"""
Flask application exposing a minimal search API backed by Elasticsearch.

Routes:
    GET /                -> Static HTML search page.
    GET /api/channels    -> List available channels (via terms aggregation).
    GET /api/search      -> Search index with pagination and simple highlighting.
    GET /api/transcript  -> Return full transcript for a given video_id.
"""

from __future__ import annotations

import copy
import json
import logging
import re
from pathlib import Path
from typing import Any, Dict, Iterable, List, Optional, Sequence, Set

from collections import Counter
from datetime import datetime

from flask import Flask, jsonify, request, send_from_directory

from .config import CONFIG, AppConfig

try:
    from elasticsearch import Elasticsearch  # type: ignore
    from elasticsearch import BadRequestError  # type: ignore
except ImportError:  # pragma: no cover - dependency optional
    Elasticsearch = None
    BadRequestError = Exception  # type: ignore

LOGGER = logging.getLogger(__name__)


def _ensure_client(config: AppConfig) -> "Elasticsearch":
    if Elasticsearch is None:
        raise RuntimeError(
            "elasticsearch package not installed. "
            "Install elasticsearch>=7 to run the Flask search app."
        )
    kwargs = {}
    if config.elastic.api_key:
        kwargs["api_key"] = config.elastic.api_key
    elif config.elastic.username and config.elastic.password:
        kwargs["basic_auth"] = (
            config.elastic.username,
            config.elastic.password,
        )
    if config.elastic.ca_cert:
        kwargs["ca_certs"] = str(config.elastic.ca_cert)
    kwargs["verify_certs"] = config.elastic.verify_certs
    return Elasticsearch(config.elastic.url, **kwargs)


def metrics_payload(data_root: Path) -> Dict[str, Any]:
    total_items = 0
    channel_counter: Counter = Counter()
    channel_name_map: Dict[str, str] = {}
    year_counter: Counter = Counter()
    month_counter: Counter = Counter()

    if not data_root.exists():
        LOGGER.warning("Data directory %s not found; metrics will be empty.", data_root)
        return {
            "totalItems": 0,
            "totalChannels": 0,
            "itemsPerChannel": [],
            "yearHistogram": [],
            "recentMonths": [],
        }

    for path in data_root.rglob("*.json"):
        try:
            with path.open("r", encoding="utf-8") as handle:
                doc = json.load(handle)
        except Exception:
            continue

        total_items += 1

        channel_id = doc.get("channel_id")
        channel_name = doc.get("channel_name") or channel_id
        if channel_id:
            channel_counter[channel_id] += 1
            if channel_name and channel_id not in channel_name_map:
                channel_name_map[channel_id] = channel_name

        date_value = doc.get("date") or doc.get("published_at")
        dt: Optional[datetime] = None
        if isinstance(date_value, str):
            for fmt in ("%Y-%m-%d", "%Y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ"):
                try:
                    dt = datetime.strptime(date_value[: len(fmt)], fmt)
                    break
                except Exception:
                    continue
        elif isinstance(date_value, (int, float)):
            try:
                dt = datetime.fromtimestamp(date_value)
            except Exception:
                dt = None

        if dt:
            year_counter[str(dt.year)] += 1
            month_counter[dt.strftime("%Y-%m")] += 1

    items_per_channel = [
        {
            "label": channel_name_map.get(cid, cid),
            "count": count,
        }
        for cid, count in channel_counter.most_common()
    ]

    year_histogram = [
        {"bucket": year, "count": year_counter[year]}
        for year in sorted(year_counter.keys())
    ]

    recent_months = sorted(month_counter.keys())
    recent_months = recent_months[-12:]
    recent_months_payload = [
        {"bucket": month, "count": month_counter[month]} for month in recent_months
    ]

    return {
        "totalItems": total_items,
        "totalChannels": len(channel_counter),
        "itemsPerChannel": items_per_channel,
        "yearHistogram": year_histogram,
        "recentMonths": recent_months_payload,
    }


def elastic_metrics_payload(
    client: "Elasticsearch",
    index: str,
    *,
    channel_field_candidates: Optional[List[str]] = None,
    debug: bool = False,
) -> Dict[str, Any]:
    if channel_field_candidates is None:
        channel_field_candidates = ["channel_id.keyword", "channel_id"]

    base_body: Dict[str, Any] = {
        "size": 0,
        "track_total_hits": True,
        "aggs": {
            "channels": {
                "terms": {
                    "field": "channel_id.keyword",
                    "size": 500,
                    "order": {"_count": "desc"},
                },
                "aggs": {
                    "name": {
                        "top_hits": {
                            "size": 1,
                            "_source": {"includes": ["channel_name"]},
                        }
                    }
                },
            },
            "year_histogram": {
                "date_histogram": {
                    "field": "date",
                    "calendar_interval": "year",
                    "format": "yyyy",
                }
            },
            "month_histogram": {
                "date_histogram": {
                    "field": "date",
                    "calendar_interval": "month",
                    "format": "yyyy-MM",
                    "order": {"_key": "asc"},
                }
            },
        },
    }

    last_error: Optional[Exception] = None
    response: Optional[Dict[str, Any]] = None
    for candidate_field in channel_field_candidates:
        body = json.loads(json.dumps(base_body))
        body["aggs"]["channels"]["terms"]["field"] = candidate_field
        try:
            if debug:
                LOGGER.info(
                    "Elasticsearch metrics request: %s",
                    json.dumps({"index": index, "body": body}, indent=2),
                )
            response = client.search(index=index, body=body)
            break
        except BadRequestError as exc:
            last_error = exc
            if debug:
                LOGGER.warning(
                    "Metrics aggregation failed for field %s: %s",
                    candidate_field,
                    exc,
                )
    if response is None:
        raise last_error or RuntimeError("Unable to compute metrics from Elasticsearch.")

    hits = response.get("hits", {})
    total_items = hits.get("total", {}).get("value", 0)

    if debug:
        LOGGER.info(
            "Elasticsearch metrics response: %s",
            json.dumps(response, indent=2, default=str),
        )

    aggregations = response.get("aggregations", {})
    channel_buckets = aggregations.get("channels", {}).get("buckets", [])
    items_per_channel = []
    for bucket in channel_buckets:
        key = bucket.get("key")
        channel_name = key
        top_hits = (
            bucket.get("name", {})
            .get("hits", {})
            .get("hits", [])
        )
        if top_hits:
            channel_name = (
                top_hits[0]
                .get("_source", {})
                .get("channel_name", channel_name)
            )
        items_per_channel.append(
            {"label": channel_name or key, "count": bucket.get("doc_count", 0)}
        )

    year_buckets = aggregations.get("year_histogram", {}).get("buckets", [])
    year_histogram = [
        {
            "bucket": bucket.get("key_as_string")
            or str(bucket.get("key")),
            "count": bucket.get("doc_count", 0),
        }
        for bucket in year_buckets
    ]

    month_buckets = aggregations.get("month_histogram", {}).get("buckets", [])
    recent_months_entries = [
        {
            "bucket": bucket.get("key_as_string")
            or str(bucket.get("key")),
            "count": bucket.get("doc_count", 0),
            "_key": bucket.get("key"),
        }
        for bucket in month_buckets
    ]
    recent_months_entries.sort(key=lambda item: item.get("_key", 0))
    recent_months_payload = [
        {"bucket": entry["bucket"], "count": entry["count"]}
        for entry in recent_months_entries[-12:]
    ]

    return {
        "totalItems": total_items,
        "totalChannels": len(items_per_channel),
        "itemsPerChannel": items_per_channel,
        "yearHistogram": year_histogram,
        "recentMonths": recent_months_payload,
    }


def parse_channel_params(values: Iterable[Optional[str]]) -> List[str]:
    seen: Set[str] = set()
    channels: List[str] = []
    for value in values:
        if not value:
            continue
        for part in str(value).split(","):
            cleaned = part.strip()
            if not cleaned or cleaned.lower() == "all":
                continue
            if cleaned not in seen:
                seen.add(cleaned)
                channels.append(cleaned)
    return channels


def build_year_filter(year: Optional[str]) -> Optional[Dict]:
    if not year:
        return None
    try:
        year_int = int(year)
        return {
            "range": {
                "date": {
                    "gte": f"{year_int}-01-01",
                    "lt": f"{year_int + 1}-01-01",
                    "format": "yyyy-MM-dd"
                }
            }
        }
    except (ValueError, TypeError):
        return None


def build_channel_filter(channels: Optional[Sequence[str]]) -> Optional[Dict]:
    if not channels:
        return None
    per_channel_clauses: List[Dict[str, Any]] = []
    for value in channels:
        if not value:
            continue
        per_channel_clauses.append(
            {
                "bool": {
                    "should": [
                        {"term": {"channel_id.keyword": value}},
                        {"term": {"channel_id": value}},
                    ],
                    "minimum_should_match": 1,
                }
            }
        )
    if not per_channel_clauses:
        return None
    if len(per_channel_clauses) == 1:
        return per_channel_clauses[0]
    return {
        "bool": {
            "should": per_channel_clauses,
            "minimum_should_match": 1,
        }
    }


def build_query_payload(
    query: str,
    *,
    channels: Optional[Sequence[str]] = None,
    year: Optional[str] = None,
    sort: str = "relevant",
    use_exact: bool = True,
    use_fuzzy: bool = True,
    use_phrase: bool = True,
    use_query_string: bool = False,
) -> Dict:
    filters: List[Dict] = []
    should: List[Dict] = []

    channel_filter = build_channel_filter(channels)
    if channel_filter:
        filters.append(channel_filter)

    year_filter = build_year_filter(year)
    if year_filter:
        filters.append(year_filter)

    if use_query_string:
        base_fields = ["title^3", "description^2", "transcript_full", "transcript_secondary_full"]
        qs_query = (query or "").strip() or "*"
        query_body: Dict[str, Any] = {
            "query_string": {
                "query": qs_query,
                "default_operator": "AND",
                "fields": base_fields,
            }
        }
        if filters:
            query_body = {"bool": {"must": query_body, "filter": filters}}
        body: Dict = {
            "query": query_body,
            "highlight": {
                "fields": {
                    "transcript_full": {
                    "fragment_size": 160,
                    "number_of_fragments": 5,
                    "fragmenter": "span",
                },
                "transcript_secondary_full": {
                    "fragment_size": 160,
                    "number_of_fragments": 5,
                    "fragmenter": "span",
                },
                "title": {"number_of_fragments": 0},
                "description": {
                        "fragment_size": 160,
                        "number_of_fragments": 1,
                    },
                },
                "require_field_match": False,
                "pre_tags": ["<mark>"],
                "post_tags": ["</mark>"],
                "encoder": "html",
                "max_analyzed_offset": 900000,
            },
        }
        if sort == "newer":
            body["sort"] = [{"date": {"order": "desc"}}]
        elif sort == "older":
            body["sort"] = [{"date": {"order": "asc"}}]
        elif sort == "referenced":
            body["sort"] = [{"referenced_by_count": {"order": "desc"}}]
        return body

    if query:
        base_fields = ["title^3", "description^2", "transcript_full", "transcript_secondary_full"]
        if use_phrase:
            should.append(
                {
                    "match_phrase": {
                        "transcript_full": {
                            "query": query,
                            "slop": 2,
                            "boost": 10.0,
                        }
                    }
                }
            )
            should.append(
                {
                    "match_phrase": {
                        "transcript_secondary_full": {
                            "query": query,
                            "slop": 2,
                            "boost": 10.0,
                        }
                    }
                }
            )
        if use_fuzzy:
            should.append(
                {
                    "multi_match": {
                        "query": query,
                        "fields": base_fields,
                        "type": "best_fields",
                        "operator": "and",
                        "fuzziness": "AUTO",
                        "prefix_length": 1,
                        "max_expansions": 50,
                        "boost": 1.5,
                    }
                }
            )
        if use_exact:
            should.append(
                {
                    "multi_match": {
                        "query": query,
                        "fields": base_fields,
                        "type": "best_fields",
                        "operator": "and",
                        "boost": 3.0,
                    }
                }
            )

    if should:
        query_body: Dict = {
            "bool": {
                "should": should,
                "minimum_should_match": 1,
            }
        }
        if filters:
            query_body["bool"]["filter"] = filters
    elif filters:
        query_body = {"bool": {"filter": filters}}
    else:
        query_body = {"match_all": {}}

    body: Dict = {
        "query": query_body,
        "highlight": {
            "fields": {
                "transcript_full": {
                    "fragment_size": 160,
                    "number_of_fragments": 5,
                    "fragmenter": "span",
                },
                "transcript_secondary_full": {
                    "fragment_size": 160,
                    "number_of_fragments": 5,
                    "fragmenter": "span",
                },
                "title": {"number_of_fragments": 0},
                "description": {
                    "fragment_size": 160,
                    "number_of_fragments": 1,
                },
            },
            "require_field_match": False,
            "pre_tags": ["<mark>"],
            "post_tags": ["</mark>"],
            "encoder": "html",
            "max_analyzed_offset": 900000,
        },
    }
    if query_body.get("match_all") is None:
        body["highlight"]["highlight_query"] = copy.deepcopy(query_body)

    if sort == "newer":
        body["sort"] = [{"date": {"order": "desc"}}]
    elif sort == "older":
        body["sort"] = [{"date": {"order": "asc"}}]
    elif sort == "referenced":
        body["sort"] = [{"referenced_by_count": {"order": "desc"}}]
    return body


def create_app(config: AppConfig = CONFIG) -> Flask:
    app = Flask(__name__, static_folder=str(Path(__file__).parent / "static"))
    client = _ensure_client(config)
    index = config.elastic.index

    @app.route("/")
    def index_page():
        return send_from_directory(app.static_folder, "index.html")

    @app.route("/static/<path:filename>")
    def static_files(filename: str):
        return send_from_directory(app.static_folder, filename)

    @app.route("/api/channels")
    def channels():
        base_channels_body = {
            "size": 0,
            "aggs": {
                "channels": {
                    "terms": {"field": "channel_id", "size": 200},
                    "aggs": {
                        "name": {
                            "top_hits": {
                                "size": 1,
                                "_source": {"includes": ["channel_name"]},
                            }
                        }
                    },
                }
            },
        }

        def run_channels_request(field_name: str):
            body = json.loads(json.dumps(base_channels_body))  # deep copy
            body["aggs"]["channels"]["terms"]["field"] = field_name
            if config.elastic.debug:
                LOGGER.info(
                    "Elasticsearch channels request: %s",
                    json.dumps({"index": index, "body": body}, indent=2),
                )
            return client.search(index=index, body=body)

        response = None
        last_error = None
        for candidate_field in ("channel_id.keyword", "channel_id"):
            try:
                response = run_channels_request(candidate_field)
                if config.elastic.debug:
                    LOGGER.info("Channels aggregation used field: %s", candidate_field)
                break
            except BadRequestError as exc:
                last_error = exc
                if config.elastic.debug:
                    LOGGER.warning(
                        "Channels aggregation failed for field %s: %s",
                        candidate_field,
                        exc,
                    )
        if response is None:
            raise last_error or RuntimeError("Unable to aggregate channels.")

        if config.elastic.debug:
            LOGGER.info(
                "Elasticsearch channels response: %s",
                json.dumps(response, indent=2, default=str),
            )
        buckets = (
            response.get("aggregations", {})
            .get("channels", {})
            .get("buckets", [])
        )
        data = [
            {
                "Id": bucket.get("key"),
                "Name": (
                    bucket.get("name", {})
                    .get("hits", {})
                    .get("hits", [{}])[0]
                    .get("_source", {})
                    .get("channel_name", bucket.get("key"))
                ),
                "Count": bucket.get("doc_count", 0),
            }
            for bucket in buckets
        ]
        data.sort(key=lambda item: item["Name"].lower())
        return jsonify(data)

    @app.route("/api/years")
    def years():
        body = {
            "size": 0,
            "aggs": {
                "years": {
                    "date_histogram": {
                        "field": "date",
                        "calendar_interval": "year",
                        "format": "yyyy",
                        "order": {"_key": "desc"}
                    }
                }
            }
        }

        if config.elastic.debug:
            LOGGER.info(
                "Elasticsearch years request: %s",
                json.dumps({"index": index, "body": body}, indent=2),
            )

        response = client.search(index=index, body=body)

        if config.elastic.debug:
            LOGGER.info(
                "Elasticsearch years response: %s",
                json.dumps(response, indent=2, default=str),
            )

        buckets = (
            response.get("aggregations", {})
            .get("years", {})
            .get("buckets", [])
        )

        data = [
            {
                "Year": bucket.get("key_as_string"),
                "Count": bucket.get("doc_count", 0),
            }
            for bucket in buckets
            if bucket.get("doc_count", 0) > 0
        ]

        return jsonify(data)

    @app.route("/api/search")
    def search():
        query = request.args.get("q", "", type=str)
        raw_channels: List[Optional[str]] = request.args.getlist("channel_id")
        legacy_channel = request.args.get("channel", type=str)
        if legacy_channel:
            raw_channels.append(legacy_channel)
        channels = parse_channel_params(raw_channels)
        year = request.args.get("year", "", type=str) or None
        sort = request.args.get("sort", "relevant", type=str)
        page = max(request.args.get("page", 0, type=int), 0)
        size = max(request.args.get("size", 10, type=int), 1)

        def parse_flag(name: str, default: bool = True) -> bool:
            value = request.args.get(name)
            if value is None:
                return default
            return value.lower() not in {"0", "false", "no"}

        use_exact = parse_flag("exact", True)
        use_fuzzy = parse_flag("fuzzy", True)
        use_phrase = parse_flag("phrase", True)
        use_query_string = parse_flag("query_string", False)
        if use_query_string:
            use_exact = use_fuzzy = use_phrase = False

        payload = build_query_payload(
            query,
            channels=channels,
            year=year,
            sort=sort,
            use_exact=use_exact,
            use_fuzzy=use_fuzzy,
            use_phrase=use_phrase,
            use_query_string=use_query_string,
        )
        start = page * size
        if config.elastic.debug:
            LOGGER.info(
                "Elasticsearch search request: %s",
                json.dumps(
                    {
                        "index": index,
                        "from": start,
                        "size": size,
                        "body": payload,
                        "channels": channels,
                        "toggles": {
                            "exact": use_exact,
                            "fuzzy": use_fuzzy,
                            "phrase": use_phrase,
                        },
                    },
                    indent=2,
                ),
            )
        response = client.search(
            index=index,
            from_=start,
            size=size,
            body=payload,
        )
        if config.elastic.debug:
            LOGGER.info(
                "Elasticsearch search response: %s",
                json.dumps(response, indent=2, default=str),
            )

        hits = response.get("hits", {})
        total = hits.get("total", {}).get("value", 0)
        documents = []
        for hit in hits.get("hits", []):
            source = hit.get("_source", {})
            highlight_map = hit.get("highlight", {})
            transcript_highlight = (
                (highlight_map.get("transcript_full", []) or [])
                + (highlight_map.get("transcript_secondary_full", []) or [])
            )

            title_html = (
                highlight_map.get("title")
                or [source.get("title") or "Untitled"]
            )[0]
            description_html = (
                highlight_map.get("description")
                or [source.get("description") or ""]
            )[0]
            documents.append(
                {
                    "video_id": source.get("video_id"),
                    "channel_id": source.get("channel_id"),
                    "channel_name": source.get("channel_name"),
                    "title": source.get("title"),
                    "titleHtml": title_html,
                    "description": source.get("description"),
                    "descriptionHtml": description_html,
                    "date": source.get("date"),
                    "url": source.get("url"),
                    "toHighlight": transcript_highlight,
                    "highlightSource": {
                        "primary": bool(highlight_map.get("transcript_full")),
                        "secondary": bool(highlight_map.get("transcript_secondary_full")),
                    },
                    "internal_references_count": source.get("internal_references_count", 0),
                    "referenced_by_count": source.get("referenced_by_count", 0),
                }
            )

        return jsonify(
            {
                "items": documents,
                "totalResults": total,
                "totalPages": (total + size - 1) // size,
                "currentPage": page,
            }
        )

    @app.route("/api/metrics")
    def metrics():
        try:
            data = elastic_metrics_payload(
                client,
                index,
                channel_field_candidates=["channel_id.keyword", "channel_id"],
                debug=config.elastic.debug,
            )
        except Exception:
            LOGGER.exception(
                "Falling back to local metrics payload due to Elasticsearch error.",
                exc_info=True,
            )
            data = metrics_payload(config.data.root)
        return jsonify(data)

    @app.route("/api/frequency")
    def frequency():
        raw_term = request.args.get("term", type=str) or ""
        use_query_string = request.args.get("query_string", default="0", type=str)
        use_query_string = (use_query_string or "").lower() in {"1", "true", "yes"}
        term = raw_term.strip()
        if not term and not use_query_string:
            return ("term parameter is required", 400)
        if use_query_string and not term:
            term = "*"

        raw_channels: List[Optional[str]] = request.args.getlist("channel_id")
        legacy_channel = request.args.get("channel", type=str)
        if legacy_channel:
            raw_channels.append(legacy_channel)
        channels = parse_channel_params(raw_channels)
        year = request.args.get("year", "", type=str) or None
        interval = (request.args.get("interval", "month") or "month").lower()
        allowed_intervals = {"day", "week", "month", "quarter", "year"}
        if interval not in allowed_intervals:
            interval = "month"
        start = request.args.get("start", type=str)
        end = request.args.get("end", type=str)

        filters: List[Dict] = []
        channel_filter = build_channel_filter(channels)
        if channel_filter:
            filters.append(channel_filter)
        year_filter = build_year_filter(year)
        if year_filter:
            filters.append(year_filter)
        if start or end:
            range_filter: Dict[str, Dict[str, Dict[str, str]]] = {"range": {"date": {}}}
            if start:
                range_filter["range"]["date"]["gte"] = start
            if end:
                range_filter["range"]["date"]["lte"] = end
            filters.append(range_filter)

        base_fields = ["title^3", "description^2", "transcript_full", "transcript_secondary_full"]
        if use_query_string:
            qs_query = term or "*"
            must_clause: List[Dict[str, Any]] = [
                {
                    "query_string": {
                        "query": qs_query,
                        "default_operator": "AND",
                        "fields": base_fields,
                    }
                }
            ]
        else:
            must_clause = [
                {
                    "multi_match": {
                        "query": term,
                        "fields": base_fields,
                        "type": "best_fields",
                        "operator": "and",
                    }
                }
            ]

        query: Dict[str, Any] = {"bool": {"must": must_clause}}
        if filters:
            query["bool"]["filter"] = filters

        histogram: Dict[str, Any] = {
            "field": "date",
            "calendar_interval": interval,
            "min_doc_count": 0,
        }
        if start or end:
            bounds: Dict[str, str] = {}
            if start:
                bounds["min"] = start
            if end:
                bounds["max"] = end
            if bounds:
                histogram["extended_bounds"] = bounds

        channel_terms_size = max(6, len(channels)) if channels else 6

        body = {
            "size": 0,
            "query": query,
            "aggs": {
                "over_time": {
                    "date_histogram": histogram,
                    "aggs": {
                        "by_channel": {
                            "terms": {
                                "field": "channel_id.keyword",
                                "size": channel_terms_size,
                                "order": {"_count": "desc"},
                            }
                        }
                    },
                }
            },
        }

        if config.elastic.debug:
            LOGGER.info(
                "Elasticsearch frequency request: %s",
                json.dumps(
                    {
                        "index": index,
                        "body": body,
                        "term": term,
                        "interval": interval,
                        "channels": channels,
                        "start": start,
                        "end": end,
                        "query_string": use_query_string,
                    },
                    indent=2,
                ),
            )

        response = client.search(index=index, body=body)

        if config.elastic.debug:
            LOGGER.info(
                "Elasticsearch frequency response: %s",
                json.dumps(response, indent=2, default=str),
            )

        raw_buckets = (
            response.get("aggregations", {})
            .get("over_time", {})
            .get("buckets", [])
        )

        channel_totals: Dict[str, int] = {}
        buckets: List[Dict[str, Any]] = []
        for bucket in raw_buckets:
            date_str = bucket.get("key_as_string")
            total = bucket.get("doc_count", 0)
            channel_entries: List[Dict[str, Any]] = []
            for ch_bucket in bucket.get("by_channel", {}).get("buckets", []):
                cid = ch_bucket.get("key")
                count = ch_bucket.get("doc_count", 0)
                if cid:
                    channel_entries.append({"id": cid, "count": count})
                    channel_totals[cid] = channel_totals.get(cid, 0) + count
            buckets.append(
                {"date": date_str, "total": total, "channels": channel_entries}
            )

        ranked_channels = sorted(
            [{"id": cid, "total": total} for cid, total in channel_totals.items()],
            key=lambda item: item["total"],
            reverse=True,
        )

        payload = {
            "term": raw_term if not use_query_string else term,
            "interval": interval,
            "buckets": buckets,
            "channels": ranked_channels,
            "totalResults": response.get("hits", {})
            .get("total", {})
            .get("value", 0),
        }
        return jsonify(payload)

    @app.route("/frequency")
    def frequency_page():
        return send_from_directory(app.static_folder, "frequency.html")

    @app.route("/api/transcript")
    def transcript():
        video_id = request.args.get("video_id", type=str)
        if not video_id:
            return ("video_id not set", 400)
        response = client.get(index=index, id=video_id, ignore=[404])
        if config.elastic.debug:
            LOGGER.info(
                "Elasticsearch transcript request: index=%s id=%s", index, video_id
            )
            LOGGER.info(
                "Elasticsearch transcript response: %s",
                json.dumps(response, indent=2, default=str)
                if response
                else "None",
            )
        if not response or not response.get("found"):
            return ("not found", 404)
        source = response["_source"]
        return jsonify(
            {
                "video_id": source.get("video_id"),
                "title": source.get("title"),
                "transcript_parts": source.get("transcript_parts", []),
                "transcript_full": source.get("transcript_full"),
                "transcript_secondary_parts": source.get("transcript_secondary_parts", []),
                "transcript_secondary_full": source.get("transcript_secondary_full"),
            }
        )

    return app


def main() -> None:  # pragma: no cover
    logging.basicConfig(level=logging.INFO, format="%(levelname)s %(message)s")
    app = create_app()
    app.run(host="0.0.0.0", port=8080, debug=True)


if __name__ == "__main__":  # pragma: no cover
    main()