"""
Pattern Deviation Analyzer
===========================
Implements the student journalist mental model for bias detection:

  1. Build a "reputable pattern" from external news titles/snippets
     (Google News + local DB results from reliable sources).
  2. Compare every article sentence against that pattern.
  3. Sentences whose concepts are NOT present in ANY reputable source
     are surfaced as "extra claims" — possible bias signals.

No new dependencies — uses the MiniLM model already loaded by the project
(sentence_transformers) for semantic similarity.
"""

import re
from typing import List, Dict, Any, Tuple

# Reliable source identifiers (same list as check_article.py)
RELIABLE_SOURCES = {
    "inquirer", "philstar", "manila bulletin", "abs-cbn",
    "cnn philippines", "gma", "rappler", "sunstar",
    "businessmirror", "pna", "philippine news agency",
    "bbc", "reuters", "ap", "associated press",
    "new york times", "the guardian",
    # Cebuano / Visayas regional outlets
    "superbalita", "cebu daily news", "sunstar cebu",
    "the freeman", "cebu pacific", "mb cebu", "cebu",
    "sugbo", "visayas", "mindanao daily", "sunstar davao",
}

# Thresholds
# Per-title comparison: a sentence is corroborated if its similarity to ANY
# individual source title meets this threshold. Lower than the concatenated
# threshold because a direct title match is a precise corroboration signal.
PER_TITLE_SIMILARITY_THRESHOLD = 0.60
# Concatenated pattern fallback (used when per-title comparison isn't possible)
SENTENCE_SIMILARITY_THRESHOLD = 0.70
DEVIATION_MINOR_THRESHOLD = 0.30      # deviation_score >= this → MINOR DEVIATION
DEVIATION_SIGNIFICANT_THRESHOLD = 0.65  # deviation_score >= this → SIGNIFICANT DEVIATION

# Cap how many sentences we analyze (performance guard for long articles)
MAX_SENTENCES = 20


def _is_reliable(source: str) -> bool:
    src = source.lower()
    return any(r in src for r in RELIABLE_SOURCES)


def _split_sentences(text: str) -> List[str]:
    """Naively split text into sentences on . ! ? boundaries."""
    raw = re.split(r"(?<=[.!?])\s+", text.strip())
    # Filter very short fragments (less than 5 words)
    return [s.strip() for s in raw if len(s.split()) >= 5]


def build_external_pattern(web_results: List[Dict], db_results: List[Dict]) -> str:
    """
    Concatenate titles from reliable web and DB sources into a single
    'pattern string' representing what reputable journalism says on this topic.

    Returns:
        str: Combined pattern text, or empty string if no reliable sources found.
    """
    fragments = []

    for r in web_results:
        source = r.get("source", "")
        title = r.get("title", "").strip()
        if title and _is_reliable(source):
            fragments.append(title)

    for r in db_results:
        source = r.get("source", "")
        title = r.get("title", "").strip()
        if title and _is_reliable(source):
            fragments.append(title)

    # Fall back to ALL sources (reliable or not) if none found
    if not fragments:
        for r in web_results + db_results:
            title = r.get("title", "").strip()
            if title:
                fragments.append(title)

    return " | ".join(fragments)


def _get_minilm():
    """Lazy import of MiniLM to avoid circular imports."""
    from checker.internal.core import get_minilm_model
    return get_minilm_model()


def _cosine(a, b) -> float:
    """Compute cosine similarity between two numpy vectors."""
    import numpy as np
    norm_a = float(np.linalg.norm(a))
    norm_b = float(np.linalg.norm(b))
    if norm_a == 0 or norm_b == 0:
        return 0.0
    return float(np.dot(a, b) / (norm_a * norm_b))


def compare_to_external_pattern(
    article_text: str,
    web_results: List[Dict],
    db_results: List[Dict] = None,
) -> Dict[str, Any]:
    """
    Compare the article's sentences against individual external source titles.

    For each article sentence, compute the maximum cosine similarity against
    EACH individual source title embedding (not a single concatenated blob).
    A sentence is corroborated if it matches ANY single title at
    >= PER_TITLE_SIMILARITY_THRESHOLD.  This fixes the false-positive 'extra
    claim' that occurred when a headline matched an external source title but
    got diluted by the concatenated embedding of all other titles.

    Args:
        article_text (str): The full article or claim to analyze.
        web_results (list): Google News results (dicts with 'title', 'source').
        db_results (list): Local DB results (dicts with 'title', 'source').

    Returns:
        dict with keys:
            - external_pattern (str): Summary of what reputable sources say.
            - extra_claims (list[str]): Sentences not found in external pattern.
            - corroborated_claims (list[str]): Sentences aligned with pattern.
            - deviation_score (float): 0-1 fraction of sentences that deviate.
            - verdict (str): "FOLLOWS PATTERN" | "MINOR DEVIATION" | "SIGNIFICANT DEVIATION"
            - reliable_source_count (int): Number of reliable sources used.
            - all_source_titles (list[str]): All titles from external sources.
    """
    if db_results is None:
        db_results = []

    # ── Build external pattern ──────────────────────────────────────────────
    external_pattern = build_external_pattern(web_results, db_results)

    # Collect all titles for display
    all_titles = []
    for r in web_results + db_results:
        t = r.get("title", "").strip()
        if t:
            all_titles.append((t, r.get("source", "")))

    reliable_count = sum(1 for r in web_results + db_results if _is_reliable(r.get("source", "")))

    # If no external sources at all, can't compare
    if not external_pattern.strip():
        return {
            "external_pattern": "",
            "extra_claims": [],
            "corroborated_claims": [],
            "deviation_score": 0.0,
            "verdict": "NO EXTERNAL SOURCES",
            "reliable_source_count": 0,
            "all_source_titles": [],
        }

    # ── Split article into sentences ────────────────────────────────────────
    sentences = _split_sentences(article_text)[:MAX_SENTENCES]

    if not sentences:
        return {
            "external_pattern": external_pattern,
            "extra_claims": [],
            "corroborated_claims": [],
            "deviation_score": 0.0,
            "verdict": "FOLLOWS PATTERN",
            "reliable_source_count": reliable_count,
            "all_source_titles": all_titles,
        }

    # Collect unique source titles for per-title comparison
    source_titles_list = []
    for r in web_results + db_results:
        t = r.get("title", "").strip()
        if t:
            source_titles_list.append(t)
    # Deduplicate while preserving order
    seen_keys: set = set()
    unique_titles: List[str] = []
    for t in source_titles_list:
        key = t.lower()[:60]
        if key not in seen_keys:
            seen_keys.add(key)
            unique_titles.append(t)

    # ── Embed sentences + individual source titles in one batched call ──────
    # Comparing each sentence to EACH title individually (not a concatenated
    # blob) prevents identical headlines from being diluted and falsely flagged
    # as "extra claims" — the original bug reported in user testing.
    try:
        minilm = _get_minilm()
        all_texts = sentences + unique_titles
        embeddings = minilm.encode(all_texts, batch_size=64, show_progress_bar=False)
        sentence_embeddings = embeddings[:len(sentences)]
        title_embeddings = embeddings[len(sentences):]
    except Exception:
        # Graceful degradation — can't run MiniLM, return neutral result
        return {
            "external_pattern": external_pattern,
            "extra_claims": [],
            "corroborated_claims": list(sentences),
            "deviation_score": 0.0,
            "verdict": "FOLLOWS PATTERN",
            "reliable_source_count": reliable_count,
            "all_source_titles": all_titles,
        }

    # ── Classify each sentence against individual titles ────────────────────
    # A sentence is corroborated if it matches ANY single source title at
    # >= PER_TITLE_SIMILARITY_THRESHOLD.
    extra_claims: List[str] = []
    corroborated_claims: List[str] = []

    for sent, s_emb in zip(sentences, sentence_embeddings):
        if len(title_embeddings) > 0:
            # Compute similarity to each individual title; take the maximum
            max_sim = max((_cosine(s_emb, t_emb) for t_emb in title_embeddings), default=0.0)
            corroborated = max_sim >= PER_TITLE_SIMILARITY_THRESHOLD
        else:
            corroborated = False

        if corroborated:
            corroborated_claims.append(sent)
        else:
            extra_claims.append(sent)

    # ── Compute deviation score ─────────────────────────────────────────────
    total = len(sentences)
    deviation_score = round(len(extra_claims) / total, 3) if total > 0 else 0.0

    # ── Determine verdict ───────────────────────────────────────────────────
    if deviation_score >= DEVIATION_SIGNIFICANT_THRESHOLD:
        verdict = "SIGNIFICANT DEVIATION"
    elif deviation_score >= DEVIATION_MINOR_THRESHOLD:
        verdict = "MINOR DEVIATION"
    else:
        verdict = "FOLLOWS PATTERN"

    return {
        "external_pattern": external_pattern,
        "extra_claims": extra_claims,
        "corroborated_claims": corroborated_claims,
        "deviation_score": deviation_score,
        "verdict": verdict,
        "reliable_source_count": reliable_count,
        "all_source_titles": all_titles,
    }