import base64
import json
import logging
import mimetypes
import os
import re
import threading
import unicodedata
from pathlib import Path

import requests
import streamlit as st
import streamlit.components.v1 as components
from streamlit.errors import StreamlitSecretNotFoundError


def _load_secrets() -> None:
    try:
        secrets = st.secrets.to_dict()
    except StreamlitSecretNotFoundError:
        return
    except Exception:
        return
    for key in (
        "BLABLADOR_API_KEY",
        "BLABLADOR_BASE_URL",
        "OPENAI_API_KEY",
        "OPENAI_BASE_URL",
        "OPENAI_API_BASE",
    ):
        if key in secrets and not os.getenv(key):
            os.environ[key] = str(secrets[key])


_load_secrets()

DEFAULT_BLABLADOR_BASE_URL = "https://api.helmholtz-blablador.fz-juelich.de/v1/"

# If the user provides only BLABLADOR_API_KEY, default to the Blablador endpoint.
if (
    os.getenv("BLABLADOR_API_KEY")
    and not os.getenv("BLABLADOR_BASE_URL")
    and not os.getenv("OPENAI_BASE_URL")
    and not os.getenv("OPENAI_API_BASE")
):
    os.environ["BLABLADOR_BASE_URL"] = DEFAULT_BLABLADOR_BASE_URL

if not os.getenv("OPENAI_API_KEY") and os.getenv("BLABLADOR_API_KEY"):
    os.environ["OPENAI_API_KEY"] = os.environ["BLABLADOR_API_KEY"]
if not os.getenv("OPENAI_BASE_URL") and os.getenv("BLABLADOR_BASE_URL"):
    os.environ["OPENAI_BASE_URL"] = os.environ["BLABLADOR_BASE_URL"]
    os.environ["OPENAI_API_BASE"] = os.environ["BLABLADOR_BASE_URL"]

if not os.getenv("OPENAI_API_KEY"):
    st.error(
        "Missing BLABLADOR_API_KEY / OPENAI_API_KEY. "
        "Set it in `.streamlit/secrets.toml` or export it before running."
    )
    st.stop()

os.environ.setdefault("DB_NAME", "test_db")
os.environ.setdefault("KG_DB_URL", "ws://localhost:8000/rpc")
os.environ.setdefault("KG_SEARCH_THRESHOLD", "0.15")
os.environ.setdefault("KG_SEARCH_FALLBACK", "true")
os.environ.setdefault("KG_EMBEDDINGS_PROVIDER", "sentence-transformers")
os.environ.setdefault(
    "KG_LOCAL_EMBEDDINGS_MODEL", "sentence-transformers/all-MiniLM-L6-v2"
)
os.environ.setdefault("KG_DOCLING_TOKENIZER", "cl100k_base")

# Prefer fast PDF extraction (no OCR) for constrained environments.
os.environ.setdefault("KG_PDF_CONVERTER", "kreuzberg")
os.environ.setdefault("KG_PDF_FALLBACK", "false")
DEFAULT_MODEL = os.getenv(
    "KG_DEFAULT_MODEL",
    "7 - Qwen3-Coder-30B-A3B-Instruct - A code model from August 2025",
)
os.environ.setdefault("KG_LLM_MODEL", DEFAULT_MODEL)
os.environ.setdefault("KG_CHAT_MODEL", DEFAULT_MODEL)
os.environ.setdefault("KG_LLM_FALLBACK_MODELS", "alias-large")

MODEL_CONFIG_PATH = Path(__file__).parent / "data" / "model_allowlist.json"


def _load_model_allowlist() -> tuple[list[str], list[str]]:
    """Load model allowlist / preferences.

    Intended for providers like Blablador where not every model supports tool
    calling. If the file doesn't exist, fall back to a small safe list.
    """

    tool_models_default = ["alias-fast", "alias-large", "alias-code"]
    preferred_default = list(tool_models_default)
    if not MODEL_CONFIG_PATH.exists():
        return tool_models_default, preferred_default
    try:
        data = json.loads(MODEL_CONFIG_PATH.read_text(encoding="utf-8"))
    except Exception:
        return tool_models_default, preferred_default
    if not isinstance(data, dict):
        return tool_models_default, preferred_default
    tool_models = data.get("tool_models")
    preferred_order = data.get("preferred_order")
    if not isinstance(tool_models, list) or not all(
        isinstance(x, str) for x in tool_models
    ):
        tool_models = tool_models_default
    if not isinstance(preferred_order, list) or not all(
        isinstance(x, str) for x in preferred_order
    ):
        preferred_order = list(tool_models)
    return list(tool_models), list(preferred_order)


def _compute_model_options() -> tuple[list[str], list[str]]:
    """Returns (tool_models, all_models).

    We show only models that are both available from the provider AND known to
    support tool calling.
    """

    api_key = os.getenv("OPENAI_API_KEY")
    base_url = (
        os.getenv("OPENAI_BASE_URL")
        or os.getenv("OPENAI_API_BASE")
        or "https://api.openai.com/v1/"
    )
    tool_models, preferred = _load_model_allowlist()
    if not api_key:
        return preferred, []
    all_models = _fetch_model_ids(base_url, api_key)
    if not all_models:
        return preferred, []

    filtered = [m for m in preferred if m in all_models and m in tool_models]
    if not filtered:
        filtered = [m for m in all_models if m in tool_models]
    return filtered, all_models


@st.cache_data(ttl=300)
def _fetch_model_ids(base_url: str, api_key: str) -> list[str]:
    url = base_url.rstrip("/") + "/models"
    headers = {"Authorization": f"Bearer {api_key}"}
    resp = requests.get(url, headers=headers, timeout=10)
    if resp.status_code != 200:
        return []
    data = resp.json()
    if not isinstance(data, dict):
        return []
    items = data.get("data")
    if not isinstance(items, list):
        return []
    model_ids: list[str] = []
    for item in items:
        if isinstance(item, dict):
            model_id = item.get("id")
            if isinstance(model_id, str) and model_id.strip():
                model_ids.append(model_id.strip())
    return model_ids


from knowledge_graph.agent import Deps, db, get_agent, openai  # noqa: E402
from knowledge_graph.db import init_db  # noqa: E402
from knowledge_graph.definitions import Chunk  # noqa: E402
from knowledge_graph.handlers.chunk import chunking_handler  # noqa: E402
from knowledge_graph.handlers.inference import (  # noqa: E402
    inferrence_handler,
)


ROOT_DIR = Path(__file__).parent.parent.parent
LOG_DIR = Path("logs")
LOG_DIR.mkdir(parents=True, exist_ok=True)
INGESTION_LOG = LOG_DIR / "ingestion.log"
STATUS_FILE = LOG_DIR / "ingestion.status"
MAX_UPLOAD_MB = int(os.getenv("KG_MAX_UPLOAD_MB", "50"))
MAX_UPLOAD_BYTES = MAX_UPLOAD_MB * 1024 * 1024
METADATA_PATH = Path(__file__).parent / "data" / "party_plan_metadata.json"
IMAGES_METADATA_PATH = ROOT_DIR / "images" / "metadata.json"
BLABLADOR_LOGO_PATH = ROOT_DIR / "logos" / "blablador-ng.svg"
APP_LOGO_PATH = ROOT_DIR / "logos" / "VotoCriterioIA.png"


def _write_status(status: str) -> None:
    STATUS_FILE.write_text(status, encoding="utf-8")


def _read_status() -> str:
    if STATUS_FILE.exists():
        return STATUS_FILE.read_text(encoding="utf-8").strip()
    return "idle"


def _ingestion_logger() -> logging.Logger:
    logger = logging.getLogger("knowledge_graph.streamlit_ingestion")
    logger.setLevel(logging.INFO)
    if not any(
        isinstance(handler, logging.FileHandler)
        and handler.baseFilename == str(INGESTION_LOG)
        for handler in logger.handlers
    ):
        handler = logging.FileHandler(INGESTION_LOG)
        formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s")
        handler.setFormatter(formatter)
        logger.addHandler(handler)
    return logger


def _tail_log(path: Path, lines: int = 20) -> str:
    if not path.exists():
        return ""
    data = path.read_text(encoding="utf-8", errors="ignore").splitlines()
    return "\n".join(data[-lines:])


def _render_svg(path: Path, width: int = 180) -> None:
    if not path.exists():
        return
    encoded = base64.b64encode(path.read_bytes()).decode("ascii")
    st.markdown(
        (
            '<div style="margin: 0.25rem 0 0.5rem 0;">'
            f'<img src="data:image/svg+xml;base64,{encoded}" '
            f'width="{width}" />'
            "</div>"
        ),
        unsafe_allow_html=True,
    )


def _normalize_text(value: str) -> str:
    normalized = unicodedata.normalize("NFKD", value)
    stripped = "".join(ch for ch in normalized if not unicodedata.combining(ch))
    return stripped.lower()


def _normalize_words(value: str) -> list[str]:
    cleaned = _normalize_text(value)
    stopwords = {
        "a",
        "al",
        "con",
        "de",
        "del",
        "el",
        "en",
        "la",
        "las",
        "los",
        "para",
        "por",
        "un",
        "una",
        "y",
    }
    words: list[str] = []
    for chunk in cleaned.replace("-", " ").split():
        token = "".join(ch for ch in chunk if ch.isalnum())
        if not token or token in stopwords or len(token) <= 2:
            continue
        if token:
            words.append(token)
    return words


def _strip_name_prefixes(name: str) -> str:
    normalized = _normalize_text(name).strip()
    prefixes = (
        "coalicion ",
        "partido ",
        "movimiento ",
        "frente ",
        "alianza ",
    )
    for prefix in prefixes:
        if normalized.startswith(prefix):
            return normalized.removeprefix(prefix).strip()
    return normalized


def _load_party_metadata() -> list[dict[str, str]]:
    if not METADATA_PATH.exists():
        return []
    try:
        data = json.loads(METADATA_PATH.read_text(encoding="utf-8"))
    except json.JSONDecodeError:
        return []
    parties = data.get("parties", [])
    if not isinstance(parties, list):
        return []
    cleaned: list[dict[str, str]] = []
    for item in parties:
        if not isinstance(item, dict):
            continue
        name = str(item.get("name", "")).strip()
        acronym = str(item.get("acronym", "")).strip()
        plan_url = str(item.get("plan_url", "")).strip()
        if name and acronym and plan_url:
            cleaned.append({"name": name, "acronym": acronym, "url": plan_url})
    return cleaned


def _load_party_images() -> list[dict[str, str]]:
    if not IMAGES_METADATA_PATH.exists():
        return []
    try:
        data = json.loads(IMAGES_METADATA_PATH.read_text(encoding="utf-8"))
    except json.JSONDecodeError:
        return []
    if not isinstance(data, list):
        return []
    items: list[dict[str, str]] = []
    for entry in data:
        if not isinstance(entry, dict):
            continue
        image_path = str(entry.get("image_path", "")).strip()
        if not image_path:
            continue
        text = " ".join(
            str(entry.get(key, "")) for key in ("alt", "title", "caption")
        ).strip()
        if not text:
            continue
        items.append(
            {
                "text": text,
                "path": image_path,
            }
        )
    return items


def _attach_party_images(
    parties: list[dict[str, str]], images: list[dict[str, str]]
) -> list[dict[str, str]]:
    if not parties or not images:
        return parties
    normalized_images = [
        {
            "text": _normalize_text(entry["text"]),
            "path": entry["path"],
        }
        for entry in images
    ]
    for party in parties:
        name_key = _normalize_text(party["name"])
        stripped_name = _strip_name_prefixes(party["name"])
        acronym = str(party.get("acronym", "")).strip()
        acronym_key = _normalize_text(acronym)
        acronym_words = [acronym_key] if acronym_key else []
        name_words = _normalize_words(name_key)
        stripped_words = _normalize_words(stripped_name)

        best_score = 0
        best_path: str | None = None
        for entry in normalized_images:
            text = entry["text"]
            score = 0
            if name_key and name_key in text:
                score = 4
            elif stripped_name and stripped_name in text:
                score = 3
            elif stripped_words and all(word in text for word in stripped_words):
                score = 2
            elif name_words and all(word in text for word in name_words):
                score = 1
            if acronym_words and any(
                f" {word} " in f" {text} " for word in acronym_words
            ):
                score = max(score, 2)
            if score and "divisa" in text:
                score += 1
            if score > best_score:
                image_path = ROOT_DIR / entry["path"]
                if image_path.exists():
                    best_score = score
                    best_path = str(image_path)
        if best_path:
            party["image_path"] = best_path
    return parties


def _render_party_grid(items: list[dict[str, str]]) -> None:
    if not items:
        return
    st.subheader("Planes de gobierno 2026")
    st.caption("Accesos directos a los programas oficiales del TSE.")
    cols = 5
    for i in range(0, len(items), cols):
        row = st.columns(cols)
        for col, item in zip(row, items[i : i + cols], strict=False):
            with col:
                image_path = item.get("image_path")
                if image_path:
                    st.image(image_path, width="stretch")
                else:
                    _render_party_placeholder(item.get("acronym", ""))
                st.markdown(
                    "\n".join(
                        [
                            f"<strong>{item['name']}</strong>",
                            f'<a href="{item["url"]}" target="_blank">'
                            f"{item['acronym']} (ver plan)</a>",
                        ]
                    ),
                    unsafe_allow_html=True,
                )


def _render_party_placeholder(acronym: str) -> None:
    label = (acronym or "?").strip().upper()
    st.markdown(
        "\n".join(
            [
                '<div class="party-placeholder">',
                f'<div class="party-placeholder-acronym">{label}</div>',
                "</div>",
            ]
        ),
        unsafe_allow_html=True,
    )


def _run_ingestion(doc, db_name: str) -> None:
    logger = _ingestion_logger()
    _write_status("running")
    logger.info("Starting ingestion for %s", doc.filename)
    try:
        db_ingest = init_db(init_llm=True, db_name=db_name, init_indexes=False)
        chunking_handler(db_ingest, doc)

        stamp = "streamlit"
        db_ingest.sync_conn.query(
            "UPDATE $rec SET chunked = $hash",
            {"rec": doc.id, "hash": stamp},
        )

        chunks = db_ingest.query(
            """SELECT * FROM chunk
                WHERE doc = $doc
                AND concepts_inferred IS NONE
                ORDER BY index ASC
            """,
            {"doc": doc.id},
            dict,
        )
        for chunk_data in chunks:
            chunk = Chunk.model_validate(chunk_data)
            _ = inferrence_handler(db_ingest, chunk)
            db_ingest.sync_conn.query(
                "UPDATE $rec SET concepts_inferred = $hash",
                {"rec": chunk.id, "hash": stamp},
            )

        logger.info("Finished ingestion for %s", doc.filename)
        _write_status("finished")
    except Exception as exc:
        logger.exception("Ingestion failed: %s", exc)
        _write_status(f"error: {exc}")


def _start_ingestion_thread(doc, db_name: str) -> None:
    thread = threading.Thread(
        target=_run_ingestion,
        args=(doc, db_name),
        daemon=True,
    )
    thread.start()


def _guess_content_type(filename: str, content_type: str | None) -> str:
    if not content_type or content_type == "application/octet-stream":
        guessed, _ = mimetypes.guess_type(filename)
        if guessed:
            return guessed
        return "application/octet-stream"
    return content_type


def _is_port_open(port: int) -> bool:
    import socket

    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
        sock.settimeout(0.5)
        try:
            sock.connect(("127.0.0.1", port))
        except Exception:
            return False
    return True


def _status_line(label: str, ok: bool) -> str:
    icon = "✅" if ok else "❌"
    return f"{icon} {label}"


def _ingestion_badge(status: str) -> str:
    normalized = status.strip().lower()
    if normalized.startswith("running"):
        return "🟡 running"
    if normalized.startswith("finished"):
        return "✅ finished"
    if normalized.startswith("error"):
        return "❌ error"
    return "⚪ idle"


def _auto_refresh(interval_ms: int = 3000) -> None:
    """Best-effort auto refresh.

    Streamlit OSS doesn't expose a timer-based rerun API (st.autorefresh is not
    available). We use a tiny HTML component to reload the page while ingestion
    is running so status/logs update without manual refresh.
    """

    # Avoid full page reload. Trigger a Streamlit rerun by sending a
    # `streamlit:setComponentValue` message from the iframe.
    components.html(
        f"""
        <script>
        (() => {{
          const msg = {{
            isStreamlitMessage: true,
            type: "streamlit:setComponentValue",
            value: Date.now(),
          }};
          setTimeout(() => window.parent.postMessage(msg, "*"), {int(interval_ms)});
        }})();
        </script>
        """,
        height=0,
        width=0,
    )


def _looks_english(text: str) -> bool:
    english_markers = (
        "based on the information",
        "i cannot",
        "i can't",
        "the retrieved documents",
        "you would need to",
        "to get accurate information",
        "the knowledge base",
        "in the context of",
        "the party",
        "government plan",
    )
    spanish_markers = (
        " el ",
        " la ",
        " de ",
        " que ",
        " para ",
        " sobre ",
        " partido ",
        " elecciones ",
        " costa ",
        " rica ",
        " gobierno ",
    )
    lowered = text.lower()
    english_hits = sum(marker in lowered for marker in english_markers)
    spanish_hits = sum(marker in lowered for marker in spanish_markers)
    return english_hits >= 2 and spanish_hits < 2


_URL_RE = re.compile(r"https?://[^\s\)\]\}<>\"']+")


def _guess_party(text: str, parties: list[dict[str, str]]) -> dict[str, str] | None:
    lowered = text.lower()
    for party in parties:
        acronym = party.get("acronym")
        if acronym and re.search(rf"\b{re.escape(acronym)}\b", text, re.IGNORECASE):
            return party
    for party in parties:
        name = party.get("name")
        if name and name.lower() in lowered:
            return party
    return None


def _sanitize_plan_urls(
    response: str,
    user_text: str,
    parties: list[dict[str, str]],
) -> str:
    """Remove hallucinated plan URLs and optionally add the canonical TSE link.

    We only filter URLs that look like "plan" links (contain 'plan') and are not
    among the known plan URLs from party metadata.
    """

    allowed_plan_urls = {
        p.get("plan_url")
        for p in parties
        if isinstance(p.get("plan_url"), str) and p.get("plan_url")
    }
    if not allowed_plan_urls:
        return response

    text = response
    removed_any = False
    for raw in _URL_RE.findall(response):
        cleaned = raw.rstrip(".,);:]\"'")
        if "plan" in cleaned.lower() and cleaned not in allowed_plan_urls:
            text = text.replace(raw, "")
            removed_any = True

    if not removed_any:
        return response

    party = _guess_party(user_text, parties) or _guess_party(text, parties)
    if party:
        plan_url = party.get("plan_url")
        if plan_url and plan_url in allowed_plan_urls and plan_url not in text:
            text = text.strip() + f"\n\nPlan de Gobierno (TSE): {plan_url}"

    return text


st.set_page_config(page_title="Voto Criterio IA", layout="wide")

logo_col, title_col = st.columns([1.4, 6.6], gap="small")
with logo_col:
    if APP_LOGO_PATH.exists():
        st.image(str(APP_LOGO_PATH), width=180)
with title_col:
    st.title("VotoCriterioIA")
    st.caption(
        "Análisis político asistido por IA a partir de documentos estructurados "
        "y fuentes públicas sobre el proceso electoral 2026 en Costa Rica"
    )

st.markdown(
    """
    <style>
    div[data-testid="stFileUploaderDropzone"] span {
        display: none !important;
    }
    div[data-testid="stFileUploaderDropzone"]::before {
        content: "Arrastra y suelta el archivo aqui o haz clic para seleccionarlo";
        display: block;
        color: #4b5563;
        font-size: 0.9rem;
        line-height: 1.4;
        padding: 0.4rem 0;
    }

    .party-placeholder {
        height: 110px;
        border-radius: 12px;
        background: linear-gradient(135deg, #0ea5e9 0%, #22c55e 100%);
        display: flex;
        align-items: center;
        justify-content: center;
        margin-bottom: 0.5rem;
    }

    .party-placeholder-acronym {
        font-weight: 700;
        letter-spacing: 0.08em;
        color: rgba(255, 255, 255, 0.92);
        font-size: 1.25rem;
        text-shadow: 0 1px 1px rgba(0, 0, 0, 0.25);
    }
    </style>
    """,
    unsafe_allow_html=True,
)

party_metadata = _load_party_metadata()
party_images = _load_party_images()
party_metadata = _attach_party_images(party_metadata, party_images)
_render_party_grid(party_metadata)


SUGGESTED_QUESTIONS_PATH = Path(__file__).parent / "data" / "suggested_questions.json"


def _load_suggested_questions() -> list[str]:
    fallback = [
        "Resumen del plan de gobierno del PLN (puntos clave)",
        "Que propone el PUSC sobre empleo y economia?",
        "Que propone el Frente Amplio sobre educacion?",
        "Cuales partidos mencionan 'transporte publico' en su plan?",
    ]
    if not SUGGESTED_QUESTIONS_PATH.exists():
        return fallback
    try:
        data = json.loads(SUGGESTED_QUESTIONS_PATH.read_text(encoding="utf-8"))
    except Exception:
        return fallback
    if not isinstance(data, list):
        return fallback
    questions: list[str] = []
    for item in data:
        if isinstance(item, str) and item.strip():
            questions.append(item.strip())
    return questions or fallback


def _on_suggested_pill_change() -> None:
    prompt = st.session_state.get("suggested_pill")
    if isinstance(prompt, str) and prompt.strip():
        st.session_state["pending_prompt"] = prompt.strip()
    # Reset selection so it doesn't auto-resend on reruns.
    st.session_state["suggested_pill"] = None


st.markdown("### Preguntas sugeridas")
st.caption(
    "Son solo ejemplos. Si no hay resultados, es porque ese documento aun no esta "
    "en la base; puedes subir el PDF desde la barra lateral."
)
st.pills(
    "Preguntas sugeridas",
    _load_suggested_questions(),
    selection_mode="single",
    default=None,
    key="suggested_pill",
    on_change=_on_suggested_pill_change,
    label_visibility="collapsed",
)

MODEL_OPTIONS, ALL_MODELS = _compute_model_options()
if not MODEL_OPTIONS:
    st.warning(
        "No tool-capable models detected from the provider. "
        "Check your API key / base URL."
    )

if "chat_messages" not in st.session_state:
    st.session_state.chat_messages = []
if "history" not in st.session_state:
    st.session_state.history = []
if "ingestion_running" not in st.session_state:
    st.session_state.ingestion_running = False
if "selected_model" not in st.session_state:
    if DEFAULT_MODEL in MODEL_OPTIONS:
        st.session_state.selected_model = DEFAULT_MODEL
    elif MODEL_OPTIONS:
        st.session_state.selected_model = MODEL_OPTIONS[0]
    else:
        st.session_state.selected_model = DEFAULT_MODEL

current_status = _read_status()
if current_status.startswith("running"):
    st.session_state.ingestion_running = True
elif current_status:
    st.session_state.ingestion_running = False

if st.session_state.ingestion_running:
    _auto_refresh(3000)

with st.sidebar:
    st.header("Status")
    st.caption(_status_line("SurrealDB (8000)", _is_port_open(8000)))
    st.caption(_status_line("Streamlit UI (8501)", _is_port_open(8501)))
    st.caption(f"DB name: {os.environ.get('DB_NAME', '')}")
    st.caption(f"Ingestion: {_ingestion_badge(current_status or 'idle')}")
    st.markdown("### Modelo")
    if not MODEL_OPTIONS:
        st.error("No hay modelos disponibles (tool calling).")
    else:
        if st.session_state.selected_model not in MODEL_OPTIONS:
            st.session_state.selected_model = MODEL_OPTIONS[0]
        selected_index = MODEL_OPTIONS.index(st.session_state.selected_model)
        st.session_state.selected_model = st.selectbox(
            "Modelo",
            MODEL_OPTIONS,
            index=selected_index,
        )
        os.environ["KG_CHAT_MODEL"] = st.session_state.selected_model
        os.environ["KG_LLM_MODEL"] = st.session_state.selected_model
        fallbacks = [m for m in MODEL_OPTIONS if m != st.session_state.selected_model]
        os.environ["KG_LLM_FALLBACK_MODELS"] = ",".join(fallbacks[:2])
        st.caption(f"Modelo activo: {st.session_state.selected_model}")
        if ALL_MODELS:
            st.caption(
                f"Detectados: {len(ALL_MODELS)} modelos, mostrando: {len(MODEL_OPTIONS)}"
            )

    _render_svg(BLABLADOR_LOGO_PATH)
    st.caption(
        "Gracias a Blablador y a Helmholtz AI por el soporte con los modelos LLM"
    )

    st.divider()
    st.header("Subir documento")
    st.caption(
        "Sube un PDF o Markdown. Al confirmar, se guardara en la base y se "
        "iniciara la ingesta en segundo plano."
    )
    st.caption(f"Tamano maximo: {MAX_UPLOAD_MB} MB")

    uploaded = st.file_uploader(
        "Selecciona un PDF o Markdown",
        type=["pdf", "md", "markdown"],
        accept_multiple_files=False,
        disabled=st.session_state.ingestion_running or not MODEL_OPTIONS,
    )
    if uploaded is not None and st.button(
        "Subir y procesar",
        disabled=st.session_state.ingestion_running or not MODEL_OPTIONS,
    ):
        if uploaded.size and uploaded.size > MAX_UPLOAD_BYTES:
            st.error("File exceeds 50 MB limit.")
        else:
            content = uploaded.getvalue()
            content_type = _guess_content_type(uploaded.name, uploaded.type)
            doc, cached = db.store_original_document_from_bytes(
                uploaded.name,
                content_type,
                content,
            )
            if cached:
                st.info("Document already exists; ingestion will re-run.")
            st.session_state.ingestion_running = True
            _write_status("running")
            _start_ingestion_thread(doc, os.environ.get("DB_NAME", ""))
            st.success("Upload complete. Ingestion started.")
            st.rerun()

    st.divider()
    st.header("Ingestion logs")
    st.caption(f"Status: {current_status}")
    log_text = _tail_log(INGESTION_LOG, lines=20)
    if log_text:
        st.code(log_text, language="text")
    else:
        st.caption("No ingestion logs yet.")

    st.divider()
    if st.button("🗑️ Reset chat"):
        st.session_state.chat_messages = []
        st.session_state.history = []
        st.rerun()

for message in st.session_state.chat_messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

user_input = st.chat_input("Pregunta sobre elecciones en Costa Rica")
pending_prompt = st.session_state.pop("pending_prompt", None)
if not user_input and isinstance(pending_prompt, str) and pending_prompt.strip():
    user_input = pending_prompt.strip()
if user_input:
    st.session_state.chat_messages.append({"role": "user", "content": user_input})
    with st.chat_message("user"):
        st.markdown(user_input)

    with st.chat_message("assistant"):
        with st.spinner("Pensando..."):
            try:
                agent = get_agent(st.session_state.selected_model)
                result = agent.run_sync(
                    user_input,
                    deps=Deps(db, openai),
                    message_history=st.session_state.history,
                )
                response = result.output
                st.session_state.history = result.all_messages()
                if _looks_english(response):
                    retry = agent.run_sync(
                        user_input,
                        deps=Deps(db, openai),
                        message_history=st.session_state.history,
                        instructions="Responde solo en español.",
                    )
                    response = retry.output
                    st.session_state.history = retry.all_messages()
                if _looks_english(response):
                    response = "No tengo informacion en la base de conocimiento sobre ese tema."
                response = _sanitize_plan_urls(response, user_input, party_metadata)
            except Exception as exc:
                message = str(exc)
                if "Exceeded maximum retries" in message:
                    response = "No tengo informacion en la base de conocimiento sobre ese tema."
                else:
                    response = f"Error: {message}"
        st.markdown(response)

    st.session_state.chat_messages.append({"role": "assistant", "content": response})