"""
scorer.py — Resume scoring module.
 
Computes:
  1. Resume Base Score  (0–100) based on resume content analysis
  2. ATS Score          (0–100) combining base score + job match similarity
 
Scoring rubric (Base Score):
  - Skills richness   : up to 20 pts
  - Experience section: up to 30 pts
  - Projects section  : up to 20 pts
  - Education section : up to 10 pts
  - Resume length     : up to 10 pts
  - Skill diversity   : up to 10 pts
  TOTAL               : 100 pts
"""
 
import math
 
 
def compute_base_score(
    text: str,
    sections: dict,
    skills: dict,
) -> dict:
    """
    Compute the resume base score from its content.
 
    Args:
        text    : full resume text
        sections: output of nlp_utils.detect_sections()
        skills  : output of nlp_utils.extract_skills()
 
    Returns:
        dict with:
            'total'      : overall score (0–100)
            'breakdown'  : per-category score dict
    """
    breakdown = {}
 
    # ── 1. Skills richness (0–20) ─────────────────────────────────────────
    tech_count = len(skills.get("technical", []))
    # 0 skills → 0, 5 skills → 10, 10+ skills → 20
    skills_score = min(20, int((tech_count / 10) * 20))
    breakdown["Skills"] = skills_score
 
    # ── 2. Experience section (0–30) ──────────────────────────────────────
    if sections.get("experience"):
        # More experience-related content = higher score
        exp_text = _extract_section_text(text, ["experience", "employment", "work history"])
        exp_words = len(exp_text.split())
        # 0 words = 0, 100+ words = 30
        exp_score = min(30, int((exp_words / 100) * 30))
        exp_score = max(exp_score, 10 if sections.get("experience") else 0)
    else:
        exp_score = 0
    breakdown["Experience"] = exp_score
 
    # ── 3. Projects section (0–20) ────────────────────────────────────────
    if sections.get("projects"):
        proj_text = _extract_section_text(text, ["project"])
        proj_words = len(proj_text.split())
        proj_score = min(20, int((proj_words / 60) * 20))
        proj_score = max(proj_score, 8 if sections.get("projects") else 0)
    else:
        proj_score = 0
    breakdown["Projects"] = proj_score
 
    # ── 4. Education section (0–10) ───────────────────────────────────────
    breakdown["Education"] = 10 if sections.get("education") else 0
 
    # ── 5. Resume length (0–10) ───────────────────────────────────────────
    word_count = len(text.split())
    # Ideal range: 300–700 words
    if word_count >= 700:
        length_score = 10
    elif word_count >= 300:
        length_score = int(5 + ((word_count - 300) / 400) * 5)
    elif word_count >= 100:
        length_score = int((word_count / 300) * 5)
    else:
        length_score = 0
    breakdown["Length"] = length_score
 
    # ── 6. Skill diversity (0–10) ─────────────────────────────────────────
    # Reward having both technical AND soft skills
    has_tech = len(skills.get("technical", [])) >= 3
    has_soft = len(skills.get("soft", [])) >= 1
    has_summary = sections.get("summary", False)
    diversity_score = sum([has_tech * 5, has_soft * 3, has_summary * 2])
    breakdown["Diversity"] = min(10, diversity_score)
 
    total = sum(breakdown.values())
 
    return {
        "total": min(100, total),
        "breakdown": breakdown,
    }
 
 
def compute_ats_score(base_score: float, job_match_score: float) -> float:
    """
    Compute final ATS score.
 
    Formula: ATS = 0.6 × base_score + 0.4 × job_match_score
    Capped at 100.
 
    Args:
        base_score     : resume base score (0–100)
        job_match_score: job description match percentage (0–100)
 
    Returns:
        ATS score as a float (0–100), rounded to 1 decimal place.
    """
    ats = (0.6 * base_score) + (0.4 * job_match_score)
    return round(min(100.0, ats), 1)
 
 
# ---------------------------------------------------------------------------
# Internal helpers
# ---------------------------------------------------------------------------
 
def _extract_section_text(text: str, keywords: list) -> str:
    """
    Attempt to extract the content under a section heading.
 
    Searches for lines containing any of the keywords and returns
    all text until the next section-like heading.
 
    Args:
        text    : full resume text
        keywords: list of lowercase keywords to identify the section heading
 
    Returns:
        Extracted section text (may be empty string).
    """
    lines = text.splitlines()
    in_section = False
    collected = []
 
    # Common heading indicators (short, possibly title-cased lines)
    def _is_heading(line: str) -> bool:
        stripped = line.strip()
        return (
            len(stripped) < 60
            and stripped
            and stripped == stripped.upper()
            or any(
                kw in stripped.lower()
                for kw in [
                    "skills", "education", "experience", "project",
                    "certification", "summary", "objective", "awards",
                    "contact", "languages", "interests",
                ]
            )
        )
 
    for line in lines:
        line_lower = line.lower().strip()
        if any(kw in line_lower for kw in keywords) and len(line.strip()) < 60:
            in_section = True
            continue
        if in_section:
            # Stop collecting at the next major heading
            if _is_heading(line) and not any(kw in line.lower() for kw in keywords):
                break
            collected.append(line)
 
    return " ".join(collected)