chrome_grammarly/spacyback/mainspacy.py

# -*- coding: utf-8 -*-
"""Grammar highlighter powered by spaCy + benepar constituency parsing."""

import asyncio
import html
import json
import re
from collections import Counter
from dataclasses import dataclass, field
from html.parser import HTMLParser
from string import Template
from typing import Any, Dict, List, Optional, Tuple
from urllib import error as urllib_error, request as urllib_request
from urllib.parse import urlparse, urlunparse

import benepar
import httpx
import spacy
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import HTMLResponse
from pydantic import BaseModel, Field
from spacy.cli import download as spacy_download
from spacy.language import Language
from spacy.tokens import Span as SpacySpan, Token as SpacyToken
from style_config import STYLE_BLOCK

BENE_PAR_WARNING: Optional[str] = None
HAS_BENEPAR: bool = False  # new: track whether benepar was successfully attached


def _ensure_benepar_warning(message: str) -> None:
    """Record a warning once when benepar annotations are unavailable."""
    global BENE_PAR_WARNING
    if not BENE_PAR_WARNING:
        BENE_PAR_WARNING = message


def _load_spacy_pipeline(
    model_name: str = "en_core_web_sm", benepar_model: str = "benepar_en3"
) -> Language:
    global BENE_PAR_WARNING, HAS_BENEPAR
    BENE_PAR_WARNING = None
    HAS_BENEPAR = False
    try:
        nlp = spacy.load(model_name)
    except OSError:
        try:
            spacy_download(model_name)
            nlp = spacy.load(model_name)
        except Exception as exc:  # pragma: no cover - install helper
            raise RuntimeError(
                f"spaCy model '{model_name}' is required. Install via `python -m spacy download {model_name}`."
            ) from exc

    # Ensure we have sentence segmentation available
    pipe_names = set(nlp.pipe_names)
    if not ({"parser", "senter", "sentencizer"} & pipe_names):
        try:
            nlp.add_pipe("sentencizer")
        except Exception:
            pass  # if already present or unavailable, ignore

    # Try to add benepar
    if "benepar" not in nlp.pipe_names:
        try:
            nlp.add_pipe("benepar", config={"model": benepar_model}, last=True)
            HAS_BENEPAR = True
        except ValueError:
            try:
                benepar.download(benepar_model)
                nlp.add_pipe("benepar", config={"model": benepar_model}, last=True)
                HAS_BENEPAR = True
            except Exception as exc:  # pragma: no cover - install helper
                HAS_BENEPAR = False
                BENE_PAR_WARNING = (
                    "Benepar model '{model}' unavailable ({err}). Falling back to dependency-based spans."
                ).format(model=benepar_model, err=exc)
        except Exception as exc:
            HAS_BENEPAR = False
            BENE_PAR_WARNING = (
                "Failed to attach benepar parser to spaCy pipeline. Falling back to dependency-based spans ({err})."
            ).format(err=exc)
    else:
        HAS_BENEPAR = True

    return nlp


try:
    NLP: Optional[Language] = _load_spacy_pipeline()
    NLP_LOAD_ERROR: Optional[Exception] = None
except Exception as exc:  # pragma: no cover - import-time diagnostics
    NLP = None
    NLP_LOAD_ERROR = exc


class AnalyzeRequest(BaseModel):
    text: str = Field(..., description="Raw English text to highlight")


class AnalyzeResponse(BaseModel):
    highlighted_html: str


@dataclass
class Token:
    text: str
    start: int
    end: int
    kind: str  # 'word' | 'space' | 'punct'


@dataclass
class Span:
    start_token: int
    end_token: int
    cls: str
    attrs: Optional[Dict[str, str]] = None


@dataclass
class SentenceSummary:
    subjects: List[str] = field(default_factory=list)
    predicates: List[str] = field(default_factory=list)
    objects: List[str] = field(default_factory=list)
    complements: List[str] = field(default_factory=list)
    clauses: List[str] = field(default_factory=list)
    clause_functions: List[str] = field(default_factory=list)
    connectors: List[str] = field(default_factory=list)
    residual_roles: List[str] = field(default_factory=list)
    sentence_length: int = 0


TOKEN_REGEX = re.compile(
    r"""
    (?:\s+)
    |(?:\d+(?:[\.,]\d+)*)
    |(?:\w+(?:[-']\w+)*)
    |(?:.)
    """,
    re.VERBOSE | re.UNICODE,
)

WORD_LIKE_RE = re.compile(r"\w+(?:[-']\w+)*\Z", re.UNICODE)
NUMBER_RE = re.compile(r"\d+(?:[\.,]\d+)*\Z", re.UNICODE)
PARAGRAPH_BREAK_RE = re.compile(r"(?:\r?\n[ \t]*){2,}")


SUBJECT_DEPS = {"nsubj", "nsubjpass", "csubj", "csubjpass"}
DIRECT_OBJECT_DEPS = {"dobj", "obj"}
INDIRECT_OBJECT_DEPS = {"iobj", "dative"}
COMPLEMENT_DEPS = {"attr", "oprd", "acomp", "ccomp", "xcomp"}
ADVERBIAL_DEPS = {"advmod", "npadvmod", "advcl", "obl", "prep", "pcomp"}
RELATIVE_PRONOUNS = {"which", "that", "who", "whom", "whose", "where", "when"}
SUBORDINATORS_TO_FUNCTION = {
    "when": "TIME",
    "while": "TIME",
    "after": "TIME",
    "before": "TIME",
    "until": "TIME",
    "as": "TIME",
    "once": "TIME",
    "since": "TIME",
    "because": "REASON",
    "now that": "REASON",
    "if": "CONDITION",
    "unless": "CONDITION",
    "provided": "CONDITION",
    "provided that": "CONDITION",
    "although": "CONCESSION",
    "though": "CONCESSION",
    "even though": "CONCESSION",
    "whereas": "CONCESSION",
    "so that": "RESULT",
    "so": "RESULT",
    "lest": "PURPOSE",
    "in order that": "PURPOSE",
}
FINITE_VERB_TAGS = {"VBD", "VBP", "VBZ"}
NONFINITE_VERB_TAGS = {"VBG", "VBN"}
CLAUSE_PREDICATE_DEPS = {
    "advcl",
    "ccomp",
    "xcomp",
    "acl",
    "relcl",
    "csubj",
    "csubjpass",
    "parataxis",
}
FIXED_MULTIWORD_PHRASES: Tuple[Tuple[re.Pattern, str], ...] = tuple(
    (
        re.compile(pattern, re.IGNORECASE),
        label,
    )
    for pattern, label in [
        (r"\bas well as\b", "as well as"),
        (r"\brather than\b", "rather than"),
        (r"\bin addition to\b", "in addition to"),
        (r"\bin spite of\b", "in spite of"),
        (r"\baccording to\b", "according to"),
        (r"\bas soon as\b", "as soon as"),
    ]
)
CLAUSE_FUNCTION_LABELS = {
    "TIME": "时间",
    "REASON": "原因",
    "CONDITION": "条件",
    "CONCESSION": "让步",
    "RESULT": "结果",
    "PURPOSE": "目的",
}


def _iter_infinitive_markers(token: SpacyToken) -> List[SpacyToken]:
    """Collect 'to' markers attached to a verb head."""
    markers = []
    for child in token.children:
        if child.lower_ == "to" and child.tag_ == "TO":
            markers.append(child)
    return markers


def _token_is_infinitive(token: SpacyToken) -> bool:
    if token.pos_ not in {"VERB", "AUX"}:
        return False
    verb_forms = set(token.morph.get("VerbForm"))
    if "Inf" not in verb_forms and token.tag_ != "VB":
        return False
    return bool(_iter_infinitive_markers(token))


def _token_is_gerund(token: SpacyToken) -> bool:
    if token.pos_ not in {"VERB", "AUX"}:
        return False
    verb_forms = set(token.morph.get("VerbForm"))
    if "Ger" in verb_forms:
        return True
    return token.tag_ == "VBG"


def _annotate_nonfinite_verbals(
    sentence: SpacySpan,
    spans: List[Span],
    mapping: Dict[int, int],
) -> None:
    """Highlight infinitive和gerund短语，帮助识别非限定动词。"""
    for token in sentence:
        if _token_is_infinitive(token):
            start_char, end_char = subtree_char_span(token)
            markers = _iter_infinitive_markers(token)
            if markers:
                start_char = min(start_char, min(child.idx for child in markers))
            add_char_based_span(
                spans,
                start_char,
                end_char,
                "verbal-infinitive",
                mapping,
                attrs={"data-form": "不定式"},
            )
    seen_gerunds = set()
    for token in sentence:
        if token.i in seen_gerunds:
            continue
        if _token_is_gerund(token):
            start_char, end_char = subtree_char_span(token)
            add_char_based_span(
                spans,
                start_char,
                end_char,
                "verbal-gerund",
                mapping,
                attrs={"data-form": "动名词"},
            )
            seen_gerunds.add(token.i)
RESIDUAL_DEP_LABELS = {
    "det": "限定词",
    "prep": "介词",
    "case": "介词标记",
    "cc": "并列连词",
    "mark": "从属连词",
    "poss": "所有格标记",
    "nummod": "数量修饰语",
    "aux": "助动词",
    "prt": "小品词",
}
RESIDUAL_POS_LABELS = {
    "ADJ": "形容词修饰语",
    "ADV": "副词",
    "NUM": "数词",
    "PRON": "代词",
}


def _classify_segment(seg: str) -> str:
    if not seg:
        return "punct"
    if seg.isspace():
        return "space"
    if NUMBER_RE.fullmatch(seg) or WORD_LIKE_RE.fullmatch(seg):
        return "word"
    return "punct"


def _append_fallback_tokens(text: str, start: int, end: int, tokens: List[Token]) -> None:
    for idx in range(start, end):
        ch = text[idx]
        if ch.isspace():
            kind = "space"
        elif ch.isalnum() or ch == "_":
            kind = "word"
        else:
            kind = "punct"
        tokens.append(Token(ch, idx, idx + 1, kind))


def tokenize_preserve(text: str) -> List[Token]:
    tokens: List[Token] = []
    if not text:
        return tokens

    last_end = 0
    for match in TOKEN_REGEX.finditer(text):
        if match.start() > last_end:
            _append_fallback_tokens(text, last_end, match.start(), tokens)
        seg = text[match.start() : match.end()]
        tokens.append(Token(seg, match.start(), match.end(), _classify_segment(seg)))
        last_end = match.end()

    if last_end < len(text):
        _append_fallback_tokens(text, last_end, len(text), tokens)

    if not tokens and text:
        tokens = [Token(text, 0, len(text), "word" if text[0].isalnum() else "punct")]
    return tokens


def build_char_to_token_map(tokens: List[Token]) -> Dict[int, int]:
    mapping: Dict[int, int] = {}
    for idx, tok in enumerate(tokens):
        for pos in range(tok.start, tok.end):
            mapping[pos] = idx
    return mapping


def char_span_to_token_span(
    char_start: int, char_end: int, mapping: Dict[int, int]
) -> Tuple[int, int]:
    if char_end <= char_start:
        return -1, -1
    start_idx = mapping.get(char_start)
    end_idx = mapping.get(char_end - 1)
    if start_idx is None or end_idx is None:
        return -1, -1
    return start_idx, end_idx + 1


def add_char_based_span(
    spans: List[Span],
    char_start: int,
    char_end: int,
    cls: str,
    mapping: Dict[int, int],
    attrs: Optional[Dict[str, str]] = None,
) -> None:
    s_tok, e_tok = char_span_to_token_span(char_start, char_end, mapping)
    if s_tok < 0 or e_tok < 0:
        return
    safe_attrs = None
    if attrs:
        safe_attrs = {k: html.escape(v, quote=True) for k, v in attrs.items() if v}
    spans.append(Span(start_token=s_tok, end_token=e_tok, cls=cls, attrs=safe_attrs))


def add_span(spans: List[Span], start_token: int, end_token: int, cls: str, attrs: Optional[Dict[str, str]] = None):
    if start_token < 0 or end_token < 0 or end_token <= start_token:
        return
    spans.append(Span(start_token=start_token, end_token=end_token, cls=cls, attrs=attrs))


def subtree_char_span(token: SpacyToken) -> Tuple[int, int]:
    subtree = list(token.subtree)
    if not subtree:
        return token.idx, token.idx + len(token.text)
    return subtree[0].idx, subtree[-1].idx + len(subtree[-1].text)


def _subtree_text(token: SpacyToken) -> str:
    span = token.doc[token.left_edge.i : token.right_edge.i + 1]
    return span.text


def _find_antecedent_word(sentence: SpacySpan, clause_start_char: int) -> Optional[str]:
    candidate = None
    for tok in sentence:
        if tok.idx >= clause_start_char:
            break
        if tok.pos_ in {"NOUN", "PROPN", "PRON"}:
            candidate = tok.text
    return candidate


def _is_nonfinite_clause(span: SpacySpan) -> bool:
    tags = {tok.tag_ for tok in span if tok.tag_}
    if tags & FINITE_VERB_TAGS:
        return False
    if "TO" in tags or tags & NONFINITE_VERB_TAGS:
        return True
    return False


def _classify_noun_clause(span: SpacySpan) -> Optional[str]:
    deps = {tok.dep_ for tok in span}
    if deps & {"csubj", "csubjpass"}:
        return "subject"
    if deps & {"ccomp", "xcomp"}:
        return "complement"
    if deps & {"dobj", "obj"}:
        return "object"
    return None


def _split_paragraph_ranges(text: str) -> List[Tuple[int, int]]:
    """Return inclusive paragraph ranges, keeping separators intact."""
    if not text:
        return [(0, 0)]
    ranges: List[Tuple[int, int]] = []
    start = 0
    for match in PARAGRAPH_BREAK_RE.finditer(text):
        ranges.append((start, match.start()))
        start = match.end()
    ranges.append((start, len(text)))
    # Ensure at least one range and sorted order
    if not ranges:
        ranges = [(0, len(text))]
    return ranges


def _circled_number(value: int) -> str:
    """Return the circled number style for sentence numbering."""
    if value <= 0:
        return ""
    if value <= 20:
        return chr(ord("\u2460") + value - 1)
    if 21 <= value <= 35:
        return chr(ord("\u3251") + value - 21)
    if 36 <= value <= 50:
        return chr(ord("\u32B1") + value - 36)
    return f"({value})"


def annotate_constituents(
    sentence: SpacySpan,
    spans: List[Span],
    mapping: Dict[int, int],
    sentence_start_char: int,
    sentence_end_char: int,
    summary: Optional[SentenceSummary] = None,
) -> None:
    # If benepar is not attached or a previous warning indicates fallback, skip.
    if not HAS_BENEPAR or BENE_PAR_WARNING:
        _ensure_benepar_warning(
            "Benepar component missing or unavailable. Using dependency-based spans."
        )
        return

    # If the extension is not present, skip
    if not SpacySpan.has_extension("constituents"):
        _ensure_benepar_warning(
            "Benepar component missing from spaCy pipeline. Falling back to dependency spans."
        )
        return
    try:
        constituents = sentence._.constituents
    except Exception as exc:
        # Catch any error while accessing benepar results and fallback safely
        _ensure_benepar_warning(
            f"Benepar constituency parse unavailable: {exc}. Falling back to dependency spans."
        )
        return

    seen_ranges = set()
    for const in constituents:
        label = getattr(const, "label_", None)
        if not label:
            continue
        start_char, end_char = const.start_char, const.end_char
        if start_char == sentence_start_char and end_char == sentence_end_char:
            continue  # skip the entire sentence span itself

        key = (start_char, end_char, label)
        is_relative = False

        if label in {"PP", "ADVP"}:
            if key in seen_ranges:
                continue
            seen_ranges.add(key)
            add_char_based_span(spans, start_char, end_char, "role-adverbial", mapping)
            continue

        if label == "SBAR" and const:
            first_token = const[0]
            lowered = first_token.text.lower()
            if lowered in RELATIVE_PRONOUNS:
                antecedent = _find_antecedent_word(sentence, start_char)
                attrs = {"data-modifies": antecedent} if antecedent else None
                add_char_based_span(spans, start_char, end_char, "clause-relative", mapping, attrs)
                if summary:
                    summary.clauses.append("定语从句")
                is_relative = True
            else:
                function = SUBORDINATORS_TO_FUNCTION.get(lowered)
                attrs = {"data-function": function}
                add_char_based_span(spans, start_char, end_char, "clause-adverbial", mapping, attrs)
                if summary:
                    summary.clauses.append("状语从句")
                    if function:
                        summary.clause_functions.append(function)
            continue

        if label in {"S", "VP"}:
            if _is_nonfinite_clause(const):
                add_char_based_span(spans, start_char, end_char, "clause-nonfinite", mapping)
                if summary:
                    summary.clauses.append("非限定结构")
                continue
            if label == "S" and not is_relative:
                role = _classify_noun_clause(const)
                if role:
                    attrs = {"data-clause-role": role}
                    add_char_based_span(spans, start_char, end_char, "clause-noun", mapping, attrs)
                    if summary:
                        summary.clauses.append(f"名词性从句({role})")


def _predicate_span_bounds(head: SpacyToken) -> Tuple[int, int]:
    """Return a character range covering predicate head + functional dependents."""
    tokens = [head]
    for child in head.children:
        if child.dep_ in {"aux", "auxpass", "prt", "cop", "neg"}:
            tokens.append(child)
    start_char = min(tok.idx for tok in tokens)
    end_char = max(tok.idx + len(tok.text) for tok in tokens)
    return start_char, end_char


def _token_is_finite(token: SpacyToken) -> bool:
    """Return True if token carries finite verb morphology."""
    if token.pos_ not in {"VERB", "AUX"}:
        return False
    verb_forms = set(token.morph.get("VerbForm"))
    if "Fin" in verb_forms or "Imp" in verb_forms:
        return True
    if token.tag_ in FINITE_VERB_TAGS or token.tag_ == "MD":
        return True
    return False


def _has_finite_auxiliary(token: SpacyToken) -> bool:
    """Detect whether the verb head has a finite auxiliary helper."""
    for child in token.children:
        if child.dep_ in {"aux", "auxpass", "cop"} and _token_is_finite(child):
            return True
    return False


def _is_finite_predicate_head(token: SpacyToken) -> bool:
    """Filter predicate heads to exclude bare infinitives/participles."""
    if _token_is_finite(token):
        return True
    verb_forms = set(token.morph.get("VerbForm"))
    if "Inf" in verb_forms:
        return _has_finite_auxiliary(token)
    if verb_forms & {"Part", "Ger"}:
        return _has_finite_auxiliary(token)
    if token.tag_ in NONFINITE_VERB_TAGS:
        return _has_finite_auxiliary(token)
    if token.tag_ == "VB":
        has_to_marker = any(
            child.dep_ == "mark" and child.lower_ == "to" for child in token.children
        )
        if has_to_marker:
            return False
        return token.dep_ == "ROOT"
    return False


def _predicate_heads(sentence: SpacySpan) -> List[SpacyToken]:
    """Collect predicate heads including coordinated verbs."""
    candidates: List[SpacyToken] = []
    for tok in sentence:
        if tok.pos_ not in {"VERB", "AUX"} and tok.tag_ not in FINITE_VERB_TAGS:
            continue
        if tok.dep_ == "ROOT":
            candidates.append(tok)
            continue
        if tok.dep_ == "conj" and tok.head.pos_ in {"VERB", "AUX"}:
            candidates.append(tok)
            continue
        if tok.dep_ in {"ccomp", "xcomp", "advcl", "acl", "relcl", "parataxis"}:
            candidates.append(tok)
    seen = set()
    ordered: List[SpacyToken] = []
    for tok in sorted(candidates, key=lambda t: t.i):
        if tok.i in seen:
            continue
        seen.add(tok.i)
        if _is_finite_predicate_head(tok):
            ordered.append(tok)
    return ordered


def _is_clause_predicate(token: SpacyToken) -> bool:
    """Return True if predicate originates inside从句."""
    if token.dep_ in CLAUSE_PREDICATE_DEPS:
        return True
    if token.dep_ != "conj":
        return False
    ancestor = token.head
    safety = 0
    while ancestor is not None and safety < 10:
        if ancestor.dep_ in CLAUSE_PREDICATE_DEPS:
            return True
        if ancestor.dep_ != "conj" or ancestor.head is ancestor:
            break
        ancestor = ancestor.head
        safety += 1
    return False


def _add_fixed_phrases(
    sentence: SpacySpan,
    mapping: Dict[int, int],
    spans: List[Span],
    summary: Optional[SentenceSummary] = None,
) -> None:
    base = sentence.start_char
    text = sentence.text
    for pattern, label in FIXED_MULTIWORD_PHRASES:
        for match in pattern.finditer(text):
            start_char = base + match.start()
            end_char = base + match.end()
            add_char_based_span(
                spans,
                start_char,
                end_char,
                "phrase-fixed",
                mapping,
                attrs={"data-phrase": label},
            )
            if summary is not None:
                summary.connectors.append(label.lower())


def annotate_sentence(
    tokens: List[Token],
    sentence: SpacySpan,
    mapping: Dict[int, int],
    collect_summary: bool = True,
) -> Tuple[List[Span], Optional[SentenceSummary]]:
    spans: List[Span] = []
    summary = SentenceSummary(sentence_length=len(sentence)) if collect_summary else None
    sent_bounds = char_span_to_token_span(sentence.start_char, sentence.end_char, mapping)
    sent_start_tok, sent_end_tok = sent_bounds

    def add_subtree(token: SpacyToken, cls: str):
        start_char, end_char = subtree_char_span(token)
        add_char_based_span(spans, start_char, end_char, cls, mapping)

    def add_token(token: SpacyToken, cls: str):
        add_char_based_span(spans, token.idx, token.idx + len(token.text), cls, mapping)

    for tok in sentence:
        if tok.dep_ in SUBJECT_DEPS:
            add_subtree(tok, "role-subject")
            if summary is not None:
                summary.subjects.append(_subtree_text(tok))

    for head in _predicate_heads(sentence):
        start_char, end_char = _predicate_span_bounds(head)
        cls = "role-predicate"
        if _is_clause_predicate(head):
            cls = "role-predicate role-predicate-clause"
        add_char_based_span(spans, start_char, end_char, cls, mapping)
        predicate_text = sentence.doc.text[start_char:end_char].strip()
        if summary is not None:
            summary.predicates.append(predicate_text or head.text)

    for tok in sentence:
        if tok.dep_ in DIRECT_OBJECT_DEPS:
            add_subtree(tok, "role-object-do")
            if summary is not None:
                summary.objects.append(_subtree_text(tok))
            break

    io_token = next((tok for tok in sentence if tok.dep_ in INDIRECT_OBJECT_DEPS), None)
    if io_token is None:
        for tok in sentence:
            if tok.dep_ == "pobj" and tok.head.dep_ == "prep" and tok.head.lemma_.lower() in {"to", "for"}:
                io_token = tok
                break
    if io_token:
        add_subtree(io_token, "role-object-io")
        if summary is not None:
            summary.objects.append(_subtree_text(io_token))

    for tok in sentence:
        if tok.dep_ in COMPLEMENT_DEPS:
            add_subtree(tok, "role-complement")
            if summary is not None:
                summary.complements.append(_subtree_text(tok))
            break

    for tok in sentence:
        lowered = tok.text.lower()
        if tok.dep_ in {"cc", "mark", "preconj"} or tok.pos_ in {"CCONJ", "SCONJ"}:
            add_token(tok, "role-connector")
            if summary is not None:
                summary.connectors.append(lowered)
        if tok.dep_ == "det" or tok.pos_ == "DET":
            add_token(tok, "role-determiner")
        if tok.dep_ in {"amod", "poss", "compound", "nummod"}:
            add_token(tok, "role-modifier")

    adverbial_ranges = set()
    for tok in sentence:
        if tok.dep_ in ADVERBIAL_DEPS:
            adverbial_ranges.add(subtree_char_span(tok))
    for start_char, end_char in adverbial_ranges:
        add_char_based_span(spans, start_char, end_char, "role-adverbial", mapping)

    for tok in sentence:
        if tok.dep_ == "appos":
            add_subtree(tok, "role-apposition")

    if sent_start_tok >= 0 and sent_end_tok >= 0:
        stack = []
        for idx in range(sent_start_tok, sent_end_tok):
            token = tokens[idx]
            if token.text == "(":
                stack.append(idx)
            elif token.text == ")" and stack:
                add_span(spans, stack.pop(), idx + 1, "role-parenthetical")

        comma_token_idxs = [
            i
            for i in range(sent_start_tok, sent_end_tok)
            if tokens[i].kind == "punct" and tokens[i].text == ","
        ]
        for idx, first_comma in enumerate(comma_token_idxs):
            if idx + 1 >= len(comma_token_idxs):
                break
            second_comma = comma_token_idxs[idx + 1]
            start_char = tokens[first_comma].start
            end_char = tokens[second_comma].end
            span = sentence.doc.char_span(start_char, end_char, alignment_mode="expand")
            if span and any(tok.tag_ == "VBG" for tok in span):
                add_span(spans, first_comma, second_comma + 1, "role-absolute")

    _annotate_nonfinite_verbals(sentence, spans, mapping)
    annotate_constituents(
        sentence,
        spans,
        mapping,
        sentence.start_char,
        sentence.end_char,
        summary,
    )
    _add_fixed_phrases(sentence, mapping, spans, summary)

    return spans, summary


def _label_residual_token(token: SpacyToken) -> Optional[str]:
    dep_label = RESIDUAL_DEP_LABELS.get(token.dep_)
    if dep_label:
        return dep_label
    return RESIDUAL_POS_LABELS.get(token.pos_)


def _collect_residual_roles(
    sentence: SpacySpan,
    tokens: List[Token],
    spans: List[Span],
    sent_bounds: Tuple[int, int],
    summary: Optional[SentenceSummary],
    mapping: Dict[int, int],
) -> None:
    sent_start, sent_end = sent_bounds
    if sent_start < 0 or sent_end < 0 or sent_start >= sent_end:
        return
    coverage = [False] * (sent_end - sent_start)
    for span in spans:
        lo = max(span.start_token, sent_start)
        hi = min(span.end_token, sent_end)
        for idx in range(lo, hi):
            coverage[idx - sent_start] = True
    doc = sentence.doc
    for offset, covered in enumerate(coverage):
        if covered:
            continue
        token = tokens[sent_start + offset]
        if token.kind != "word":
            continue
        span = doc.char_span(token.start, token.end, alignment_mode="expand")
        if not span or not span.text.strip():
            continue
        label = _label_residual_token(span[0])
        if summary is not None and label and label not in summary.residual_roles:
            summary.residual_roles.append(label)
        if label:
            add_char_based_span(
                spans,
                token.start,
                token.end,
                "role-residual",
                mapping,
                attrs={"data-role": label},
            )


def _classify_sentence_complexity(summary: SentenceSummary) -> Tuple[str, bool]:
    clause_count = len(summary.clauses)
    connector_count = len(summary.connectors)
    word_count = summary.sentence_length
    if clause_count >= 2:
        return "多重复杂句", True
    if clause_count == 1:
        return "主从复合句", True
    if connector_count >= 2:
        return "并列复合句", True
    if word_count >= 25:
        return "长句", True
    return "简单句", False


def _translate_clause_functions(functions: List[str]) -> List[str]:
    translated = []
    for item in functions:
        label = CLAUSE_FUNCTION_LABELS.get(item, item)
        if label not in translated:
            translated.append(label)
    return translated


def build_sentence_note(summary: SentenceSummary) -> Tuple[str, bool]:
    note_parts: List[str] = []
    clause_label = "无"
    if summary.clauses:
        counts = Counter(summary.clauses)
        clause_label = "、".join(
            f"{name}×{count}" if count > 1 else name for name, count in counts.items()
        )
    functions = _translate_clause_functions(summary.clause_functions)
    connectors = list(dict.fromkeys(summary.connectors))
    residual = summary.residual_roles
    subjects_seq = list(dict.fromkeys(summary.subjects))
    predicates_seq = list(dict.fromkeys(summary.predicates))
    objects_seq = list(dict.fromkeys(summary.objects))
    complements_seq = list(dict.fromkeys(summary.complements))
    subjects = "、".join(subjects_seq) if subjects_seq else "未识别"
    predicates = "、".join(predicates_seq) if predicates_seq else "未识别"
    objects = "、".join(objects_seq) if objects_seq else "无"
    complements = "、".join(complements_seq) if complements_seq else "无"
    note_parts.append(f"主语：{subjects}")
    note_parts.append(f"谓语：{predicates}")
    note_parts.append(f"宾语：{objects}")
    if complements != "无":
        note_parts.append(f"补语：{complements}")
    note_parts.append(f"从句：{clause_label}")
    if functions:
        note_parts.append(f"从句功能：{'、'.join(functions)}")
    connector_text = "、".join(connectors) if connectors else "未检测到典型连接词"
    note_parts.append(f"连接词：{connector_text}")
    if residual:
        note_parts.append(f"未高亮：{'、'.join(residual)}")
    complexity_label, is_complex = _classify_sentence_complexity(summary)
    note_parts.insert(0, f"句型：{complexity_label}")
    note_parts.append(f"词数：{summary.sentence_length}")
    return "；".join(note_parts), is_complex


def render_with_spans(tokens: List[Token], spans: List[Span]) -> str:
    spans = sorted(spans, key=lambda s: (s.start_token, -s.end_token))
    out_parts: List[str] = []
    active_stack: List[Span] = []
    span_queue = list(spans)
    current_idx = 0

    def open_span(span: Span):
        attrs = ""
        if span.attrs:
            attrs = " " + " ".join(
                f"{k}='" + html.escape(v, quote=True) + "'" for k, v in span.attrs.items()
            )
        out_parts.append(f"<span class='{span.cls}'{attrs}>")

    def close_span():
        out_parts.append("</span>")

    while current_idx < len(tokens):
        opening = [sp for sp in span_queue if sp.start_token == current_idx]
        for sp in opening:
            open_span(sp)
            active_stack.append(sp)
            span_queue.remove(sp)

        token = tokens[current_idx]
        out_parts.append(html.escape(token.text))
        current_idx += 1

        while active_stack and active_stack[-1].end_token == current_idx:
            active_stack.pop()
            close_span()

    while active_stack:
        active_stack.pop()
        close_span()

    return "".join(out_parts)


def _run_pipeline_without_benepar(text: str) -> "spacy.tokens.Doc":
    """Run the spaCy pipeline skipping benepar, for robust fallback."""
    assert NLP is not None
    doc = NLP.make_doc(text)
    for name, proc in NLP.pipeline:
        if name == "benepar":
            continue
        doc = proc(doc)
    return doc


def highlight_text_with_spacy(
    text: str,
    paragraph_meta: Optional[List[Dict[str, str]]] = None,
    include_helper: bool = False,
    paragraph_ranges: Optional[List[Tuple[int, int]]] = None,
) -> str:
    if NLP is None:
        raise RuntimeError(f"spaCy pipeline unavailable: {NLP_LOAD_ERROR}")
    tokens = tokenize_preserve(text)
    if not tokens:
        return ""
    mapping = build_char_to_token_map(tokens)

    # Robust doc creation: if benepar causes any error, skip it and fallback.
    try:
        doc = NLP(text)
    except Exception as exc:
        _ensure_benepar_warning(
            f"Benepar failed during processing: {exc}. Falling back to dependency-based spans."
        )
        doc = _run_pipeline_without_benepar(text)

    ranges = None
    if paragraph_ranges:
        valid = True
        for start, end in paragraph_ranges:
            if start < 0 or end < start or end > len(text):
                valid = False
                break
        if valid:
            ranges = list(paragraph_ranges)
    if not ranges:
        ranges = _split_paragraph_ranges(text)
    paragraph_counters = [0 for _ in ranges]
    paragraph_idx = 0
    paragraph_spans: List[Span] = []
    paragraph_attrs = paragraph_meta if paragraph_meta and len(paragraph_meta) == len(ranges) else None
    for idx, (start, end) in enumerate(ranges):
        attrs = None
        if paragraph_attrs:
            attrs = paragraph_attrs[idx] or None
        add_char_based_span(paragraph_spans, start, end, "paragraph-scope", mapping, attrs=attrs)

    spans: List[Span] = list(paragraph_spans)

    for sent in doc.sents:
        while paragraph_idx < len(ranges) and ranges[paragraph_idx][1] <= sent.start_char:
            paragraph_idx += 1
        current_idx = min(paragraph_idx, len(ranges) - 1)
        paragraph_counters[current_idx] += 1
        sentence_label = _circled_number(paragraph_counters[current_idx])

        sentence_spans, summary = annotate_sentence(tokens, sent, mapping, collect_summary=include_helper)
        sent_bounds = char_span_to_token_span(sent.start_char, sent.end_char, mapping)
        sent_start, sent_end = sent_bounds
        if sent_start >= 0 and sent_end >= 0:
            _collect_residual_roles(sent, tokens, sentence_spans, sent_bounds, summary, mapping)
            helper_note = ""
            is_complex = False
            if include_helper and summary is not None:
                helper_note, is_complex = build_sentence_note(summary)
            attrs = {
                "data-sid": sentence_label,
            }
            if include_helper:
                attrs["data-complex"] = "1" if is_complex else "0"
                attrs["data-note"] = helper_note
            sentence_spans.append(Span(start_token=sent_start, end_token=sent_end, cls="sentence-scope", attrs=attrs))
        spans.extend(sentence_spans)
    return render_with_spans(tokens, spans)


def _build_analysis_container(fragment: str, include_helper: bool) -> str:
    helper_state = "on" if include_helper else "off"
    return f"<div class='analysis' data-helper='{helper_state}'>{fragment}</div>"


def _build_highlighted_html(fragment: str, include_helper: bool) -> str:
    return f"{STYLE_BLOCK}{_build_analysis_container(fragment, include_helper)}"


def _perform_analysis(text: str, include_helper: bool) -> AnalyzeResponse:
    sanitized_fragment = highlight_text_with_spacy(text, include_helper=include_helper)
    highlighted_html = _build_highlighted_html(sanitized_fragment, include_helper)
    return AnalyzeResponse(highlighted_html=highlighted_html)


app = FastAPI(title="Grammar Highlight API (spaCy + benepar)")
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


@app.post("/analyze", response_model=AnalyzeResponse)
async def analyze(req: AnalyzeRequest):
    text = req.text
    if text is None or not text.strip():
        raise HTTPException(status_code=400, detail="Text is required")
    try:
        return _perform_analysis(text, include_helper=False)
    except RuntimeError as exc:
        raise HTTPException(status_code=500, detail=str(exc)) from exc
    except Exception as exc:  # pragma: no cover - defensive
        raise HTTPException(status_code=500, detail=f"Analysis failed: {exc}") from exc


@app.post("/analyze/detail", response_model=AnalyzeResponse)
async def analyze_with_helper(req: AnalyzeRequest):
    text = req.text
    if text is None or not text.strip():
        raise HTTPException(status_code=400, detail="Text is required")
    try:
        return _perform_analysis(text, include_helper=True)
    except RuntimeError as exc:
        raise HTTPException(status_code=500, detail=str(exc)) from exc
    except Exception as exc:  # pragma: no cover - defensive
        raise HTTPException(status_code=500, detail=f"Analysis failed: {exc}") from exc


@app.get("/health")
async def health():
    status = "ok" if NLP is not None else "failed"
    detail = None if NLP is not None else str(NLP_LOAD_ERROR)
    payload = {"status": status}
    if detail:
        payload["detail"] = detail
    if BENE_PAR_WARNING:
        payload["warning"] = BENE_PAR_WARNING
    payload["benepar_attached"] = HAS_BENEPAR
    return payload


@app.get("/proxy", response_class=HTMLResponse)
async def proxy(url: Optional[str] = None, show_images: bool = False):
    if not url:
        return HTMLResponse(_render_proxy_page(show_images=show_images))
    try:
        (
            normalized_url,
            title,
            page_text,
            images,
            code_blocks,
            paragraph_meta,
            paragraph_ranges,
        ) = await _fetch_remote_plaintext(url)
        highlighted_fragment = highlight_text_with_spacy(
            page_text,
            paragraph_meta=paragraph_meta or None,
            paragraph_ranges=paragraph_ranges or None,
        )
        if code_blocks:
            highlighted_fragment = _inject_proxy_codeblocks(highlighted_fragment, code_blocks)
        image_notice = None
        if images:
            if show_images:
                highlighted_fragment = _inject_proxy_images(highlighted_fragment, images)
            else:
                highlighted_fragment = _strip_proxy_image_markers(highlighted_fragment)
                image_notice = (
                    f"检测到 {len(images)} 张正文图片，为提速默认隐藏。勾选“显示图片”后重新抓取即可加载原图。"
                )
        html_body = _render_proxy_page(
            url_value=normalized_url,
            message="分析完成，结果如下。",
            highlight_fragment=highlighted_fragment,
            source_url=normalized_url,
            source_title=title,
            show_images=show_images,
            image_notice=image_notice,
            source_plaintext=page_text,
        )
        return HTMLResponse(html_body)
    except ValueError as exc:
        body = _render_proxy_page(url_value=url or "", message=str(exc), is_error=True, show_images=show_images)
        return HTMLResponse(body, status_code=400)
    except httpx.HTTPError as exc:
        # Provide a clearer message for common HTTP errors from the remote site.
        msg = None
        if isinstance(exc, httpx.HTTPStatusError) and exc.response is not None:
            status = exc.response.status_code
            if status == 403:
                msg = (
                    "抓取页面失败：目标站点返回 403 Forbidden（禁止访问）。"
                    "该网站很可能禁止自动抓取或代理访问，目前无法通过本工具获取正文，"
                    "可以尝试在浏览器中打开并手动复制需要的内容。"
                )
            else:
                msg = f"抓取页面失败：目标站点返回 HTTP {status}。"
        if msg is None:
            msg = f"抓取页面失败：{exc}"
        body = _render_proxy_page(
            url_value=url or "",
            message=msg,
            is_error=True,
            show_images=show_images,
        )
        return HTMLResponse(body, status_code=502)
    except Exception as exc:
        body = _render_proxy_page(
            url_value=url or "",
            message=f"代理分析失败：{exc}",
            is_error=True,
            show_images=show_images,
        )
        return HTMLResponse(body, status_code=500)


@app.get("/", response_class=HTMLResponse)
async def ui():
    return """<!DOCTYPE html>
<html lang=\"zh-CN\">
<head>
<meta charset=\"UTF-8\" />
<meta name=\"viewport\" content=\"width=device-width, initial-scale=1\" />
<title>Grammar Highlighter</title>
<style>
body { font-family: system-ui, -apple-system, sans-serif; margin: 2rem; line-height: 1.6; }
textarea { width: 100%; min-height: 140px; font-size: 1rem; padding: 0.75rem; border: 1px solid #d0d7de; border-radius: 0.5rem; }
button { margin-top: 0.75rem; padding: 0.6rem 1.4rem; font-size: 1rem; cursor: pointer; border: none; border-radius: 999px; background: #1f7a8c; color: #fff; }
button + button { margin-left: 0.5rem; background: #6b7280; }
button:disabled { opacity: 0.6; cursor: wait; }
#result { margin-top: 1.5rem; border-top: 1px solid #e5e7eb; padding-top: 1rem; min-height: 2rem; }
#status { margin-left: 0.75rem; color: #3b82f6; }
.err { color: #b00020; }
.muted { color: #6b7280; font-size: 0.9rem; }
.tts-controls { margin-top: 0.75rem; display: flex; align-items: center; gap: 0.75rem; flex-wrap: wrap; }
.tts-controls button { margin-top: 0; background: #f97316; }
.tts-status { font-size: 0.95rem; color: #475569; }
.sentence-scope.anchor-highlight { outline: 2px dashed #f97316; outline-offset: 2px; }
</style>
</head>
<body>
<h1>Grammar Highlighter (spaCy + benepar)</h1>
<textarea id=\"text\" placeholder=\"Type the English text you want to analyze...\"></textarea>
<div>
<button type=\"button\" id=\"submit\">Analyze</button>
<button type=\"button\" id=\"clear\">清空输入</button>
<span id=\"status\"></span>
</div>
<div class=\"tts-controls\">
<button type=\"button\" id=\"tts\">朗读高亮文本</button>
<button type=\"button\" id=\"tts-selection\">朗读选中文本</button>
<button type=\"button\" id=\"tts-anchor\" disabled>从点击处朗读</button>
<button type=\"button\" id=\"tts-toggle\" disabled>暂停播放</button>
<span class=\"tts-status\" id=\"tts-status\"></span>
</div>
<div id=\"result\"></div>

<script>
const btn = document.getElementById('submit');
const btnClear = document.getElementById('clear');
const textarea = document.getElementById('text');
const statusEl = document.getElementById('status');
const ttsBtn = document.getElementById('tts');
const ttsSelectionBtn = document.getElementById('tts-selection');
const ttsAnchorBtn = document.getElementById('tts-anchor');
const ttsToggleBtn = document.getElementById('tts-toggle');
const ttsStatus = document.getElementById('tts-status');
const result = document.getElementById('result');
const TTS_ENDPOINT = 'http://141.140.15.30:8028/generate';
let currentAudio = null;
let queuedAudios = [];
let streamingFinished = false;
let lastAnalyzedText = '';
let anchorSentenceIndex = 0;
let isPaused = false;
let hasHighlightContent = false;

function resetUI() {
  result.innerHTML = '';
  statusEl.textContent = '';
  statusEl.classList.remove('err');
  ttsStatus.textContent = '';
  hasHighlightContent = false;
  if (ttsAnchorBtn) {
    ttsAnchorBtn.disabled = true;
  }
  resetAnchorState();
  setTtsButtonsDisabled(false);
  resetAudioPlayback();
}

function getSentenceNodes() {
  const analysis = result.querySelector('.analysis');
  return analysis ? Array.from(analysis.querySelectorAll('.sentence-scope')) : [];
}

function clearAnchorHighlight() {
  const highlighted = result.querySelectorAll('.sentence-scope.anchor-highlight');
  highlighted.forEach(el => el.classList.remove('anchor-highlight'));
}

function resetAnchorState() {
  anchorSentenceIndex = 0;
  clearAnchorHighlight();
}

function setAnchorFromSentence(sentenceEl) {
  const sentences = getSentenceNodes();
  const idx = sentences.indexOf(sentenceEl);
  if (idx === -1) return;
  anchorSentenceIndex = idx;
  clearAnchorHighlight();
  sentenceEl.classList.add('anchor-highlight');
  const sid = sentenceEl.getAttribute('data-sid') || (idx + 1);
  ttsStatus.textContent = '已选择第 ' + sid + ' 句作为朗读起点';
}

btn.addEventListener('click', async () => {
  resetUI();
  const value = textarea.value.trim();
  if (!value) {
    statusEl.textContent = '请输入要分析的英文文本。';
    statusEl.classList.add('err');
    return;
  }

  btn.disabled = true;
  statusEl.textContent = 'Analyzing ...';

  try {
    const response = await fetch('/analyze', {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({ text: value })
    });

    if (!response.ok) {
      const error = await response.json().catch(() => ({ detail: 'Request failed' }));
      throw new Error(error.detail || 'Request failed');
    }

    const data = await response.json();
    result.innerHTML = data.highlighted_html || '';
    lastAnalyzedText = value;
    resetAnchorState();
    hasHighlightContent = true;
    if (ttsAnchorBtn) {
      ttsAnchorBtn.disabled = false;
    }
    statusEl.textContent = '';
  } catch (err) {
    statusEl.textContent = '错误：' + (err.message || 'Unknown error');
    statusEl.classList.add('err');
  } finally {
    btn.disabled = false;
  }
});

btnClear.addEventListener('click', () => {
  textarea.value = '';
  lastAnalyzedText = '';
  resetUI();
  textarea.focus();
});

result.addEventListener('click', event => {
  if (!hasHighlightContent) {
    return;
  }
  const target = event.target;
  const isTextNode = typeof Node !== 'undefined' && target && target.nodeType === Node.TEXT_NODE;
  const base = isTextNode ? target.parentElement : target;
  if (!base || typeof base.closest !== 'function') {
    return;
  }
  const sentenceEl = base.closest('.sentence-scope');
  if (sentenceEl) {
    setAnchorFromSentence(sentenceEl);
  }
});

function extractHighlightedText() {
  const highlightRoot = result.querySelector('.analysis');
  return highlightRoot ? highlightRoot.textContent.trim() : '';
}

function getFullTextForTts() {
  return lastAnalyzedText || extractHighlightedText();
}

function extractAnchorText() {
  const sentences = getSentenceNodes();
  if (!sentences.length) return '';
  const start = Math.min(anchorSentenceIndex, sentences.length - 1);
  const parts = [];
  for (let i = start; i < sentences.length; i++) {
    const text = sentences[i].textContent.trim();
    if (text) {
      parts.push(text);
    }
  }
  return parts.join(' ');
}

function setTtsButtonsDisabled(disabled) {
  if (ttsBtn) {
    ttsBtn.disabled = disabled;
  }
  if (ttsSelectionBtn) {
    ttsSelectionBtn.disabled = disabled;
  }
  if (ttsAnchorBtn) {
    ttsAnchorBtn.disabled = disabled || !hasHighlightContent;
  }
}

function resetAudioPlayback() {
  queuedAudios = [];
  streamingFinished = false;
  if (currentAudio) {
    currentAudio.pause();
    currentAudio = null;
  }
  resetPauseResumeState();
}

function setPauseResumeEnabled(enabled) {
  if (ttsToggleBtn) {
    ttsToggleBtn.disabled = !enabled;
  }
}

function resetPauseResumeState() {
  isPaused = false;
  if (ttsToggleBtn) {
    ttsToggleBtn.textContent = '暂停播放';
  }
  setPauseResumeEnabled(false);
}

function markStreamingFinished() {
  streamingFinished = true;
  if (!currentAudio && !queuedAudios.length && !isPaused) {
    ttsStatus.textContent = '播放完成';
    setPauseResumeEnabled(false);
  }
}

function playNextAudioChunk() {
  if (!queuedAudios.length) {
    currentAudio = null;
    if (streamingFinished && !isPaused) {
      ttsStatus.textContent = '播放完成';
      setPauseResumeEnabled(false);
    } else if (!streamingFinished) {
      ttsStatus.textContent = '等待更多语音...';
    }
    return;
  }
  const chunk = queuedAudios.shift();
  ttsStatus.textContent = '播放中...';
  currentAudio = new Audio('data:audio/wav;base64,' + chunk);
  currentAudio.onended = () => {
    if (!isPaused) {
      playNextAudioChunk();
    }
  };
  currentAudio.onerror = () => {
    ttsStatus.textContent = '播放失败';
    currentAudio = null;
    setPauseResumeEnabled(false);
  };
  currentAudio.play().catch(err => {
    ttsStatus.textContent = '自动播放被阻止：' + err.message;
    currentAudio = null;
    queuedAudios.unshift(chunk);
    setPauseResumeEnabled(true);
  });
}

function enqueueAudioChunk(chunk) {
  queuedAudios.push(chunk);
  setPauseResumeEnabled(true);
  if (!currentAudio) {
    playNextAudioChunk();
  }
}

function handlePauseResumeToggle() {
  if (!ttsToggleBtn) {
    return;
  }
  if (!currentAudio && !queuedAudios.length) {
    ttsStatus.textContent = '暂无可暂停的语音';
    return;
  }
  if (!currentAudio) {
    playNextAudioChunk();
    ttsToggleBtn.textContent = '暂停播放';
    return;
  }
  if (!isPaused) {
    currentAudio.pause();
    isPaused = true;
    ttsToggleBtn.textContent = '继续播放';
    ttsStatus.textContent = '已暂停';
  } else {
    currentAudio.play().then(() => {
      isPaused = false;
      ttsToggleBtn.textContent = '暂停播放';
      ttsStatus.textContent = '播放中...';
    }).catch(err => {
      ttsStatus.textContent = '无法继续播放：' + err.message;
    });
  }
}

function normalizeTtsLine(rawLine) {
  if (typeof rawLine !== 'string') {
    return '';
  }
  let trimmed = rawLine.replace(/\\r/g, '').trim();
  if (!trimmed) {
    return '';
  }
  if (trimmed.startsWith('data:')) {
    trimmed = trimmed.slice(5).trim();
  }
  if (!trimmed || trimmed === '[DONE]') {
    return '';
  }
  return trimmed;
}

function parseTtsLine(line) {
  const normalized = normalizeTtsLine(line);
  if (!normalized) {
    return false;
  }
  try {
    const parsed = JSON.parse(normalized);
    if (parsed && parsed.audio) {
      enqueueAudioChunk(parsed.audio);
      return true;
    }
  } catch (err) {
    console.warn('无法解析TTS响应行', err);
  }
  return false;
}

async function consumeTtsResponse(response) {
  let chunkCount = 0;
  const handleLine = rawLine => {
    if (parseTtsLine(rawLine)) {
      chunkCount += 1;
    }
  };

  if (response.body && response.body.getReader) {
    const reader = response.body.getReader();
    const decoder = new TextDecoder();
    let buffer = '';

    while (true) {
      const { value, done } = await reader.read();
      if (done) break;
      buffer += decoder.decode(value, { stream: true });
      let newlineIndex;
      while ((newlineIndex = buffer.indexOf('\\n')) >= 0) {
        const line = buffer.slice(0, newlineIndex);
        buffer = buffer.slice(newlineIndex + 1);
        handleLine(line);
      }
    }
    buffer += decoder.decode();
    if (buffer) {
      handleLine(buffer);
    }
  } else {
    const payload = await response.text();
    payload.split('\\n').forEach(handleLine);
  }
  return chunkCount;
}

function getSelectedPageText() {
  const selection = window.getSelection ? window.getSelection() : null;
  return selection ? selection.toString().trim() : '';
}

async function streamTtsRequest(text) {
  const response = await fetch(TTS_ENDPOINT, {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ text })
  });
  if (!response.ok) {
    throw new Error('接口响应错误');
  }
  const chunkCount = await consumeTtsResponse(response);
  if (!chunkCount) {
    throw new Error('接口未返回音频数据');
  }
  markStreamingFinished();
}

function createTtsRequest(textResolver, emptyMessage) {
  return async () => {
    const text = textResolver();
    if (!text) {
      ttsStatus.textContent = emptyMessage;
      return;
    }
    setTtsButtonsDisabled(true);
    ttsStatus.textContent = '请求语音...';
    resetAudioPlayback();
    try {
      await streamTtsRequest(text);
    } catch (err) {
      ttsStatus.textContent = 'TTS 出错：' + (err && err.message ? err.message : err);
      resetAudioPlayback();
    } finally {
      setTtsButtonsDisabled(false);
    }
  };
}

if (ttsBtn) {
  ttsBtn.addEventListener('click', createTtsRequest(getFullTextForTts, '请先生成高亮结果'));
}
if (ttsSelectionBtn) {
  ttsSelectionBtn.addEventListener('click', createTtsRequest(getSelectedPageText, '请先选择要朗读的文本'));
}
if (ttsAnchorBtn) {
  ttsAnchorBtn.addEventListener('click', createTtsRequest(extractAnchorText, '请先在结果中点击句子作为朗读起点'));
}
if (ttsToggleBtn) {
  ttsToggleBtn.addEventListener('click', handlePauseResumeToggle);
}
</script>
</body>
</html>"""
PROXY_PAGE_TEMPLATE = Template(
    """<!DOCTYPE html>
<html lang=\"zh-CN\">
<head>
<meta charset=\"UTF-8\" />
<meta name=\"viewport\" content=\"width=device-width, initial-scale=1\" />
<title>Grammar Proxy Highlighter</title>
<style>
body { font-family: system-ui, -apple-system, \"Segoe UI\", sans-serif; margin: 0 auto; max-width: 860px; padding: 1.5rem; line-height: 1.65; }
h1 { font-size: 1.45rem; margin-bottom: 1rem; }
form { display: flex; flex-wrap: wrap; gap: 0.5rem; margin-bottom: 0.75rem; }
input[type=\"url\"] { flex: 1 1 260px; padding: 0.65rem; font-size: 1rem; border-radius: 0.5rem; border: 1px solid #d0d7de; }
button { padding: 0.65rem 1.4rem; border: none; border-radius: 999px; background: #2563eb; color: #fff; font-size: 1rem; cursor: pointer; }
.show-images-toggle { display: inline-flex; align-items: center; gap: 0.35rem; font-size: 0.9rem; color: #475569; }
.show-images-toggle input { width: auto; }
.tts-controls { margin-top: 0.5rem; display: flex; align-items: center; flex-wrap: wrap; gap: 0.75rem; }
.tts-controls button { background: #f97316; }
.tts-status { font-size: 0.95rem; color: #475569; }
.sentence-scope.anchor-highlight { outline: 2px dashed #f97316; outline-offset: 2px; }
.status { margin-top: 0.25rem; font-size: 0.95rem; }
.status.err { color: #b00020; }
.status.ok { color: #059669; }
section.result { margin-top: 1.4rem; padding-top: 1rem; border-top: 1px solid #e5e7eb; }
section.result .source { font-size: 0.95rem; margin-bottom: 0.5rem; color: #475569; word-break: break-word; }
section.result .source a { color: inherit; text-decoration: underline; }
section.result img { display:block; margin:0.75rem auto; max-width:100%; height:auto; max-width:min(100%,800px); }
.image-hint { font-size:0.9rem; color:#6b7280; margin:0.5rem 0 0; }
.clear-floating { position: fixed; left: 0; right: 0; bottom: 0; padding: 0.55rem 1.5rem; border-radius: 0; border-top: 1px solid #e5e7eb; background: rgba(249,250,251,0.96); display: flex; justify-content: center; z-index: 40; }
.clear-floating button { padding: 0.55rem 1.8rem; border-radius: 999px; background: #6b7280; color: #fff; font-size: 0.95rem; }
.clear-floating button:hover { filter: brightness(1.05); }
@media (prefers-reduced-motion: reduce) { .clear-floating { scroll-behavior: auto; } }
@media (max-width: 640px) { body { padding-bottom: 3.2rem; } }
</style>
$style_block
</head>
<body>
<h1>网页代理高亮</h1>
<form method=\"get\" action=\"/proxy\" class=\"url-form\">
<input type=\"url\" name=\"url\" value=\"$url_value\" placeholder=\"https://example.com/article\" required />
<button type=\"submit\">抓取并高亮</button>
<label class=\"show-images-toggle\">
  <input type=\"checkbox\" name=\"show_images\" value=\"1\" $show_images_checked />
  <span>显示图片（默认关闭以提升速度）</span>
</label>
</form>
$status_block
<div class=\"tts-controls\">
  <button type=\"button\" id=\"proxy-tts-btn\" disabled>朗读高亮文本</button>
  <button type=\"button\" id=\"proxy-tts-selection\">朗读选中文本</button>
  <button type=\"button\" id=\"proxy-tts-anchor\" disabled>从点击处朗读</button>
  <button type=\"button\" id=\"proxy-tts-toggle\" disabled>暂停播放</button>
  <span class=\"tts-status\" id=\"proxy-tts-status\"></span>
</div>
$result_block
$source_text_script
<div class=\"clear-floating\">
  <button type=\"button\" id=\"proxy-reset\">清空并重置</button>
</div>
<script>
(function() {
  var resetBtn = document.getElementById('proxy-reset');
  if (resetBtn) {
    resetBtn.addEventListener('click', function() {
      window.location.href = '/proxy';
    });
  }
  var ttsBtn = document.getElementById('proxy-tts-btn');
  var ttsSelectionBtn = document.getElementById('proxy-tts-selection');
  var ttsAnchorBtn = document.getElementById('proxy-tts-anchor');
  var ttsToggleBtn = document.getElementById('proxy-tts-toggle');
  var ttsStatus = document.getElementById('proxy-tts-status');
  var analysisRoot = document.querySelector('section.result .analysis');
  var proxySourceText = window.__proxySourceText || '';
  var TTS_ENDPOINT = 'http://141.140.15.30:8028/generate';
  var currentAudio = null;
  var queuedAudios = [];
  var streamingFinished = false;
  var anchorSentenceIndex = 0;
  var isPaused = false;

  if (analysisRoot && ttsBtn) {
    ttsBtn.disabled = false;
  }
  if (analysisRoot && ttsAnchorBtn) {
    ttsAnchorBtn.disabled = false;
  }

  function extractProxyText() {
    var container = document.querySelector('section.result .analysis');
    return container ? container.textContent.trim() : '';
  }

  function getSentenceNodes() {
    var container = document.querySelector('section.result .analysis');
    return container ? Array.from(container.querySelectorAll('.sentence-scope')) : [];
  }

  function clearAnchorHighlight() {
    var highlighted = document.querySelectorAll('section.result .sentence-scope.anchor-highlight');
    highlighted.forEach(function(el) {
      el.classList.remove('anchor-highlight');
    });
  }

  function resetAnchorState() {
    anchorSentenceIndex = 0;
    clearAnchorHighlight();
  }

  function setAnchorFromSentence(sentenceEl) {
    var sentences = getSentenceNodes();
    var idx = sentences.indexOf(sentenceEl);
    if (idx === -1) return;
    anchorSentenceIndex = idx;
    clearAnchorHighlight();
    sentenceEl.classList.add('anchor-highlight');
    var sid = sentenceEl.getAttribute('data-sid') || (idx + 1);
    ttsStatus.textContent = '已选择第 ' + sid + ' 句作为朗读起点';
  }

  resetAnchorState();
  var resultSection = document.querySelector('section.result');
  if (resultSection) {
    resultSection.addEventListener('click', function(evt) {
      var target = evt.target;
      var isTextNode = typeof Node !== 'undefined' && target && target.nodeType === Node.TEXT_NODE;
      var base = isTextNode ? target.parentElement : target;
      if (!base || typeof base.closest !== 'function') {
        return;
      }
      var sentenceEl = base.closest('.sentence-scope');
      if (sentenceEl) {
        setAnchorFromSentence(sentenceEl);
      }
    });
  }

  function getFullTextForTts() {
    var text = proxySourceText || extractProxyText();
    return text.trim();
  }

  function extractAnchorText() {
    var sentences = getSentenceNodes();
    if (!sentences.length) return '';
    var start = Math.min(anchorSentenceIndex, sentences.length - 1);
    var parts = [];
    for (var i = start; i < sentences.length; i++) {
      var text = sentences[i].textContent.trim();
      if (text) {
        parts.push(text);
      }
    }
    return parts.join(' ');
  }

  function setTtsButtonsDisabled(disabled) {
    if (ttsBtn) {
      ttsBtn.disabled = disabled;
    }
    if (ttsSelectionBtn) {
      ttsSelectionBtn.disabled = disabled;
    }
    if (ttsAnchorBtn) {
      ttsAnchorBtn.disabled = disabled || !analysisRoot;
    }
  }

  function resetAudioPlayback() {
    queuedAudios = [];
    streamingFinished = false;
    if (currentAudio) {
      currentAudio.pause();
      currentAudio = null;
    }
    resetPauseResumeState();
  }

  function setPauseResumeEnabled(enabled) {
    if (ttsToggleBtn) {
      ttsToggleBtn.disabled = !enabled;
    }
  }

  function resetPauseResumeState() {
    isPaused = false;
    if (ttsToggleBtn) {
      ttsToggleBtn.textContent = '暂停播放';
    }
    setPauseResumeEnabled(false);
  }

  function markStreamingFinished() {
    streamingFinished = true;
    if (!currentAudio && !queuedAudios.length && !isPaused) {
      ttsStatus.textContent = '播放完成';
      setPauseResumeEnabled(false);
    }
  }

  function playNextAudioChunk() {
    if (!queuedAudios.length) {
      currentAudio = null;
      if (streamingFinished && !isPaused) {
        ttsStatus.textContent = '播放完成';
        setPauseResumeEnabled(false);
      } else if (!streamingFinished) {
        ttsStatus.textContent = '等待更多语音...';
      }
      return;
    }
    var chunk = queuedAudios.shift();
    ttsStatus.textContent = '播放中...';
    currentAudio = new Audio('data:audio/wav;base64,' + chunk);
    currentAudio.onended = function() {
      if (!isPaused) {
        playNextAudioChunk();
      }
    };
    currentAudio.onerror = function() {
      ttsStatus.textContent = '播放失败';
      currentAudio = null;
      setPauseResumeEnabled(false);
    };
    currentAudio.play().catch(function(err) {
      ttsStatus.textContent = '自动播放被阻止：' + err.message;
      currentAudio = null;
      queuedAudios.unshift(chunk);
      setPauseResumeEnabled(true);
    });
  }

  function enqueueAudioChunk(chunk) {
    queuedAudios.push(chunk);
    setPauseResumeEnabled(true);
    if (!currentAudio) {
      playNextAudioChunk();
    }
  }

  function handlePauseResumeToggle() {
    if (!ttsToggleBtn) {
      return;
    }
    if (!currentAudio && !queuedAudios.length) {
      ttsStatus.textContent = '暂无可暂停的语音';
      return;
    }
    if (!currentAudio) {
      playNextAudioChunk();
      ttsToggleBtn.textContent = '暂停播放';
      return;
    }
    if (!isPaused) {
      currentAudio.pause();
      isPaused = true;
      ttsToggleBtn.textContent = '继续播放';
      ttsStatus.textContent = '已暂停';
    } else {
      currentAudio.play().then(function() {
        isPaused = false;
        ttsToggleBtn.textContent = '暂停播放';
        ttsStatus.textContent = '播放中...';
      }).catch(function(err) {
        ttsStatus.textContent = '无法继续播放：' + err.message;
      });
    }
  }

  function normalizeProxyTtsLine(rawLine) {
    if (typeof rawLine !== 'string') {
      return '';
    }
    var trimmed = rawLine.replace(/\\r/g, '').trim();
    if (!trimmed) {
      return '';
    }
    if (trimmed.indexOf('data:') === 0) {
      trimmed = trimmed.slice(5).trim();
    }
    if (!trimmed || trimmed === '[DONE]') {
      return '';
    }
    return trimmed;
  }

  function parseTtsLine(line) {
    var normalized = normalizeProxyTtsLine(line);
    if (!normalized) {
      return false;
    }
    try {
      var parsed = JSON.parse(normalized);
      if (parsed && parsed.audio) {
        enqueueAudioChunk(parsed.audio);
        return true;
      }
    } catch (err) {
      console.warn('无法解析TTS响应行', err);
    }
    return false;
  }

  async function consumeTtsResponse(response) {
    var chunkCount = 0;
    var handleLine = function(rawLine) {
      if (parseTtsLine(rawLine)) {
        chunkCount += 1;
      }
    };

    if (response.body && response.body.getReader) {
      var reader = response.body.getReader();
      var decoder = new TextDecoder();
      var buffer = '';

      while (true) {
        var readResult = await reader.read();
        if (readResult.done) {
          break;
        }
        buffer += decoder.decode(readResult.value, { stream: true });
        var newlineIndex;
        while ((newlineIndex = buffer.indexOf('\\n')) >= 0) {
          var line = buffer.slice(0, newlineIndex);
          buffer = buffer.slice(newlineIndex + 1);
          handleLine(line);
        }
      }
      buffer += decoder.decode();
      if (buffer) {
        handleLine(buffer);
      }
    } else {
      var payload = await response.text();
      payload.split('\\n').forEach(handleLine);
    }
    return chunkCount;
  }

  function getSelectedPageText() {
    var selection = window.getSelection ? window.getSelection() : null;
    return selection ? selection.toString().trim() : '';
  }

  async function streamTtsRequest(text) {
    var response = await fetch(TTS_ENDPOINT, {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({ text: text })
    });
    if (!response.ok) {
      throw new Error('接口响应错误');
    }
    var chunkCount = await consumeTtsResponse(response);
    if (!chunkCount) {
      throw new Error('接口未返回音频数据');
    }
    markStreamingFinished();
  }

  function createTtsRequest(textResolver, emptyMessage) {
    return async function() {
      var text = textResolver();
      if (!text) {
        ttsStatus.textContent = emptyMessage;
        return;
      }
      setTtsButtonsDisabled(true);
      ttsStatus.textContent = '请求语音...';
      resetAudioPlayback();
      try {
        await streamTtsRequest(text);
      } catch (err) {
        ttsStatus.textContent = 'TTS 出错：' + (err && err.message ? err.message : err);
        resetAudioPlayback();
      } finally {
        setTtsButtonsDisabled(false);
      }
    };
  }

  if (ttsBtn) {
    ttsBtn.addEventListener('click', createTtsRequest(getFullTextForTts, '请先抓取文章内容再朗读'));
  }
  if (ttsSelectionBtn) {
    ttsSelectionBtn.addEventListener('click', createTtsRequest(getSelectedPageText, '请先选择要朗读的文本'));
  }
  if (ttsAnchorBtn) {
    ttsAnchorBtn.addEventListener('click', createTtsRequest(extractAnchorText, '请先点击句子作为朗读起点'));
  }
  if (ttsToggleBtn) {
    ttsToggleBtn.addEventListener('click', handlePauseResumeToggle);
  }
})();
</script>
</body>
</html>"""
)

ALLOWED_URL_SCHEMES = {"http", "https"}
MAX_REMOTE_HTML_BYTES = 1_000_000
REMOTE_FETCH_TIMEOUT = 10.0
REMOTE_FETCH_HEADERS = {
    # Use a browser-like user agent and common headers so that sites which
    # block generic HTTP clients are more likely to return normal content.
    "User-Agent": (
        "Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
        "AppleWebKit/537.36 (KHTML, like Gecko) "
        "Chrome/124.0.0.0 Safari/537.36"
    ),
    "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8",
    "Accept-Language": "en-US,en;q=0.9",
    # Let httpx / the underlying HTTP stack negotiate an encoding it can
    # actually decode. If we unconditionally advertise "br" but the runtime
    # does not have brotli support installed, some sites will respond with
    # brotli-compressed payloads that end up as乱码 or decoding errors.
    #
    # Most modern servers default to gzip or identity when the header is
    # absent, which are both handled fine by httpx.
    # "Accept-Encoding": "gzip, deflate, br",
    "Connection": "keep-alive",
    "Upgrade-Insecure-Requests": "1",
    # A few anti‑bot setups check these request headers; keeping them close
    # to real desktop Chrome values slightly improves compatibility, even
    # though they are not a guarantee against 403 responses.
    "Sec-Fetch-Site": "none",
    "Sec-Fetch-Mode": "navigate",
    "Sec-Fetch-User": "?1",
    "Sec-Fetch-Dest": "document",
}
SIMPLE_FETCH_HEADERS = {
    # Minimal browser-like headers for the fallback "simple request" path.
    "User-Agent": (
        "Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
        "AppleWebKit/537.36 (KHTML, like Gecko) "
        "Chrome/124.0.0.0 Safari/537.36"
    ),
    "Accept": "text/html,application/xhtml+xml;q=0.9,*/*;q=0.8",
    "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
    "Connection": "close",
}


def _inject_proxy_images(html_fragment: str, images: List[Dict[str, str]]) -> str:
    """Replace stable image placeholders with <img> tags in the highlighted HTML."""
    result = html_fragment
    for idx, img in enumerate(images):
        marker = img.get("marker") or f"__GHIMG_{idx}__"
        src = html.escape(img.get("src", "") or "", quote=True)
        if not src:
            continue
        alt = html.escape(img.get("alt", "") or "", quote=True)
        title = html.escape(img.get("title", "") or "", quote=True)
        attrs = [f"src='{src}'"]
        if alt:
            attrs.append(f"alt='{alt}'")
        if title:
            attrs.append(f"title='{title}'")
        # Preserve simple width/height hints when they look safe. Most modern
        # pages rely on CSS for sizing, but explicit attributes can help keep
        # code snippets or diagrams close to their original scale.
        def _safe_dim(value: Optional[str]) -> Optional[str]:
            if not value:
                return None
            value = value.strip()
            if re.fullmatch(r"\d+(?:\.\d+)?(px|%)?", value):
                return value
            return None

        width = _safe_dim(img.get("width"))
        height = _safe_dim(img.get("height"))
        if width:
            attrs.append(f"width='{html.escape(width, quote=True)}'")
        if height:
            attrs.append(f"height='{html.escape(height, quote=True)}'")
        img_tag = "<img " + " ".join(attrs) + " />"
        # Simple textual replacement is sufficient because placeholders
        # are emitted as plain word tokens without HTML meta characters.
        result = result.replace(marker, img_tag)
    return result


IMG_MARKER_RE = re.compile(r"__GHIMG_\d+__")


def _strip_proxy_image_markers(html_fragment: str) -> str:
    """Remove residual image placeholders when images are hidden."""
    if IMG_MARKER_RE.search(html_fragment) is None:
        return html_fragment
    return IMG_MARKER_RE.sub("", html_fragment)


def _inject_proxy_codeblocks(html_fragment: str, code_blocks: List[Dict[str, str]]) -> str:
    """Replace code placeholders with <pre><code> blocks, preserving formatting."""
    result = html_fragment
    for idx, block in enumerate(code_blocks):
        marker = block.get("marker") or f"__GHCODE_{idx}__"
        raw = block.get("text") or ""
        if not raw.strip():
            continue
        # Escape HTML but keep newlines so that <pre> preserves formatting.
        code_html = html.escape(raw, quote=False)
        pre_tag = f"<pre><code>{code_html}</code></pre>"
        result = result.replace(marker, pre_tag)
    return result

class SimpleHTMLStripper(HTMLParser):
    def __init__(self):
        super().__init__()
        # Accumulate visible text into paragraph-like blocks while skipping
        # navigation / sidebars / ads etc. We do this with a small HTML
        # structure–aware state machine instead of flattening everything.
        self._blocks: List[Dict[str, Any]] = []
        self._current_parts: List[str] = []

        # Track when we are inside potentially main content containers
        # like <article> or <main>.
        self._article_depth = 0

        # Track whether we are inside a preformatted code block so that we
        # can preserve indentation and line breaks instead of collapsing
        # whitespace as normal text.
        self._in_pre = False
        self._in_code = False
        self._current_code_chunks: List[str] = []
        self._code_blocks: List[Dict[str, str]] = []

        # Stack of flags indicating which open tags should be skipped.
        # When any active flag is True, textual data is ignored.
        self._skip_stack: List[bool] = []
        self._skip_depth = 0

        self._title_chunks: List[str] = []
        self._in_title = False
        self._h1_chunks: List[str] = []
        self._h1_main_chunks: List[str] = []
        self._in_h1 = False

        # Collected inline images from the main content, in document order.
        # Each image is represented as a small dict with sanitized attributes.
        self._images: List[Dict[str, str]] = []
        # Active list containers (<ul>/<ol>) and current <li> nesting state.
        self._list_stack: List[Dict[str, Any]] = []
        self._list_item_stack: List[Dict[str, Any]] = []

    # Keywords commonly used in class/id attributes for non‑article areas
    _NOISE_KEYWORDS = {
        "sidebar",
        "side-bar",
        "aside",
        "nav",
        "menu",
        "breadcrumb",
        "breadcrumbs",
        "pagination",
        "pager",
        "comment",
        "comments",
        "reply",
        "advert",
        "ad-",
        "ads",
        "sponsor",
        "promo",
        "promotion",
        "related",
        "recommend",
        "share",
        "social",
        "subscribe",
        "signup",
        "login",
        "popup",
        "modal",
        "banner",
        "cookie",
        "notification",
        "toolbar",
        "footer",
        "header-bar",
    }

    # Tags whose textual content is almost never part of the main article.
    _ALWAYS_SKIP_TAGS = {
        "script",
        "style",
        "noscript",
        "nav",
        "aside",
        "footer",
        "form",
        "svg",
        "iframe",
        "button",
        "input",
        "textarea",
        "select",
        "option",
        "label",
    }

    # Structural container tags where noise classes/roles are meaningful.
    # For purely inline tags we avoid applying aggressive noise heuristics
    # so that important inline text (e.g. spans in the first sentence) is
    # not accidentally dropped.
    _STRUCTURAL_NOISE_TAGS = {
        "div",
        "section",
        "aside",
        "nav",
        "header",
        "footer",
        "main",
        "article",
        "ul",
        "ol",
        "li",
    }

    # Block-level tags that naturally mark paragraph boundaries.
    _BLOCK_TAGS = {
        "p",
        "li",
        "blockquote",
        "h1",
        "h2",
        "h3",
        "h4",
        "h5",
        "h6",
        "pre",
        "table",
        "tr",
    }

    # Keywords for containers that are likely to hold the main article body.
    # Used to decide which regions count as "main content" for both text
    # and inline images.
    _CONTENT_KEYWORDS = {
        "content",
        "main-content",
        "article-body",
        "post-body",
        "post-content",
        "entry-content",
        "story-body",
        "blog-post",
        "markdown-body",
        "readable-content",
    }

    # Keywords on image-related class/id/src that usually indicate avatars,
    # logo icons, decorative banners, etc., which we want to drop from the
    # extracted main content.
    _IMAGE_NOISE_KEYWORDS = {
        "avatar",
        "author",
        "logo",
        "icon",
        "favicon",
        "badge",
        "banner",
        "thumb",
        "thumbnail",
        "profile",
        "cover",
        "background",
        "sprite",
        "emoji",
        "reaction",
    }
    _TEXT_NOISE_KEYWORDS = {
        "menu",
        "menus",
        "navigation",
        "nav",
        "目录",
        "目錄",
        "导航",
        "導航",
        "菜单",
        "菜單",
        "广告",
        "廣告",
        "ad",
        "ads",
        "sponsor",
        "sponsored",
        "上一篇",
        "下一篇",
        "返回顶部",
        "返回頂部",
        "分享",
        "分享至",
        "相关推荐",
        "相关阅读",
        "相關閱讀",
        "recommended",
        "related posts",
        "login",
        "signup",
    }
    _TEXT_NOISE_PREFIXES = (
        "目录",
        "目錄",
        "导航",
        "導航",
        "菜单",
        "菜單",
        "广告",
        "廣告",
        "上一篇",
        "下一篇",
        "上一页",
        "下一页",
        "返回目录",
        "返回目錄",
        "返回顶部",
        "返回頂部",
        "分享",
        "相关",
        "相關",
        "recommended",
        "login",
        "signup",
    )

    def _finish_paragraph(self) -> None:
        """Flush current buffered tokens into a paragraph list."""
        if not self._current_parts:
            return
        # For regular paragraphs we still collapse excessive internal
        # whitespace, but we keep logical breaks between paragraphs
        # themselves so that the downstream highlighter can reconstruct
        # paragraph structure.
        text = " ".join(self._current_parts)
        text = re.sub(r"\s+", " ", text).strip()
        self._current_parts = []
        if not text:
            return
        if self._looks_like_noise_paragraph(text):
            return
        block_kind = "paragraph"
        list_kind: Optional[str] = None
        list_depth = 0
        list_index: Optional[int] = None
        if self._list_item_stack:
            list_ctx = self._list_item_stack[-1]
            block_kind = "list-item"
            list_kind = list_ctx.get("list_type") or "ul"
            depth_value = list_ctx.get("depth", 1)
            try:
                depth_int = int(depth_value)
            except (TypeError, ValueError):
                depth_int = 1
            list_depth = min(max(depth_int, 1), 5)
            if list_kind == "ol":
                idx = list_ctx.get("index")
                if isinstance(idx, int):
                    list_index = idx
        self._blocks.append(
            {
                "text": text,
                "is_main": self._article_depth > 0,
                "kind": block_kind,
                "list_kind": list_kind,
                "list_depth": list_depth,
                "list_index": list_index,
            }
        )

    def _looks_like_noise_paragraph(self, text: str) -> bool:
        normalized = text.strip()
        if not normalized:
            return True
        lowered = normalized.lower()
        compact = re.sub(r"\s+", "", lowered)
        for prefix in self._TEXT_NOISE_PREFIXES:
            if lowered.startswith(prefix.lower()):
                if len(normalized) <= 80:
                    return True
        if len(normalized) <= 80:
            for keyword in self._TEXT_NOISE_KEYWORDS:
                if keyword in lowered or keyword in compact:
                    return True
        # Skip very short bullet-like crumbs that mostly consist of symbols.
        if len(normalized) <= 6 and sum(ch.isalnum() for ch in normalized) <= 1:
            return True
        return False

    @staticmethod
    def _parse_ordered_start(raw_value: Optional[str]) -> int:
        if raw_value is None:
            return 1
        value = raw_value.strip()
        if not value:
            return 1
        try:
            parsed = int(value)
            return parsed if parsed >= 1 else 1
        except ValueError:
            return 1

    def handle_starttag(self, tag, attrs):
        lowered = tag.lower()
        # Paragraph boundary before starting a new block element or <br>.
        if lowered in self._BLOCK_TAGS or lowered == "br":
            if self._skip_depth == 0:
                self._finish_paragraph()

        # Entering a <pre> region – treat it as a dedicated code block.
        if lowered == "pre" and self._skip_depth == 0:
            self._finish_paragraph()
            self._in_pre = True
            self._current_code_chunks = []

        # Decide whether this element should be skipped entirely.
        attr_dict = {k.lower(): (v or "") for k, v in attrs}
        role = attr_dict.get("role", "").lower()
        classes_ids = (attr_dict.get("class", "") + " " + attr_dict.get("id", "")).lower()

        is_noise_attr = False
        # Only treat class/id keywords as layout "noise" on structural
        # containers (div/section/nav/etc). Inline tags with "comment"
        # in their class (like mdspan-comment on Towards Data Science)
        # should not be discarded, otherwise we lose the first words
        # of sentences.
        if lowered in self._STRUCTURAL_NOISE_TAGS:
            is_noise_attr = any(key in classes_ids for key in self._NOISE_KEYWORDS)
            if role in {"navigation", "banner", "contentinfo", "complementary"}:
                is_noise_attr = True

        skip_this = lowered in self._ALWAYS_SKIP_TAGS or is_noise_attr
        if skip_this:
            self._skip_depth += 1
        self._skip_stack.append(skip_this)

        # Track when we are inside an article-like container; only count if not skipped.
        if self._skip_depth == 0 and lowered in {"article", "main", "section", "div"}:
            # Treat semantic containers and common "main content" classes as
            # part of the article area so that we keep their text and inline
            # media but still avoid sidebars / nav.
            if lowered in {"article", "main"} or any(
                key in classes_ids for key in self._CONTENT_KEYWORDS
            ) or role == "main":
                self._article_depth += 1

        if self._skip_depth == 0 and lowered in {"ul", "ol"}:
            start = 1
            if lowered == "ol":
                start = self._parse_ordered_start(attr_dict.get("start"))
            self._list_stack.append(
                {
                    "type": lowered,
                    "start": start,
                    "next_index": start,
                }
            )

        if lowered == "li" and self._skip_depth == 0:
            list_ctx = self._list_stack[-1] if self._list_stack else None
            depth = len(self._list_stack) if self._list_stack else 1
            list_type = list_ctx.get("type") if list_ctx else "ul"
            index = None
            if list_ctx and list_ctx["type"] == "ol":
                index = list_ctx["next_index"]
                list_ctx["next_index"] = index + 1
            li_value = attr_dict.get("value")
            if li_value and list_ctx and list_ctx["type"] == "ol":
                try:
                    value_idx = int(li_value)
                    index = value_idx
                    list_ctx["next_index"] = value_idx + 1
                except ValueError:
                    pass
            self._list_item_stack.append(
                {
                    "list_type": list_type,
                    "index": index,
                    "depth": depth,
                }
            )

        if lowered == "title" and self._skip_depth == 0:
            self._in_title = True
        if lowered == "h1" and self._skip_depth == 0:
            self._in_h1 = True

        if lowered == "code" and self._skip_depth == 0 and self._in_pre:
            # Nested <code> inside <pre> – keep track but we don't need
            # separate buffering beyond the enclosing pre block.
            self._in_code = True

        # Inline image handling: only keep <img> elements that are inside the
        # main article content (tracked via _article_depth) and that do not
        # look like avatars / logos / decorative icons. We insert a stable
        # placeholder token into the text stream so that the /proxy renderer
        # can later replace it with a real <img> tag while preserving the
        # grammar highlighting.
        if lowered == "img" and self._skip_depth == 0 and self._article_depth > 0:
            src = attr_dict.get("src", "").strip()
            if src:
                alt = attr_dict.get("alt", "") or ""
                title = attr_dict.get("title", "") or ""
                width = (attr_dict.get("width") or "").strip()
                height = (attr_dict.get("height") or "").strip()
                img_classes_ids = classes_ids + " " + src.lower()
                if any(key in img_classes_ids for key in self._IMAGE_NOISE_KEYWORDS):
                    return
                marker = f"__GHIMG_{len(self._images)}__"
                img_info: Dict[str, str] = {
                    "marker": marker,
                    "src": src,
                    "alt": alt,
                    "title": title,
                }
                if width:
                    img_info["width"] = width
                if height:
                    img_info["height"] = height
                self._images.append(img_info)
                # Treat the image as an inline token within the current
                # paragraph. Paragraph finishing logic will ensure it
                # stays grouped with surrounding text.
                self._current_parts.append(marker)

    def handle_endtag(self, tag):
        lowered = tag.lower()
        if lowered == "code" and self._in_code:
            self._in_code = False

        if lowered == "pre" and self._in_pre:
            self._in_pre = False
            # Finalize the current code block into a single placeholder
            # token so that it passes through the grammar highlighter
            # untouched, and can later be restored as a <pre><code> block.
            code_text = "".join(self._current_code_chunks)
            self._current_code_chunks = []
            if code_text.strip() and self._skip_depth == 0:
                marker = f"__GHCODE_{len(self._code_blocks)}__"
                self._code_blocks.append({"marker": marker, "text": code_text})
                # We append the marker to the paragraph parts so that
                # get_text() emits it in the right position.
                self._current_parts.append(marker)

        # Closing a block element ends the current paragraph.
        if lowered in self._BLOCK_TAGS and self._skip_depth == 0:
            self._finish_paragraph()

        if lowered == "li" and self._skip_depth == 0 and self._list_item_stack:
            self._list_item_stack.pop()
        if lowered in {"ul", "ol"} and self._skip_depth == 0 and self._list_stack:
            self._list_stack.pop()

        if lowered == "title":
            self._in_title = False
        if lowered == "h1":
            self._in_h1 = False

        if lowered in {"article", "main", "section"} and self._skip_depth == 0 and self._article_depth > 0:
            self._article_depth -= 1

        if self._skip_stack:
            skip_this = self._skip_stack.pop()
            if skip_this and self._skip_depth > 0:
                self._skip_depth -= 1

    def handle_data(self, data):
        if self._skip_depth > 0:
            return
        if self._in_pre or self._in_code:
            # Preserve code blocks exactly as they appear, including
            # newlines and indentation.
            self._current_code_chunks.append(data)
            return
        stripped = data.strip()
        if not stripped:
            return
        if self._in_title:
            self._title_chunks.append(stripped)
            return

        # Regular visible text
        self._current_parts.append(stripped)
        if self._in_h1:
            self._h1_chunks.append(stripped)
            if self._article_depth > 0:
                self._h1_main_chunks.append(stripped)

    def get_text(self) -> str:
        # Flush any trailing paragraph.
        self._finish_paragraph()
        blocks = self._selected_blocks()
        if not blocks:
            return ""
        # Keep natural paragraphs contiguous with a single newline instead of
        # injecting blank lines that did not exist in the source.
        return "\n".join(block["text"] for block in blocks)

    def _selected_blocks(self) -> List[Dict[str, Any]]:
        if not self._blocks:
            return []
        main_blocks = [block for block in self._blocks if block.get("is_main")]
        return main_blocks if main_blocks else self._blocks

    def get_blocks(self) -> List[Dict[str, Any]]:
        blocks = self._selected_blocks()
        return [dict(block) for block in blocks]

    def get_title(self) -> str:
        # Prefer <h1> heading (especially inside <article>/<main>) as the
        # primary title; fall back to <title>.
        if self._h1_main_chunks:
            raw = " ".join(self._h1_main_chunks)
        elif self._h1_chunks:
            raw = " ".join(self._h1_chunks)
        elif self._title_chunks:
            raw = " ".join(self._title_chunks)
        else:
            return ""
        return re.sub(r"\s+", " ", raw).strip()

    def get_images(self) -> List[Dict[str, str]]:
        """Return the list of captured inline images in document order."""
        return list(self._images)

    def get_code_blocks(self) -> List[Dict[str, str]]:
        """Return captured code blocks (from <pre>/<code>) in document order."""
        return list(self._code_blocks)


def _normalize_target_url(raw_url: str) -> str:
    candidate = (raw_url or "").strip()
    if not candidate:
        raise ValueError("请输入要抓取的 URL。")
    parsed = urlparse(candidate if "://" in candidate else f"https://{candidate}")
    if parsed.scheme not in ALLOWED_URL_SCHEMES:
        raise ValueError("仅支持 http/https 协议链接。")
    if not parsed.netloc:
        raise ValueError("URL 缺少域名部分。")
    sanitized = parsed._replace(fragment="")
    return urlunparse(sanitized)


def _fallback_html_to_text(html_body: str) -> str:
    """Very simple HTML-to-text fallback used when structured extraction fails.

    This does not attempt to distinguish main content from navigation, but it
    guarantees we return *something* for pages whose structure confuses the
    SimpleHTMLStripper heuristics (e.g. some mirror sites).
    """
    # Drop script/style/noscript content outright.
    cleaned = re.sub(
        r"(?is)<(script|style|noscript)[^>]*>.*?</\1>",
        " ",
        html_body,
    )
    # Convert common block separators into newlines.
    cleaned = re.sub(r"(?i)<br\s*/?>", "\n", cleaned)
    cleaned = re.sub(r"(?i)</p\s*>", "\n\n", cleaned)
    cleaned = re.sub(r"(?i)</(div|section|article|li|h[1-6])\s*>", "\n\n", cleaned)
    # Remove all remaining tags.
    cleaned = re.sub(r"(?is)<[^>]+>", " ", cleaned)
    cleaned = html.unescape(cleaned)
    # Normalize whitespace but keep paragraph-level blank lines.
    cleaned = cleaned.replace("\r", "")
    # Collapse runs of spaces/tabs inside lines.
    cleaned = re.sub(r"[ \t\f\v]+", " ", cleaned)
    # Collapse 3+ blank lines into just 2.
    cleaned = re.sub(r"\n\s*\n\s*\n+", "\n\n", cleaned)
    cleaned = cleaned.strip()
    return cleaned


def _build_paragraph_metadata(blocks: List[Dict[str, Any]]) -> List[Dict[str, str]]:
    """Convert stripped block info into span attributes for downstream rendering."""
    if not blocks:
        return []
    paragraph_meta: List[Dict[str, str]] = []
    for block in blocks:
        attrs: Dict[str, str] = {}
        if block.get("kind") == "list-item" and block.get("list_kind"):
            attrs["data-list-kind"] = str(block["list_kind"])
            depth = block.get("list_depth")
            if depth:
                attrs["data-list-depth"] = str(depth)
            if block.get("list_kind") == "ol" and block.get("list_index") is not None:
                attrs["data-list-index"] = str(block["list_index"])
        paragraph_meta.append(attrs)
    return paragraph_meta


def _build_paragraph_ranges(blocks: List[Dict[str, Any]]) -> List[Tuple[int, int]]:
    """Map each stripped block to its char span within the joined plain text."""
    if not blocks:
        return []
    ranges: List[Tuple[int, int]] = []
    cursor = 0
    for idx, block in enumerate(blocks):
        text = block.get("text") or ""
        start = cursor
        end = start + len(text)
        ranges.append((start, end))
        cursor = end
        # Plain text joins blocks with a single newline; skip trailing newline.
        if idx < len(blocks) - 1:
            cursor += 1
    return ranges


def _decode_html_bytes(raw_content: bytes, encoding_hint: Optional[str]) -> str:
    encoding_candidates: List[str] = []
    if encoding_hint:
        encoding_candidates.append(encoding_hint)
    encoding_candidates.extend(["utf-8", "latin-1"])
    last_exc: Optional[Exception] = None
    for enc in encoding_candidates:
        try:
            html_body = raw_content.decode(enc, errors="replace")
            break
        except Exception as exc:  # pragma: no cover - defensive
            last_exc = exc
    else:  # pragma: no cover - extremely unlikely
        raise RuntimeError(f"无法解码远程页面内容: {last_exc}")
    if len(html_body) > MAX_REMOTE_HTML_BYTES:
        html_body = html_body[:MAX_REMOTE_HTML_BYTES]
    return html_body


async def _download_html_via_httpx(url: str) -> str:
    async with httpx.AsyncClient(timeout=REMOTE_FETCH_TIMEOUT, follow_redirects=True) as client:
        response = await client.get(url, headers=REMOTE_FETCH_HEADERS)
    html_body = _decode_html_bytes(response.content, response.encoding)
    response.raise_for_status()
    return html_body


async def _download_html_via_stdlib(url: str) -> str:
    def _sync_fetch() -> Tuple[bytes, Optional[str]]:
        req = urllib_request.Request(url, headers=SIMPLE_FETCH_HEADERS)
        opener = urllib_request.build_opener(urllib_request.ProxyHandler({}))
        with opener.open(req, timeout=REMOTE_FETCH_TIMEOUT) as resp:
            data = resp.read(MAX_REMOTE_HTML_BYTES + 1)
            headers = getattr(resp, "headers", None)
            encoding_hint = None
            if headers is not None:
                get_charset = getattr(headers, "get_content_charset", None)
                if callable(get_charset):
                    encoding_hint = get_charset()
                if not encoding_hint:
                    content_type = headers.get("Content-Type", "")
                    match = re.search(r"charset=([\w-]+)", content_type or "", re.IGNORECASE)
                    if match:
                        encoding_hint = match.group(1)
            return data, encoding_hint

    raw_content, encoding_hint = await asyncio.to_thread(_sync_fetch)
    return _decode_html_bytes(raw_content, encoding_hint)


async def _download_html_with_fallback(url: str) -> str:
    first_exc: Optional[Exception] = None
    try:
        return await _download_html_via_httpx(url)
    except httpx.HTTPStatusError as exc:
        status = exc.response.status_code if exc.response is not None else None
        if status not in {401, 403, 407, 451, 429}:
            raise
        first_exc = exc
    except httpx.HTTPError as exc:
        first_exc = exc

    try:
        return await _download_html_via_stdlib(url)
    except (urllib_error.URLError, urllib_error.HTTPError, TimeoutError) as fallback_exc:
        if first_exc:
            raise first_exc from fallback_exc
        raise


async def _fetch_remote_plaintext(
    url: str,
) -> Tuple[
    str,
    str,
    str,
    List[Dict[str, str]],
    List[Dict[str, str]],
    List[Dict[str, str]],
    List[Tuple[int, int]],
]:
    normalized = _normalize_target_url(url)
    html_body = await _download_html_with_fallback(normalized)
    stripper = SimpleHTMLStripper()
    stripper.feed(html_body)
    title = stripper.get_title() or normalized
    images = stripper.get_images()
    code_blocks = stripper.get_code_blocks()
    plain_text = stripper.get_text()
    block_info = stripper.get_blocks()
    paragraph_ranges = _build_paragraph_ranges(block_info)
    if not plain_text:
        plain_text = _fallback_html_to_text(html_body)
        if not plain_text:
            raise ValueError("未能从该页面提取正文。")
        # Fallback text no longer contains structured placeholders, so any
        # collected media/code markers would be invalid.
        images = []
        code_blocks = []
        block_info = []
        paragraph_ranges = []
    paragraph_meta = _build_paragraph_metadata(block_info)
    return normalized, title, plain_text, images, code_blocks, paragraph_meta, paragraph_ranges


def _render_proxy_page(
    *,
    url_value: str = "",
    message: Optional[str] = None,
    is_error: bool = False,
    highlight_fragment: Optional[str] = None,
    helper_enabled: bool = False,
    source_url: Optional[str] = None,
    source_title: Optional[str] = None,
    show_images: bool = False,
    image_notice: Optional[str] = None,
    source_plaintext: Optional[str] = None,
) -> str:
    helper_state = "on" if helper_enabled else "off"
    status_block = ""
    if message:
        cls = "status err" if is_error else "status ok"
        status_block = f"<p class='{cls}'>{html.escape(message)}</p>"

    style_block = STYLE_BLOCK if highlight_fragment else ""
    result_block = ""
    source_script = ""
    if highlight_fragment and source_url:
        safe_url = html.escape(source_url, quote=True)
        safe_title = html.escape(source_title or source_url)
        image_hint = ""
        if image_notice:
            image_hint = f"<p class='image-hint'>{html.escape(image_notice)}</p>"
        if source_plaintext:
            source_script = f"<script>window.__proxySourceText = {json.dumps(source_plaintext)}</script>"
        result_block = (
            "<section class='result'>"
            f"<div class='source'>原页面：<a href='{safe_url}' target='_blank' rel='noopener'>{safe_title}</a></div>"
            f"<div class='analysis' data-helper='{helper_state}'>{highlight_fragment}</div>"
            f"{image_hint}"
            "</section>"
        )

    show_images_checked = "checked" if show_images else ""
    return PROXY_PAGE_TEMPLATE.substitute(
        style_block=style_block,
        url_value=html.escape(url_value or "", quote=True),
        status_block=status_block,
        result_block=result_block,
        show_images_checked=show_images_checked,
        source_text_script=source_script,
    )