backend/services/report_generator.py

"""Utilities for assembling and exporting interview reports.

This module provides two primary helpers used by the recruiter dashboard:

``generate_llm_interview_report(application)``
    Given a candidate's ``Application`` record, assemble a plain‑text report
    summarising the interview.  Because the interview process currently
    executes entirely client‑side and does not persist questions or answers
    to the database, this report focuses on the information available on
    the server: the candidate's profile, the job requirements and a skills
    match score.  Should future iterations store richer interview data
    server‑side, this function can be extended to include question/answer
    transcripts, per‑question scores and LLM‑generated feedback.

``create_pdf_report(report_text)``
    Convert a multi‑line string into a simple PDF.  The implementation
    leverages Matplotlib's PDF backend (available by default) to avoid
    heavyweight dependencies such as ReportLab or WeasyPrint, which are
    absent from the runtime environment.  Text is wrapped and split
    across multiple pages as necessary.
"""

from __future__ import annotations

import json
from io import BytesIO
import textwrap
from typing import List

import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages

def generate_llm_interview_report(application) -> str:
    """Generate a human‑readable interview report for a candidate.

    The report includes the candidate's name and email, job details,
    application date, a computed skills match summary and placeholder
    sections for future enhancements.  If server‑side storage of
    question/answer pairs is added later, this function can be updated
    to incorporate those details.

    Parameters
    ----------
    application : backend.models.database.Application
        The SQLAlchemy Application instance representing the candidate's
        job application.  Assumed to have related ``job`` and
        ``date_applied`` attributes available.

    Returns
    -------
    str
        A multi‑line string containing the report contents.
    """
    # Defensive imports to avoid circular dependencies at import time
    try:
        from datetime import datetime  # noqa: F401
    except Exception:
        pass

    # Extract candidate skills and job skills
    try:
        candidate_features = json.loads(application.extracted_features) if application.extracted_features else {}
    except Exception:
        candidate_features = {}
    candidate_skills: List[str] = candidate_features.get('skills', []) or []

    job_skills: List[str] = []
    try:
        job_skills = json.loads(application.job.skills) if application.job and application.job.skills else []
    except Exception:
        job_skills = []

    # Compute skills match ratio and label.  Normalise to lower case for
    # comparison and avoid dividing by zero when ``job_skills`` is empty.
    candidate_set = {s.strip().lower() for s in candidate_skills}
    job_set = {s.strip().lower() for s in job_skills}
    common = candidate_set & job_set
    ratio = len(common) / len(job_set) if job_set else 0.0

    if ratio >= 0.75:
        score_label = 'Excellent'
    elif ratio >= 0.5:
        score_label = 'Good'
    elif ratio >= 0.25:
        score_label = 'Medium'
    else:
        score_label = 'Poor'

    # Assemble report lines
    lines: List[str] = []
    lines.append('Interview Report')
    lines.append('=================')
    lines.append('')
    lines.append(f'Candidate Name: {application.name}')
    lines.append(f'Candidate Email: {application.email}')
    if application.job:
        lines.append(f'Job Applied: {application.job.role}')
        lines.append(f'Company: {application.job.company}')
    else:
        lines.append('Job Applied: N/A')
        lines.append('Company: N/A')
    # Format date_applied if available
    try:
        date_str = application.date_applied.strftime('%Y-%m-%d') if application.date_applied else 'N/A'
    except Exception:
        date_str = 'N/A'
    lines.append(f'Date Applied: {date_str}')
    lines.append('')
    lines.append('Skills Match Summary:')
    # Represent required and candidate skills as comma‑separated lists.  Use
    # title‑case for presentation and handle empty lists gracefully.
    formatted_job_skills = ', '.join(job_skills) if job_skills else 'N/A'
    formatted_candidate_skills = ', '.join(candidate_skills) if candidate_skills else 'N/A'
    formatted_common = ', '.join(sorted(common)) if common else 'None'
    lines.append(f'  Required Skills: {formatted_job_skills}')
    lines.append(f'  Candidate Skills: {formatted_candidate_skills}')
    lines.append(f'  Skills in Common: {formatted_common}')
    lines.append(f'  Match Ratio: {ratio * 100:.0f}%')
    lines.append(f'  Score: {score_label}')
    lines.append('')
    lines.append('Interview Transcript & Evaluation:')
    try:
        if application.interview_log:
            try:
                qa_log = json.loads(application.interview_log)
            except Exception:
                qa_log = []

            if qa_log:
                for idx, entry in enumerate(qa_log, 1):
                    q = entry.get("question", "N/A")
                    a = entry.get("answer", "N/A")
                    eval_score = entry.get("evaluation", {}).get("score", "N/A")
                    eval_feedback = entry.get("evaluation", {}).get("feedback", "N/A")

                    lines.append(f"\nQuestion {idx}: {q}")
                    lines.append(f"Answer: {a}")
                    lines.append(f"Score: {eval_score}")
                    lines.append(f"Feedback: {eval_feedback}")
            else:
                lines.append("No interview log data recorded.")
        else:
            lines.append("No interview log data recorded.")
    except Exception as e:
        lines.append(f"Error loading interview log: {e}")

    return '\n'.join(lines)


def create_pdf_report(report_text: str) -> BytesIO:
    """
    Alternative implementation using reportlab for better PDF generation.
    Install with: pip install reportlab
    """
    try:
        from reportlab.lib.pagesizes import A4
        from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
        from reportlab.lib.units import inch
        from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, PageBreak
        from reportlab.lib.colors import HexColor
        
        buffer = BytesIO()
        doc = SimpleDocTemplate(
            buffer,
            pagesize=A4,
            rightMargin=0.75*inch,
            leftMargin=0.75*inch,
            topMargin=1*inch,
            bottomMargin=1*inch
        )
        
        # Create custom styles
        styles = getSampleStyleSheet()
        
        question_style = ParagraphStyle(
            'Question',
            parent=styles['Heading2'],
            fontSize=12,
            textColor=HexColor('#2C3E50'),
            spaceAfter=6,
            spaceBefore=12
        )
        
        answer_style = ParagraphStyle(
            'Answer',
            parent=styles['Normal'],
            fontSize=10,
            textColor=HexColor('#34495E'),
            leftIndent=20,
            spaceAfter=3
        )
        
        score_style = ParagraphStyle(
            'Score',
            parent=styles['Normal'],
            fontSize=10,
            textColor=HexColor('#27AE60'),
            leftIndent=20,
            fontName='Helvetica-Bold'
        )
        
        feedback_style = ParagraphStyle(
            'Feedback',
            parent=styles['Normal'],
            fontSize=10,
            textColor=HexColor('#E74C3C'),
            leftIndent=20,
            spaceAfter=6
        )
        
        # Build document content
        story = []
        lines = report_text.split('\n')
        
        for line in lines:
            stripped = line.strip()
            
            if stripped.startswith('Question'):
                story.append(Paragraph(stripped, question_style))
            elif stripped.startswith('Answer:'):
                story.append(Paragraph(stripped, answer_style))
            elif stripped.startswith('Score:'):
                story.append(Paragraph(stripped, score_style))
            elif stripped.startswith('Feedback:'):
                story.append(Paragraph(stripped, feedback_style))
            elif stripped:
                story.append(Paragraph(stripped, styles['Normal']))
            else:
                story.append(Spacer(1, 12))
        
        # Build PDF
        doc.build(story)
        buffer.seek(0)
        return buffer

__all__ = ['generate_llm_interview_report', 'create_pdf_report']