;
}

.section-divider {
  border-top: 0.5px solid var(--border);
  margin: 28px 0 0;
}

.output-shell {
  background: var(--bg-surface);
  border: 0.5px solid var(--border);
  border-radius: 6px;
  margin-top: 12px;
  overflow: hidden;
}

.output-head {
  align-items: center;
  background: var(--bg-surface);
  border-bottom: 0.5px solid var(--border);
  display: flex;
  justify-content: space-between;
  min-height: 34px;
  padding: 0 12px;
}

.output-head span:first-child {
  color: var(--text-secondary);
  font-size: 11px;
  font-weight: 500;
  letter-spacing: 0.08em;
  text-transform: uppercase;
}

.validator-detail {
  color: var(--text-secondary) !important;
  font-size: 11px;
  margin-left: 8px;
}

.output-shell .cm-editor,
.output-shell pre,
.output-shell code {
  border: 0 !important;
  font-size: 12px !important;
  font-weight: 400 !important;
}

.message-box {
  color: var(--text-secondary);
  font-size: 11px;
  font-weight: 400;
  min-height: 24px;
  padding-top: 8px;
}

.message-error {
  color: var(--amber-text);
}

.message-ok {
  color: var(--teal);
}

.loading-overlay {
  align-items: center;
  background: var(--overlay-bg);
  bottom: 0;
  display: flex;
  justify-content: center;
  left: 0;
  position: fixed;
  right: 0;
  top: 0;
  z-index: 1000;
}

.loading-overlay.hidden {
  display: none;
}

.loading-card {
  background: var(--bg-surface);
  border: 0.5px solid var(--border-hi);
  border-radius: 6px;
  max-width: 480px;
  padding: 18px;
  width: min(90vw, 480px);
}

.loading-title {
  color: var(--text-primary);
  font-size: 13px;
  font-weight: 500;
  line-height: 1.35;
  margin-bottom: 12px;
}

.loading-line {
  background: var(--bg-raised);
  border-radius: 999px;
  height: 8px;
  overflow: hidden;
}

.loading-line span {
  animation: loadingPulse 1.3s infinite ease-in-out;
  background: var(--teal);
  border-radius: inherit;
  display: block;
  height: 100%;
  width: 45%;
}

.loading-card p {
  color: var(--text-secondary);
  font-size: 11px;
  font-weight: 400;
  line-height: 1.4;
  margin: 12px 0 0;
}

@keyframes loadingPulse {
  0% { transform: translateX(-70%); }
  50% { transform: translateX(70%); }
  100% { transform: translateX(220%); }
}

@media (max-width: 860px) {
  .top-panel,
  .model-grid,
  .evidence-grid {
    grid-template-columns: 1fr;
  }

  .stats-row {
    grid-template-columns: repeat(2, minmax(0, 1fr));
  }

  .top-badges {
    justify-content: flex-start;
  }

  .app-shell {
    padding: 0 14px;
  }
}

/* Finalized Lab Workbench direction: dark-first technical UI, source contract always visible. */
@import url('https://fonts.googleapis.com/css2?family=IBM+Plex+Sans:wght@400;500;600;700&family=JetBrains+Mono:wght@400;500;600&display=swap');

:root {
  color-scheme: dark;
  --bg-base: #101214;
  --bg-surface: #1b2027;
  --bg-raised: #242b35;
  --border: #2d3642;
  --border-hi: #3c4654;
  --text-hi: #edf1f5;
  --text-mid: #a8b3c1;
  --text-lo: #8491a2;
  --text-primary: var(--text-hi);
  --text-secondary: var(--text-mid);
  --text-muted: var(--text-lo);
  --teal: #1d9e75;
  --teal-soft: #153f34;
  --teal-text: #87e8c5;
  --amber-soft: #473111;
  --amber-text: #f0c878;
  --overlay-bg: rgba(0, 0, 0, 0.55);
  --chat-bubble-bot-bg: #242b35;
  --chat-bubble-user-bg: #1c252f;
  --chat-pending-bg: #27313d;
  --chat-pending-text: #edf1f5;
  --chat-copy-btn-bg: #1b2027;
  --code-bg: #0b1020;
  --code-border: #26344d;
  --code-text: #d7e3f3;
  --schema-context-bg: #10251f;
  --disabled-bg: #536171;
  --font-ui: "IBM Plex Sans", ui-sans-serif, sans-serif;
  --font-code: "JetBrains Mono", ui-monospace, monospace;
}

@media (prefers-color-scheme: light) {
  :root {
    color-scheme: light;
    --bg-base: #edf2f7;
    --bg-surface: #ffffff;
    --bg-raised: #f4f7fb;
    --border: #d8e0ea;
    --border-hi: #c5cfdd;
    --text-hi: #172033;
    --text-mid: #667085;
    --text-lo: #8a95a6;
    --teal: #0d8b67;
    --teal-soft: #dff6ec;
    --teal-text: #076348;
    --amber-soft: #fff3d8;
    --amber-text: #7a4700;
    --overlay-bg: rgba(23, 32, 51, 0.28);
    --chat-bubble-bot-bg: #f4f7fb;
    --chat-bubble-user-bg: #edf6ff;
    --chat-pending-bg: #e8eef8;
    --chat-pending-text: #172033;
    --chat-copy-btn-bg: #ffffff;
    --code-bg: #101828;
    --code-border: #25344d;
    --schema-context-bg: #f3fbf8;
    --disabled-bg: #b7c3cf;
  }
}

.gradio-container,
.gradio-container .main,
.gradio-container .wrap,
.gradio-container .contain {
  background: var(--bg-base) !important;
  font-family: var(--font-ui) !important;
}

.app-shell {
  max-width: 1240px;
  padding: 0 24px;
}

.header-wrapper {
  border: 0 !important;
  padding: 0 !important;
}

.theme-style-wrapper {
  display: none !important;
}

.html-container:has(.top-panel) {
  padding: 0 !important;
}

.top-panel,
.model-card,
.stats-row,
.source-panel,
.example-panel,
.evidence-panel,
.chat-history,
.output-shell {
  background: var(--bg-surface) !important;
  border: 1px solid var(--border) !important;
  border-radius: 8px !important;
  box-shadow: none !important;
}

.top-panel {
  padding: 16px !important;
}

.top-panel h1 {
  font-family: var(--font-ui) !important;
  font-size: 20px !important;
  font-weight: 700 !important;
  letter-spacing: 0 !important;
}

.top-panel p {
  font-family: var(--font-ui) !important;
  font-size: 15px !important;
  line-height: 1.45 !important;
  max-width: 720px;
}

.badge,
.validator-badge,
.model-tag,
.status-pill,
.schema-context code,
.preset-row-gradio button,
.output-head span:first-child,
.panel-heading span,
.source-row strong,
.example-group code {
  font-family: var(--font-code) !important;
}

.badge,
.validator-badge,
.model-tag {
  border-radius: 6px !important;
  font-size: 12px !important;
  font-weight: 600 !important;
  min-height: 28px !important;
  padding: 7px 9px !important;
}

.workbench-grid {
  align-items: start !important;
  display: grid !important;
  gap: 16px !important;
  grid-template-columns: minmax(0, 1.6fr) 320px !important;
  margin-top: 16px !important;
}

.main-stack,
.context-rail {
  display: grid !important;
  gap: 10px !important;
}

.context-rail {
  align-content: start !important;
}

.model-side-panel {
  gap: 6px !important;
}

.source-panel {
  position: sticky;
  top: 16px;
  z-index: 2;
}

.mobile-source-panel {
  display: none !important;
}

.mobile-source-wrapper {
  display: none !important;
}

.panel-heading {
  align-items: center;
  border-bottom: 1px solid var(--border);
  display: flex;
  gap: 12px;
  justify-content: space-between;
  min-height: 36px;
  padding: 0 12px;
}

.panel-heading h2 {
  color: var(--text-primary) !important;
  flex: 0 0 auto;
  font-size: 12px;
  font-weight: 700;
  letter-spacing: 0.06em;
  line-height: 1;
  margin: 0;
  text-transform: uppercase;
  white-space: nowrap;
}

.panel-heading span {
  color: var(--text-secondary) !important;
  font-size: 10px;
  min-width: 0;
  overflow: hidden;
  text-align: right;
  text-overflow: ellipsis;
  white-space: nowrap;
}

.source-list,
.example-list {
  display: grid;
  gap: 6px;
  padding: 10px 12px 12px;
}

.example-list {
  grid-template-columns: repeat(2, minmax(0, 1fr));
}

.source-row {
  border-bottom: 1px solid var(--border);
  display: grid;
  gap: 3px;
  padding-bottom: 6px;
}

.source-row:last-child {
  border-bottom: 0;
  padding-bottom: 0;
}

.source-row strong {
  color: var(--text-primary) !important;
  font-size: 11px;
}

.source-row span,
.example-group span,
.evidence-copy p {
  color: var(--text-secondary) !important;
  font-size: 11px !important;
  line-height: 1.3 !important;
}

.example-group {
  display: grid;
  gap: 5px;
}

.example-group code {
  background: var(--bg-raised);
  border: 1px solid var(--border);
  border-radius: 6px;
  color: var(--text-primary) !important;
  font-size: 11px;
  line-height: 1.35;
  padding: 7px 8px;
  white-space: normal;
}

.model-card {
  min-height: auto !important;
  padding: 12px !important;
}

.model-card code {
  font-family: var(--font-code) !important;
  white-space: normal !important;
}

.model-score span {
  color: var(--teal) !important;
  font-size: 24px !important;
}

.stats-row {
  gap: 8px !important;
  grid-template-columns: repeat(2, minmax(0, 1fr)) !important;
  padding: 12px !important;
}

.stat-card {
  background: var(--bg-raised) !important;
  border: 1px solid var(--border) !important;
  padding: 10px !important;
}

#load-button,
#generate-button {
  --button-primary-background-fill: var(--teal) !important;
  --button-primary-background-fill-hover: #076348 !important;
  --button-primary-text-color: #ffffff !important;
  background: var(--teal) !important;
  background-color: var(--teal) !important;
  background-image: none !important;
  border: 0 !important;
  color: var(--bg-surface) !important;
  font-family: var(--font-ui) !important;
  font-size: 13px !important;
  font-weight: 700 !important;
  min-height: 44px !important;
}

#generate-button {
  background: var(--teal) !important;
  color: #ffffff !important;
}

.composer-row #generate-button {
  height: 42px !important;
  margin-top: 0 !important;
  min-height: 42px !important;
}

.composer-row #generate-button button {
  height: 42px !important;
  min-height: 42px !important;
}

#load-button:hover,
#generate-button:hover {
  opacity: 0.88 !important;
}

.status-pill {
  align-items: center !important;
  background: var(--bg-surface) !important;
  border: 1px solid var(--border) !important;
  border-radius: 8px !important;
  display: flex !important;
  font-family: var(--font-ui) !important;
  font-size: 13px !important;
  line-height: 1.4 !important;
  margin: 0 !important;
  min-height: 44px !important;
  padding: 10px 12px !important;
  width: 100%;
}

.model-side-panel {
  display: grid !important;
  gap: 12px !important;
}

.query-section {
  background: var(--bg-surface);
  border: 1px solid var(--border);
  border-radius: 8px;
  overflow: hidden;
  scroll-margin-top: 24px;
}

.chat-section-heading {
  border-bottom: 1px solid var(--border);
}

.chat-history {
  border: 0 !important;
  border-radius: 0 !important;
  margin-bottom: 0 !important;
}

.chat-history .bubble-wrap,
.chat-history .message,
.chat-history .prose {
  font-family: var(--font-ui) !important;
  font-size: 15px !important;
  line-height: 1.5 !important;
}

.chat-history .message,
.chat-history .bubble-wrap .message {
  background: var(--chat-bubble-bot-bg) !important;
  border: 1px solid var(--border) !important;
  border-radius: 8px !important;
}

.chat-history [data-testid*="user"] .message,
.chat-history [class*="user"] .message {
  background: var(--chat-bubble-user-bg) !important;
}

.field-label {
  border-top: 1px solid var(--border);
  color: var(--text-secondary) !important;
  font-family: var(--font-ui) !important;
  font-size: 13px !important;
  letter-spacing: 0 !important;
  margin: 0 !important;
  padding: 6px 16px 0;
  text-transform: none !important;
}

.preset-row-gradio {
  border-bottom: 1px solid var(--border);
  gap: 6px !important;
  margin: 0 !important;
  padding: 6px 16px !important;
}

.preset-row-gradio button {
  background: var(--bg-raised) !important;
  border: 1px solid var(--border) !important;
  color: var(--text-secondary) !important;
  min-height: 28px !important;
  padding: 2px 10px !important;
  font-size: 12px !important;
}

.schema-pill-container {
  flex: 1 1 auto !important;
  min-width: 0 !important;
  overflow: hidden !important;
}

.schema-context-row {
  background: var(--schema-context-bg);
  border-bottom: 1px solid var(--border);
  margin: 0 !important;
  padding: 6px 16px !important;
}


#clear-schema-button {
  flex: 0 0 auto !important;
  min-width: 0 !important;
}

.schema-context-row:has(.schema-context.empty) #clear-schema-button {
  display: none !important;
}

#clear-schema-button button {
  min-height: 28px !important;
  padding: 0 12px !important;
  font-size: 12px !important;
}

.composer-row {
  border-top: 1px solid var(--border);
  gap: 6px !important;
  padding: 8px 16px 14px !important;
}

.gradio-container label,
.gradio-container .label-wrap span {
  font-family: var(--font-ui) !important;
}

textarea,
input,
.cm-editor {
  background: var(--bg-surface) !important;
  border: 1px solid var(--border-hi) !important;
  border-radius: 6px !important;
  color: var(--text-primary) !important;
  font-family: var(--font-ui) !important;
  font-size: 14px !important;
}

.output-shell {
  background: var(--code-bg) !important;
  border-color: var(--code-border) !important;
  margin-top: 0 !important;
}

.output-head {
  background: var(--code-bg) !important;
  border-bottom: 1px solid var(--code-border) !important;
  min-height: 44px !important;
  padding: 0 16px !important;
}

.output-head span:first-child,
.output-head .validator-detail {
  color: #c3cfdd !important;
}

.output-shell .cm-editor,
.output-shell pre,
.output-shell code {
  background: var(--code-bg) !important;
  color: var(--code-text) !important;
  font-family: var(--font-code) !important;
  font-size: 13px !important;
  line-height: 1.6 !important;
}

.message-box {
  font-family: var(--font-ui) !important;
  font-size: 13px !important;
  padding-top: 0 !important;
}

.message-output-wrapper:has(.message-empty) {
  display: none !important;
}

.evidence-panel {
  margin-top: 0 !important;
  padding: 0 !important;
}

.evidence-copy {
  padding: 14px 16px 0;
}

.evidence-grid {
  grid-template-columns: repeat(3, minmax(0, 1fr)) !important;
  margin-top: 0 !important;
  padding: 14px 16px 16px;
}

.evidence-card {
  background: var(--bg-raised) !important;
  border: 1px solid var(--border) !important;
}

@media (max-width: 1024px) {
  .workbench-grid {
    grid-template-columns: 1fr !important;
  }

  .source-panel {
    position: static;
  }

  .mobile-source-panel {
    display: block !important;
  }

  .mobile-source-wrapper {
    display: block !important;
    margin-top: 16px !important;
  }

  .context-rail .source-panel {
    display: none !important;
  }
}

@media (max-width: 768px) {
  .app-shell {
    padding: 0 16px;
  }

  .top-panel {
    grid-template-columns: 1fr !important;
  }

  .top-badges {
    justify-content: flex-start !important;
  }

  .composer-row {
    align-items: stretch !important;
    flex-direction: column !important;
  }

  #generate-button {
    align-self: stretch !important;
    margin-top: 0 !important;
    transform: none;
  }

  #message-input textarea {
    transform: none !important;
  }

  .evidence-grid {
    grid-template-columns: 1fr !important;
  }
}

.metadata-panel {
  background: var(--bg-surface) !important;
  border: 1px solid var(--border) !important;
  border-radius: 8px !important;
  box-shadow: none !important;
}

.metadata-body {
  display: grid;
  gap: 8px;
  padding: 10px 12px 12px;
}

.metadata-source-list {
  gap: 6px !important;
  padding: 0 !important;
}

.metadata-source-list .source-row {
  padding-bottom: 6px;
}

.metadata-source-list .source-row strong {
  font-size: 11px;
}

.metadata-source-list .source-row span {
  font-size: 11px !important;
  line-height: 1.3 !important;
}

.stats-row-compact {
  gap: 5px !important;
  grid-template-columns: repeat(2, minmax(0, 1fr)) !important;
  margin-top: 0 !important;
  padding: 0 !important;
}

.stats-row-compact .stat-card {
  min-height: 58px;
  padding: 7px !important;
}

.stats-row-compact .stat-card strong {
  display: block;
  font-size: 12px !important;
  line-height: 1.15;
  margin-bottom: 4px;
}

.stats-row-compact .stat-card span {
  display: block;
  font-size: 9px !important;
  line-height: 1.25 !important;
}

.model-card {
  min-height: auto !important;
  padding: 12px !important;
}

.model-card code {
  margin-bottom: 12px !important;
}

.model-score {
  margin-bottom: 10px !important;
}

.model-card-footer {
  margin-bottom: 10px;
}

.model-card-evidence {
  border-top: 1px solid var(--border);
  display: grid;
  gap: 6px;
  margin-top: 8px;
  padding-top: 8px;
}

.model-card-evidence-heading {
  color: var(--text-primary) !important;
  font-size: 11px;
  font-weight: 700;
  letter-spacing: 0.04em;
  text-transform: uppercase;
}

.model-card-evidence-grid {
  display: grid;
  gap: 4px;
  grid-template-columns: repeat(3, minmax(0, 1fr));
}

.model-card-evidence-chip {
  background: var(--bg-raised) !important;
  border: 1px solid var(--border) !important;
  border-radius: 6px;
  padding: 7px;
}

.model-card-evidence-chip.highlighted {
  border-color: rgba(29, 158, 117, 0.5);
}

.model-card-evidence-chip strong {
  color: var(--text-primary) !important;
  display: block;
  font-size: 12px;
  line-height: 1.15;
  margin-bottom: 3px;
}

.model-card-evidence-chip small {
  color: var(--text-secondary) !important;
  display: block;
  font-size: 9px;
  line-height: 1.15;
}

.context-rail {
  gap: 10px !important;
}

.model-side-panel {
  gap: 6px !important;
}

.panel-heading {
  min-height: 36px;
  padding: 0 12px;
}

.panel-heading h2 {
  font-size: 12px;
}

.panel-heading span {
  font-size: 10px;
}

.example-panel .example-list {
  grid-template-columns: 1fr;
  padding: 10px 12px 12px;
}

.example-group code {
  padding: 7px 8px;
}

/* Late alignment overrides for Gradio wrapper defaults. */
.gradio-container .header-wrapper {
  padding: 0 !important;
}

.gradio-container #load-button,
.gradio-container #generate-button {
  --button-primary-background-fill: var(--teal) !important;
  --button-primary-background-fill-hover: #076348 !important;
  --button-primary-text-color: #ffffff !important;
  background: var(--teal) !important;
  background-color: var(--teal) !important;
  background-image: none !important;
  box-shadow: none !important;
  color: #ffffff !important;
}

.gradio-container #generate-button:disabled {
  background: var(--disabled-bg) !important;
  color: #ffffff !important;
  opacity: 1 !important;
}

.gradio-container .message-output-wrapper:has(.message-empty) {
  display: none !important;
}

/* Keep dark as the fallback default; only explicit light devices get the light workbench tokens. */
@media (prefers-color-scheme: light) {
  :root,
  .gradio-container,
  * {
    color-scheme: light;
    --bg-base: #edf2f7;
    --bg-surface: #ffffff;
    --bg-raised: #f4f7fb;
    --border: #d8e0ea;
    --border-hi: #c5cfdd;
    --text-hi: #172033;
    --text-mid: #667085;
    --text-lo: #8a95a6;
    --text-primary: var(--text-hi);
    --text-secondary: var(--text-mid);
    --text-muted: var(--text-lo);
    --teal: #0d8b67;
    --teal-soft: #dff6ec;
    --teal-text: #076348;
    --amber-soft: #fff3d8;
    --amber-text: #7a4700;
    --overlay-bg: rgba(23, 32, 51, 0.28);
    --chat-bubble-bot-bg: #f4f7fb;
    --chat-bubble-user-bg: #edf6ff;
    --chat-pending-bg: #e8eef8;
    --chat-pending-text: #172033;
    --chat-copy-btn-bg: #ffffff;
    --code-bg: #101828;
    --code-border: #25344d;
    --code-text: #d7e3f3;
    --schema-context-bg: #f3fbf8;
    --disabled-bg: #b7c3cf;
  }
}
"""

with gr.Blocks(title="Phi-3 Mini SQL Chatbot") as demo:
    loaded_key_state = gr.State(value=None)
    active_schema = gr.State(value="")
    conversation_state = gr.State(value=chat_core.default_state())
    last_user_message = gr.State(value="")

    with gr.Column(elem_classes=["app-shell"]):
        loading_overlay = gr.HTML(render_loading_overlay(visible=False))
        gr.HTML(render_theme_detection_style(), elem_classes=["theme-style-wrapper"])
        gr.HTML(render_header(), elem_classes=["header-wrapper"])
        gr.HTML(render_source_legend("mobile-source-panel"), elem_classes=["mobile-source-wrapper"])

        with gr.Row(elem_classes=["workbench-grid"]):
            with gr.Column(elem_classes=["main-stack"]):
                with gr.Column(elem_id="query-section", elem_classes=["query-section"]):
                    gr.HTML(
                        '<div class="panel-heading chat-section-heading">'
                        '<h2>Chat workbench</h2><span>schema + prompt</span></div>'
                    )
                    chatbot_kwargs = {
                        "label": "",
                        "height": 360,
                        "show_label": False,
                        "elem_classes": ["chat-history"],
                    }
                    if "type" in inspect.signature(gr.Chatbot).parameters:
                        chatbot_kwargs["type"] = "messages"
                    chatbot = gr.Chatbot(**chatbot_kwargs)
                    with gr.Row(elem_classes=["schema-strip"]):
                        gr.HTML('<span class="schema-strip-label">Schema</span>')
                        employees_preset = gr.Button("employees", size="sm")
                        orders_preset = gr.Button("orders", size="sm")
                        students_preset = gr.Button("students", size="sm")
                        products_preset = gr.Button("products", size="sm")
                        sales_preset = gr.Button("sales", size="sm")
                    with gr.Row(elem_classes=["schema-context-row"]):
                        active_schema_pill = gr.HTML(render_schema_context(""), elem_classes=["schema-pill-container"])
                        clear_schema_button = gr.Button("Clear", size="sm", elem_id="clear-schema-button", scale=0)
                    with gr.Row(elem_classes=["composer-row"]):
                        message_input = gr.Textbox(
                            label="Message",
                            value="",
                            placeholder="Ask a SQL question against the active schema",
                            lines=1,
                            max_lines=5,
                            interactive=True,
                            show_label=True,
                            elem_id="message-input",
                        )
                        send_button = gr.Button(
                            "Send",
                            variant="primary",
                            interactive=False,
                            elem_id="generate-button",
                        )

                with gr.Column(elem_classes=["output-shell"]):
                    with gr.Row(elem_classes=["output-head"]):
                        gr.HTML("<span>SQL artifact</span>")
                        validator_output = gr.HTML(validate_sql(""))
                    sql_output = gr.Code(
                        label="",
                        language="sql",
                        lines=7,
                        interactive=False,
                        show_label=False,
                    )
                error_output = gr.HTML(render_message(), elem_classes=["message-output-wrapper"])

            with gr.Column(elem_classes=["context-rail"]):
                fine_tuned_model_card = gr.HTML(render_model_card(FINE_TUNED_MODEL_KEY, DEFAULT_MODEL_KEY))
                with gr.Column(elem_classes=["model-side-panel"]):
                    load_button = gr.Button("Load fine-tuned model", variant="primary", elem_id="load-button")
                    model_status = gr.HTML(render_status(DEFAULT_MODEL_KEY, None))
                model_info = gr.HTML(model_metadata(DEFAULT_MODEL_KEY))
                gr.HTML(render_example_prompts())

    model_state_outputs = [
        fine_tuned_model_card,
        model_status,
        model_info,
        employees_preset,
        orders_preset,
        students_preset,
        products_preset,
        sales_preset,
        clear_schema_button,
        message_input,
        send_button,
        error_output,
    ]

    load_button.click(
        load_selected_model,
        inputs=None,
        outputs=[
            loaded_key_state,
            model_status,
            loading_overlay,
            model_info,
            load_button,
            employees_preset,
            orders_preset,
            students_preset,
            products_preset,
            sales_preset,
            clear_schema_button,
            message_input,
            send_button,
            sql_output,
            validator_output,
            error_output,
        ],
        js=LOAD_SCROLL_JS,
    )

    schema_context_outputs = [active_schema, active_schema_pill, clear_schema_button, conversation_state]
    employees_preset.click(set_preset, inputs=gr.State("employees"), outputs=schema_context_outputs)
    orders_preset.click(set_preset, inputs=gr.State("orders"), outputs=schema_context_outputs)
    students_preset.click(set_preset, inputs=gr.State("students"), outputs=schema_context_outputs)
    products_preset.click(set_preset, inputs=gr.State("products"), outputs=schema_context_outputs)
    sales_preset.click(set_preset, inputs=gr.State("sales"), outputs=schema_context_outputs)
    clear_schema_button.click(clear_schema_context, outputs=schema_context_outputs)

    chat_generation_outputs = [
        chatbot,
        message_input,
        active_schema,
        last_user_message,
        sql_output,
        validator_output,
        error_output,
        conversation_state,
        active_schema_pill,
        clear_schema_button,
    ]
    send_button.click(
        generate_response,
        inputs=[message_input, chatbot, active_schema, loaded_key_state, conversation_state],
        outputs=chat_generation_outputs,
    )
    message_input.submit(
        generate_response,
        inputs=[message_input, chatbot, active_schema, loaded_key_state, conversation_state],
        outputs=chat_generation_outputs,
    )
    demo.load(
        sync_on_load,
        outputs=[
            loaded_key_state,
            model_status,
            loading_overlay,
            model_info,
            load_button,
            employees_preset,
            orders_preset,
            students_preset,
            products_preset,
            sales_preset,
            clear_schema_button,
            message_input,
            send_button,
            sql_output,
            validator_output,
            error_output,
        ],
    )

queue_kwargs = {}
if "default_concurrency_limit" in inspect.signature(demo.queue).parameters:
    queue_kwargs["default_concurrency_limit"] = 1
demo.queue(**queue_kwargs)


if __name__ == "__main__":
    demo.launch(css=CSS)