import spacy
from spacy.cli import download
import fitz  # PyMuPDF
import re
from transformers import pipeline

def load_models():
    # Try loading spaCy model; download if missing
    try:
        nlp = spacy.load("en_core_web_sm")
    except OSError:
        download("en_core_web_sm")
        nlp = spacy.load("en_core_web_sm")

    # Use a fast summarization model instead of GPT-2
    llm = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
    return nlp, llm

def parse_resume(uploaded_file, nlp):
    doc = fitz.open(stream=uploaded_file.read(), filetype="pdf")
    text = "\n".join(page.get_text() for page in doc)
    doc = nlp(text)

    name = [ent.text for ent in doc.ents if ent.label_ == "PERSON"]
    email = re.findall(r"[\w\.-]+@[\w\.-]+", text)
    skills = [token.text.lower() for token in doc if token.pos_ == "NOUN"]
    edu_keywords = ["bachelor", "master", "phd", "degree", "certification", "diploma"]
    edu = [sent.text for sent in doc.sents if any(k in sent.text.lower() for k in edu_keywords)]

    return text, {
        "name": name[0] if name else "N/A",
        "email": email[0] if email else "N/A",
        "skills": list(set(skills)),
        "education": edu,
    }

def get_recommendations(parsed):
    score = 50 + len(parsed["skills"]) % 50
    feedback = "Try adding more specific technical skills and quantifiable achievements."
    return score, feedback

def generate_career_insights(parsed, llm, suggestion_type="roadmap"):
    name = parsed.get("name", "Candidate")
    skills = ", ".join(parsed["skills"][:10]) if parsed["skills"] else "unspecified"
    education = "; ".join(parsed["education"][:3]) if parsed["education"] else "not mentioned"

    input_text_map = {
        "certifications": (
            f"Candidate has skills in: {skills}. With education: {education}. "
            "Summarize relevant certifications they can pursue."
        ),
        "degrees": (
            f"Given the education background: {education}, summarize higher education degrees "
            "that can help in career advancement."
        ),
        "roadmap": (
            f"Create a short 1-year career roadmap for someone with skills: {skills} "
            f"and education: {education}. Suggest goals."
        ),
        "counselor": (
            f"As a career advisor, suggest top 3 career moves for a person skilled in {skills} "
            f"with education in {education}."
        )
    }

    result = llm(input_text_map[suggestion_type], max_length=150, min_length=50, do_sample=False)
    return result[0]["summary_text"]