ats_core.py

import re
import logging
from io import BytesIO
from typing import Dict, Any

import pdfplumber
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity

import nltk
from nltk.corpus import stopwords
from nltk import pos_tag
from nltk.tokenize import wordpunct_tokenize  # ✅ SAFE TOKENIZER

logging.getLogger("pdfminer").setLevel(logging.ERROR)

STOPWORDS = set(stopwords.words("english"))

model = SentenceTransformer("all-MiniLM-L6-v2")

def f(x):
    return float(round(x, 2))

def clean(text: str) -> str:
    text = text.lower()
    text = re.sub(r"[^a-z0-9\s\-]", " ", text)
    return re.sub(r"\s+", " ", text).strip()

def extract_pdf_text(file_bytes: bytes) -> str:
    text = ""
    with pdfplumber.open(BytesIO(file_bytes)) as pdf:
        for page in pdf.pages:
            if page.extract_text():
                text += page.extract_text() + "\n"
    return text.strip()

def extract_email(text: str):
    m = re.search(r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}", text)
    return m.group(0) if m else None

def extract_phone(text: str):
    patterns = [
        r"\b(\+91[-\s]?)?[6-9]\d{9}\b",
        r"\b\+?\d{1,3}[-\s]?\(?\d{2,4}\)?[-\s]?\d{3,4}[-\s]?\d{4}\b"
    ]
    for p in patterns:
        m = re.search(p, text)
        if m:
            return m.group(0)
    return None

def extract_name(text: str):
    BAD = {
        "resume", "curriculum", "vitae",
        "engineer", "developer", "analyst",
        "software", "machine", "data"
    }

    lines = [l.strip() for l in text.split("\n") if l.strip()]
    for line in lines[:5]:
        words = line.split()
        if 2 <= len(words) <= 4 and all(w[0].isupper() for w in words):
            low = line.lower()
            if not any(b in low for b in BAD):
                return line
    return None

def embed_sim(a: str, b: str) -> float:
    emb = model.encode([a, b])
    return float(cosine_similarity([emb[0]], [emb[1]])[0][0])

def chunk_text(text: str, size=120):
    words = text.split()
    return [" ".join(words[i:i + size]) for i in range(0, len(words), size)]

def chunked_similarity(long_text: str, short_text: str) -> float:
    chunks = chunk_text(long_text)
    if not chunks:
        return 0.0
    sims = [embed_sim(c, short_text) for c in chunks]
    sims.sort(reverse=True)
    return sum(sims[:5]) / min(5, len(sims))

def extract_keywords(text: str):
    tokens = wordpunct_tokenize(text)  # ✅ NO punkt, NO punkt_tab
    tagged = pos_tag(tokens)
    return {
        w for w, t in tagged
        if t.startswith("NN") and w not in STOPWORDS and len(w) > 2
    }

def formatting_score(text: str) -> float:
    score = 0
    t = text.lower()
    if "experience" in t: score += 2
    if "skills" in t: score += 2
    if "education" in t: score += 2
    if "-" in text or "•" in text: score += 2
    wc = len(text.split())
    if 300 <= wc <= 900: score += 2
    return score / 10.0

def generic_penalty(text: str) -> float:
    GENERIC = [
        "hardworking", "team player", "looking for opportunity",
        "learn new things", "technology", "computer",
        "passionate", "motivated", "self learner"
    ]
    penalty = sum(0.05 for g in GENERIC if g in text)
    return min(penalty, 0.20)

def verdict(score: float) -> str:
    if score < 35: return "Poor Fit"
    if score < 50: return "Below Average"
    if score < 65: return "Average Fit"
    if score < 80: return "Good Fit"
    return "Strong Fit"

def calibrate(raw: float) -> float:
    RAW_MIN, RAW_MAX = 0.08, 0.70
    raw = max(min(raw, RAW_MAX), RAW_MIN)
    scaled = (raw - RAW_MIN) / (RAW_MAX - RAW_MIN)
    return 20 + scaled * 70  # → [20, 90]

def ats_score(resume_text: str, jd_text: str, role: str) -> Dict[str, Any]:
    resume_clean = clean(resume_text)
    jd_clean = clean(jd_text)

    # Contact info
    name = extract_name(resume_text)
    email = extract_email(resume_text)
    phone = extract_phone(resume_text)

    # Similarities
    skill_sim = chunked_similarity(resume_clean, jd_clean)
    exp_sim = chunked_similarity(resume_clean, jd_clean)

    # Keywords
    r_kw = extract_keywords(resume_clean)
    j_kw = extract_keywords(jd_clean)
    kw_ratio = min(len(r_kw & j_kw) / max(len(j_kw), 1), 1.0)

    # Formatting
    fmt = formatting_score(resume_clean)

    # Weights
    if role in {"researcher", "research_engineer"}:
        w_skill, w_kw, w_exp, w_fmt = 0.45, 0.20, 0.25, 0.10
    else:
        w_skill, w_kw, w_exp, w_fmt = 0.40, 0.25, 0.25, 0.10

    raw = (
        w_skill * skill_sim +
        w_kw * kw_ratio +
        w_exp * exp_sim +
        w_fmt * fmt
    )

    raw -= generic_penalty(resume_clean)
    raw = max(raw, 0.0)

    final_score = f(calibrate(raw))

    # Component maxima
    MAX = {
        "skill": 40,
        "keyword": 30,
        "experience": 20,
        "formatting": 10
    }

    components = {
        "skill": skill_sim * MAX["skill"],
        "keyword": kw_ratio * MAX["keyword"],
        "experience": exp_sim * MAX["experience"],
        "formatting": fmt * MAX["formatting"]
    }

    component_sum = sum(components.values()) or 1.0
    scale = final_score / component_sum

    breakdown = {
        "skill_match": {
            "score": f(components["skill"] * scale),
            "max": MAX["skill"]
        },
        "keyword_match": {
            "score": f(components["keyword"] * scale),
            "max": MAX["keyword"]
        },
        "experience_match": {
            "score": f(components["experience"] * scale),
            "max": MAX["experience"]
        },
        "formatting": {
            "score": f(components["formatting"] * scale),
            "max": MAX["formatting"]
        }
    }

    return {
        "name": name,
        "email": email,
        "phone": phone,
        "ats_score": final_score,
        "verdict": verdict(final_score),
        "score_breakdown": breakdown,
        "missing_keywords": sorted(j_kw - r_kw)[:10]
    }