import fitz  # PyMuPDF
import spacy
import os
from recruitment import models


nlp = spacy.load("en_core_web_sm")

def extract_text_from_pdf(pdf_path):
    text = ""
    with fitz.open(pdf_path) as doc:
        for page in doc:
            text += page.get_text()
    return text

def extract_summary_from_pdf(pdf_path):
    if not os.path.exists(pdf_path):
        return {'error': 'File not found'}

    text = extract_text_from_pdf(pdf_path)
    doc = nlp(text)
    summary = {
        'name': doc.ents[0].text if doc.ents else '',
        'skills': [chunk.text for chunk in doc.noun_chunks if len(chunk.text.split()) > 1],
        'summary': text[:500]
    }
    return summary


def dashboard_callback(request, context):
    total_jobs = models.Job.objects.count()
    total_candidates = models.Candidate.objects.count()
    jobs = models.Job.objects.all()
    job_titles = [job.title for job in jobs]
    job_app_counts = [job.candidates.count() for job in jobs]

    context.update({
        "total_jobs": total_jobs,
        "total_candidates": total_candidates,
        "job_titles": job_titles,
        "job_app_counts": job_app_counts,
    })
    return context