"""Resume-to-job-description skill matcher powered by spaCy EntityRuler."""

from pathlib import Path

import gradio as gr
import jsonlines
import spacy
from spacy.cli import download

DATA_DIR = Path(__file__).parent
SKILL_PATTERNS = DATA_DIR / "skill_patterns.jsonl"

# Ensure the small English model exists before the Space starts serving traffic.
try:
    nlp = spacy.load("en_core_web_sm")
except OSError:
    download("en_core_web_sm")
    nlp = spacy.load("en_core_web_sm")

# Load custom skill patterns into the pipeline once at startup.
ruler = nlp.add_pipe("entity_ruler", after="parser")
ruler.from_disk(str(SKILL_PATTERNS))


def create_skill_set(doc):
    """Return upper-cased skill labels found in a spaCy Doc."""
    return {
        ent.label_.upper()[6:]
        for ent in doc.ents
        if "skill" in ent.label_.lower()
    }


def match_skills(job_skills, resume_skills):
    """Return the percent of job skills also present in the resume."""
    if not job_skills:
        return None
    return round(len(job_skills.intersection(resume_skills)) / len(job_skills) * 100, 0)


def match(resume_text, job_description):
    """Compare extracted skills and return a match score."""
    resume_text = (resume_text or "").strip()
    job_description = (job_description or "").strip()
    if not resume_text or not job_description:
        return "Paste both a resume and a job description."

    resume_skills = create_skill_set(nlp(resume_text.lower()))
    job_skills = create_skill_set(nlp(job_description.lower()))
    score = match_skills(job_skills, resume_skills)

    if score is None:
        return "No matching skill set."
    return score


demo = gr.Interface(
    fn=match,
    inputs=[
        gr.Textbox(lines=10, label="Resume details"),
        gr.Textbox(lines=10, label="Job description"),
    ],
    outputs=gr.Textbox(label="Match score"),
    title="Resume Matcher",
    description=(
        "A spaCy-based resume matcher that compares resume skills with a job "
        "description and returns a match percentage."
    ),
    examples=[
        [
            "Experienced Python developer with machine learning, SQL, and Docker.",
            "Looking for a Python engineer with machine learning and cloud experience.",
        ]
    ],
)

if __name__ == "__main__":
    demo.queue().launch()