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, embedding_model
from sentence_transformers import SentenceTransformer, util

# These functions will be called in the app.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

def score_candidate(resume_text, job_description):
    """
    Scores the candidate's resume based on the job description using sentence-transformers.
    
    :param resume_text: The extracted resume text.
    :param job_description: The job description for comparison.
    :return: A numerical score (cosine similarity between 0 and 1).
    """
    try:
        # Generate embeddings
        resume_embedding = embedding_model.encode(resume_text, convert_to_tensor=True)
        job_embedding = embedding_model.encode(job_description, convert_to_tensor=True)

        # Compute cosine similarity
        score = util.pytorch_cos_sim(resume_embedding, job_embedding).item()

        return round(score, 4)  # Return similarity score rounded to 4 decimal places

    except Exception as e:
        print(f"Error computing similarity score: {e}")
        return 0  # Return 0 if scoring fails

def summarize_resume(resume_text):
    """
    Summarizes a resume using the Google gemma model.
    
    :param resume_text: The resume text to summarize.
    :return: A summarized version of the resume.
    """
    payload = {"inputs": f"Summarize this resume: {resume_text}"}
    
    response = query(payload, model="gemma")  # Use gemma for summarization

    if response is None:
        print("Error: API response is None")
        return "Summary could not be generated."

    # If the response is a list, extract the first element
    if isinstance(response, list) and len(response) > 0:
        response = response[0]

    try:
        if isinstance(response, dict) and "generated_text" in response:
            return response["generated_text"]
        else:
            print("Unexpected API response format:", response)
            return "Summary could not be generated."
    except (TypeError, ValueError) as e:
        print(f"Error parsing summary: {e}")
        return "Summary could not be generated."

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