utils.py

# === Imports ===

# Standard Library
import os
import re
import json
import random
import subprocess
from io import BytesIO
from collections import Counter

# Third-Party Libraries
import fitz  # PyMuPDF
import requests
import spacy
import streamlit as st
from fuzzywuzzy import fuzz
from sentence_transformers import SentenceTransformer, util
from sklearn.feature_extraction.text import TfidfVectorizer
from huggingface_hub import InferenceClient
from openai import OpenAI

# Local Configuration
from config import (
    SUPABASE_URL, SUPABASE_KEY, HF_API_TOKEN, HF_HEADERS,
    supabase, HF_MODELS, query, embedding_model, client
)

# === Initialization ===

# # Hugging Face inference client for Gemma model
# client = InferenceClient(
#     model="tgi",
#     token=HF_API_TOKEN
# )

# Load or download spaCy model
try:
    nlp = spacy.load("en_core_web_sm")
except OSError:
    subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"])
    nlp = spacy.load("en_core_web_sm")


# === Core Resume Evaluation ===

def evaluate_resumes(uploaded_files, job_description, min_keyword_match=2):
    """
    Evaluate uploaded resumes and return shortlisted candidates with scores and summaries.
    """
    candidates, removed_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)
        summary = summarize_resume(resume_text)

        if score < 0.20:
            removed_candidates.append({"name": pdf_file.name, "reason": "Low confidence score (< 0.20)"})
            continue

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

    # 🔹 Step 2: Filter candidates based on keyword matches
    filtered_candidates, keyword_removed = filter_resumes_by_keywords(
        candidates, job_description, min_keyword_match
    )
    
    # 🔹 Step 3: Log removed candidates
    for name in keyword_removed:
        removed_candidates.append({"name": name, "reason": "Insufficient keyword matches"})
    
    # 🔹 Step 4: Ensure the final list is sorted by score and limit to top 5 candidates
    shortlisted_candidates = sorted(filtered_candidates, key=lambda x: x["score"], reverse=True)[:5]
    
    # 🔹 Step 4.5: Store shortlisted candidates in Supabase
    for candidate in shortlisted_candidates:
        try:
            store_in_supabase(
                resume_text=candidate["resume"],
                score=candidate["score"],
                candidate_name=candidate["name"],
                email=candidate["email"],
                summary=candidate["summary"]
            )
        except Exception as e:
            print(f"❌ Failed to store {candidate['name']} in Supabase: {e}")

    # 🔹 Step 5: Ensure return value is always a list
    if not isinstance(shortlisted_candidates, list):
        print("⚠️ ERROR: shortlisted_candidates is not a list! Returning empty list.")
        return [], removed_candidates

    return shortlisted_candidates, removed_candidates

# === Keyword & Scoring Functions ===

def extract_keywords(text, top_n=10):
    """
    Extracts top keywords from the job description using spaCy and TF-IDF.
    """
    if not text.strip():
        return []

    doc = nlp(text.lower())
    keywords = [t.text for t in doc if t.pos_ in {"NOUN", "PROPN", "VERB", "ADJ"} and not t.is_stop]

    if not keywords:
        return []

    try:
        tfidf = TfidfVectorizer(stop_words="english", ngram_range=(1, 2))
        matrix = tfidf.fit_transform([" ".join(keywords)])
        scores = matrix.toarray()[0]
        features = tfidf.get_feature_names_out()
        ranked = sorted(zip(features, scores), key=lambda x: x[1], reverse=True)

        return [kw for kw, _ in ranked[:top_n]]

    except ValueError:
        return []


def filter_resumes_by_keywords(resumes, job_description, min_keyword_match=2):
    """
    Filters resumes by keyword match using fuzzy logic.
    """
    job_keywords = extract_keywords(job_description)
    if len(job_keywords) < min_keyword_match:
        st.warning("⚠️ Job description too short or missing for keyword filtering.")
        return resumes, []

    filtered, removed = [], []

    for resume in resumes:
        matched = {
            keyword for keyword in job_keywords
            if any(fuzz.partial_ratio(keyword, word) > 80 for word in resume["resume"].lower().split())
        }

        if len(matched) >= min_keyword_match:
            filtered.append(resume)
        else:
            removed.append(resume["name"])

    return filtered, removed


def score_candidate(resume_text, job_description):
    """
    Computes cosine similarity between resume and job description using embeddings.
    """
    try:
        resume_vec = embedding_model.encode(resume_text, convert_to_tensor=True)
        job_vec = embedding_model.encode(job_description, convert_to_tensor=True)
        score = util.pytorch_cos_sim(resume_vec, job_vec).item()
        return round(score, 4)
    except Exception as e:
        print(f"Error computing similarity: {e}")
        return 0


# === Text Extraction & Summarization ===

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


def extract_email(resume_text):
    """
    Extracts the first valid email found in text.
    """
    match = re.search(r"[\w\.-]+@[\w\.-]+", resume_text)
    return match.group(0) if match else None

def summarize_resume(resume_text):
    prompt = (
        "You are an expert technical recruiter. Extract a professional summary for this candidate based on their resume text. "
        "Include: full name (if found), job title, years of experience, key technologies/tools, industries worked in, and certifications. "
        "Format it as a professional summary paragraph.\n\n"
        f"Resume:\n{resume_text}\n\n"
        "Summary:"
    )

    try:
        response = client.chat.completions.create(
            model="tgi",
            messages=[{"role": "user", "content": prompt}],
            temperature=0.5,
            max_tokens=300,
        )
        result = response.choices[0].message.content.strip()

        # Clean up generic lead-ins from the model
        cleaned = re.sub(
            r"^(Sure,|Certainly,)?\s*(here is|here’s|this is)?\s*(the)?\s*(extracted)?\s*(professional)?\s*summary.*?:\s*",
            "", result, flags=re.IGNORECASE
        ).strip()

        return cleaned

    except Exception as e:
        print(f"❌ Error generating structured summary: {e}")
        return "Summary unavailable due to API issues."

# === Data Storage & Reporting ===

def store_in_supabase(resume_text, score, candidate_name, email, summary):
    """
    Saves candidate data to the Supabase table.
    """
    data = {
        "name": candidate_name,
        "resume": resume_text,
        "score": score or 0,
        "email": email,
        "summary": summary
    }

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


def generate_pdf_report(shortlisted_candidates, questions=None):
    """
    Creates a PDF report summarizing top candidates and interview questions.
    """
    pdf = BytesIO()
    doc = fitz.open()

    for candidate in shortlisted_candidates:
        page = doc.new_page()
        info = (
            f"Candidate: {candidate['name']}\n"
            f"Email: {candidate['email']}\n"
            f"Score: {candidate['score']}\n\n"
            f"Summary:\n{candidate.get('summary', 'No summary available')}"
        )
        page.insert_textbox(fitz.Rect(50, 50, 550, 750), info, fontsize=11, fontname="helv", align=0)

    if questions:
        q_page = doc.new_page()
        q_text = "Suggested Interview Questions:\n\n" + "\n".join(questions)
        q_page.insert_textbox(fitz.Rect(50, 50, 550, 750), q_text, fontsize=11, fontname="helv", align=0)

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


def generate_interview_questions_from_summaries(candidates):
    if not isinstance(candidates, list):
        raise TypeError("Expected a list of candidate dictionaries.")

    summaries = " ".join(c.get("summary", "") for c in candidates)

    prompt = (
        "Based on the following summary of a top candidate for a job role, "
        "generate 5 thoughtful, general interview questions that would help a recruiter assess their fit:\n\n"
        f"{summaries}"
    )

    try:
        response = client.chat.completions.create(
            model="tgi",
            messages=[{"role": "user", "content": prompt}],
            temperature=0.7,
            max_tokens=500,
)

        result = response.choices[0].message.content

        # Clean and normalize questions
        raw_questions = result.split("\n")
        questions = []

        for q in raw_questions:
            q = q.strip()

            # Skip empty lines and markdown headers
            if not q or re.match(r"^#+\s*", q):
                continue

            # Remove leading bullets like "1.", "1)", "- 1.", etc.
            q = re.sub(r"^(?:[-*]?\s*)?(?:Q?\d+[\.\)\-]?\s*)+", "", q)

            # Remove markdown bold/italics (**, *, etc.)
            q = re.sub(r"[*_]+", "", q)
            
            # Remove duplicate trailing punctuation
            q = q.strip(" .")

            questions.append(q.strip())
            
        return [f"Q{i+1}. {q}" for i, q in enumerate(questions[:5])] or ["⚠️ No questions generated."]
    
    except Exception as e:
        print(f"❌ Error generating interview questions: {e}")
        return ["⚠️ Error generating questions."]