import fitz  # PyMuPDF for PDF processing
import requests
import json
import re
from io import BytesIO
import supabase
from config import SUPABASE_URL, SUPABASE_KEY, HF_API_TOKEN, HF_HEADERS, supabase, HF_MODELS, query

# These functions will be called in the main.py file

def evaluate_resumes(uploaded_files, job_description):
    """Evaluates uploaded resumes and returns shortlisted candidates."""
    candidates = []
    for pdf_file in uploaded_files:
        resume_text = parse_resume(pdf_file)
        score = score_candidate(resume_text, job_description)
        email = extract_email(resume_text)

        # Generate a summary of the resume
        summary = summarize_resume(resume_text)

        candidates.append({
            "name": pdf_file.name,
            "resume": resume_text,
            "score": score,
            "email": email,
            "summary": summary
        })

        # Store all details including summary in Supabase
        store_in_supabase(resume_text, score, pdf_file.name, email, summary)

    return sorted(candidates, key=lambda x: x["score"], reverse=True)[:5]  # Return top 5 candidates

def parse_resume(pdf_file):
    """Extracts text from a resume PDF."""
    doc = fitz.open(stream=pdf_file.read(), filetype="pdf")
    text = "\n".join([page.get_text("text") for page in doc])
    return text

def extract_email(resume_text):
    """Extracts an email address from resume text."""
    match = re.search(r"[\w\.-]+@[\w\.-]+", resume_text)
    return match.group(0) if match else None

# Test on why score 0 is returned even though resume matches key words
# score_candidate function will use HuggingFace gemini model
def score_candidate(resume_text, job_description):
    """
    Scores the candidate's resume based on the job description using the Hugging Face API.

    :param resume_text: The extracted resume text.
    :param job_description: The job description for comparison.
    :return: A numerical score (default 0 if scoring fails).
    """
    payload = {"inputs": f"Resume: {resume_text}\nJob Description: {job_description}"}
    response_gemma = query(payload, model="gemma")  # Use Google Gemma Model for scoring

    if response_gemma is None:
        return 0  # Return 0 if API call fails

    try:
        return float(response_gemma.get("score", 0))  # Ensure score is always a float
    except (TypeError, ValueError):
        return 0  # Return 0 if score parsing fails

# summarize_resume function will use HuggingFace BART model
def summarize_resume(resume_text):
    """
    Summarizes the resume using Facebook's BART-Large-CNN model.
    
    :param resume_text: The extracted resume text.
    :return: A summarized version of the resume or an error message.
    """
    payload = {"inputs": resume_text}
    response_bart = query(payload, model="bart")

    if response_bart is None:
        return "Summary could not be generated."  # Handle API failures gracefully

    try:
        summary = response_bart[0].get("summary_text", "Summary not available.")
        return summary
    except (IndexError, KeyError):
        return "Summary not available."

def store_in_supabase(resume_text, score, candidate_name, email, summary):
    """
    Stores resume data in Supabase.

    :param resume_text: The extracted resume text.
    :param score: The candidate's score (must be a valid number).
    :param candidate_name: The candidate's name.
    :param email: Candidate's email address.
    :param summary: A summarized version of the resume.
    """
    if score is None:
        score = 0  # Ensure score is never NULL

    data = {
        "name": candidate_name,
        "resume": resume_text,
        "score": score,
        "email": email,
        "summary": summary
    }

    response = supabase.table("candidates").insert(data).execute()
    return response

# Test with 10 resumes, if they will be shortlisted
def generate_pdf_report(shortlisted_candidates):
    """Generates a PDF summary of shortlisted candidates."""
    pdf = BytesIO()
    doc = fitz.open()

    for candidate in shortlisted_candidates:
        page = doc.new_page()

        # Use the stored summary, or provide a fallback
        summary = candidate.get("summary", "No summary available")

        page.insert_text(
            (50, 50),
            f"Candidate: {candidate['name']}\n"
            f"Email: {candidate['email']}\n"
            f"Score: {candidate['score']}\n"
            f"Summary: {summary}"
        )

    doc.save(pdf)
    pdf.seek(0)
    return pdf