537 lines
25 KiB
Python
537 lines
25 KiB
Python
import os
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import json
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import logging
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import requests
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from PyPDF2 import PdfReader
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from datetime import datetime
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from django.db import transaction
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from .utils import create_zoom_meeting
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from recruitment.models import Candidate
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from . linkedin_service import LinkedInService
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from django.shortcuts import get_object_or_404
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from . models import JobPosting
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from django.utils import timezone
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from .models import ScheduledInterview, ZoomMeeting, Candidate, JobPosting, InterviewSchedule
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logger = logging.getLogger(__name__)
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OPENROUTER_API_KEY ='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'
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# OPENROUTER_MODEL = 'qwen/qwen-2.5-72b-instruct:free'
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# OPENROUTER_MODEL = 'openai/gpt-oss-20b:free'
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OPENROUTER_MODEL = 'openai/gpt-oss-20b'
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# OPENROUTER_MODEL = 'mistralai/mistral-small-3.2-24b-instruct:free'
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if not OPENROUTER_API_KEY:
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logger.warning("OPENROUTER_API_KEY not set. Resume scoring will be skipped.")
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def extract_text_from_pdf(file_path):
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print("text extraction")
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text = ""
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try:
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with open(file_path, "rb") as f:
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reader = PdfReader(f)
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for page in reader.pages:
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text += (page.extract_text() or "")
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except Exception as e:
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logger.error(f"PDF extraction failed: {e}")
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raise
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return text.strip()
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def ai_handler(prompt):
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print("model call")
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response = requests.post(
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url="https://openrouter.ai/api/v1/chat/completions",
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headers={
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"Authorization": f"Bearer {OPENROUTER_API_KEY}",
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"Content-Type": "application/json",
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},
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data=json.dumps({
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"model": OPENROUTER_MODEL,
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"messages": [{"role": "user", "content": prompt}],
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},
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)
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)
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res = {}
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print(response.status_code)
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if response.status_code == 200:
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res = response.json()
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content = res["choices"][0]['message']['content']
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try:
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# print(content)
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content = content.replace("```json","").replace("```","")
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res = json.loads(content)
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print("success response")
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return {"status": "success", "data": res}
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except Exception as e:
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print(e)
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return {"status": "error", "data": str(e)}
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else:
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print("error response")
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return {"status": "error", "data": response.json()}
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# def handle_reume_parsing_and_scoring(pk):
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# from django.db import transaction
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# logger.info(f"Scoring resume for candidate {pk}")
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# instance = Candidate.objects.get(pk=pk)
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# try:
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# file_path = instance.resume.path
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# with transaction.atomic():
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# if not os.path.exists(file_path):
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# logger.warning(f"Resume file not found: {file_path}")
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# return
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# resume_text = extract_text_from_pdf(file_path)
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# job_detail= f"{instance.job.description} {instance.job.qualifications}"
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# resume_parser_prompt = f"""
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# You are an expert resume parser and summarizer. Given a resume in plain text format, extract and organize the following key-value information into a clean, valid JSON object:
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# full_name: Full name of the candidate
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# current_title: Most recent or current job title
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# location: City and state (or country if outside the U.S.)
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# contact: Phone number and email (as a single string or separate fields)
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# linkedin: LinkedIn profile URL (if present)
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# github: GitHub or portfolio URL (if present)
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# summary: Brief professional profile or summary (1–2 sentences)
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# education: List of degrees, each with:
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# institution
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# degree
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# year
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# gpa (if provided)
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# relevant_courses (as a list, if mentioned)
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# skills: Grouped by category if possible (e.g., programming, big data, visualization), otherwise as a flat list of technologies/tools
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# experience: List of roles, each with:
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# company
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# job_title
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# location
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# start_date and end_date (or "Present" if applicable)
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# key_achievements (as a list of concise bullet points)
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# projects: List of notable projects (if clearly labeled), each with:
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# name
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# year
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# technologies_used
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# brief_description
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# Instructions:
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# Be concise but preserve key details.
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# Normalize formatting (e.g., “Jun. 2014” → “2014-06”).
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# Omit redundant or promotional language.
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# If a section is missing, omit the key or set it to null/empty list as appropriate.
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# Output only valid JSON—no markdown, no extra text.
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# Now, process the following resume text:
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# {resume_text}
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# """
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# resume_parser_result = ai_handler(resume_parser_prompt)
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# resume_scoring_prompt = f"""
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# You are an expert technical recruiter. Your task is to score the following candidate for the role based on the provided job criteria.
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# **Job Criteria:**
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# {job_detail}
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# **Candidate's Extracted Resume Json:**
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# \"\"\"
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# {resume_parser_result}
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# \"\"\"
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# **Your Task:**
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# Provide a response in strict JSON format with the following keys:
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# 1. 'match_score': A score from 0 to 100 representing how well the candidate fits the role.
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# 2. 'strengths': A brief summary of why the candidate is a strong fit, referencing specific criteria.
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# 3. 'weaknesses': A brief summary of where the candidate falls short or what criteria are missing.
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# 4. 'years_of_experience': The total number of years of professional experience mentioned in the resume as a numerical value (e.g., 6.5).
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# 5. 'criteria_checklist': An object where you rate the candidate's match for each specific criterion (e.g., {{'Python': 'Met', 'AWS': 'Not Mentioned'}}).
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# 6. 'criteria_checklist': An object where you rate the candidate's match for each specific criterion (e.g., {{'Python': 'Met', 'AWS': 'Not Mentioned'}}).
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# 7. 'category': Based on the content provided, determine the most fitting professional field or category for the individual. (e.g., {{"category" : "Data Science"}}) only output the category name and no other text example ('Software Development', 'correct') , ('Software Development and devops','wrong').
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# 8. 'most_recent_job_title': The candidate's most recent or current professional job title.
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# 9. 'recommendation': Provide a recommendation for the candidate (e.g., {{"recommendation": "
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# Conclusion and Minor Considerations
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# Overall Assessment: Highly Recommended Candidate.
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# [Candidate] is an exceptionally strong candidate for this role. His proven track record with the core technology stack (Django, Python, Docker, CI/CD) and relevant experience in large-scale, high-impact enterprise projects (Telecom BPM/MDM) make him an excellent technical fit. His fluency in Arabic and English directly addresses a major non-negotiable requirement.
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# The only minor area not explicitly mentioned is the mentoring aspect, but his senior level of experience and technical breadth strongly suggest he possesses the capability to mentor junior engineers.
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# The hiring manager should move forward with this candidate with high confidence.
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# ."}}).
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# 10. 'top_3_keywords': A list of the three most dominant and relevant technical skills or technologies from the resume that match the job criteria.
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# 11. 'job_fit_narrative': A single, concise sentence summarizing the core fit.
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# 12. 'language_fluency': A list of languages and their fluency levels mentioned.
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# 13. 'screening_stage_rating': A standardized rating (e.g., "A - Highly Qualified", "B - Qualified").
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# 14. 'min_req_met_bool': Boolean (true/false) indicating if all non-negotiable minimum requirements are met.
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# 15. 'soft_skills_score': A score (0-100) for inferred non-technical skills like leadership and communication.
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# 16. 'experience_industry_match': A score (0-100) for the relevance of the candidate's industry experience.
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# Only output valid JSON. Do not include any other text.
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# """
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# resume_scoring_result = ai_handler(resume_scoring_prompt)
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# print(resume_scoring_result)
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# instance.parsed_summary = str(resume_parser_result)
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# # Core Scores
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# instance.set_field('match_score', resume_scoring_result.get('match_score', 0)) # Set default for int
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# instance.set_field('years_of_experience', resume_scoring_result.get('years_of_experience', 0.0)) # Set default for float
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# instance.set_field('soft_skills_score', resume_scoring_result.get('soft_skills_score', 0))
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# instance.set_field('experience_industry_match', resume_scoring_result.get('experience_industry_match', 0))
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# # Screening & Funnel
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# instance.set_field('min_req_met_bool', resume_scoring_result.get('min_req_met_bool', False)) # Set default for bool
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# instance.set_field('screening_stage_rating', resume_scoring_result.get('screening_stage_rating', 'N/A'))
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# instance.set_field('most_recent_job_title', resume_scoring_result.get('most_recent_job_title', 'N/A'))
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# instance.set_field('top_3_keywords', resume_scoring_result.get('top_3_keywords', [])) # Set default for list
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# # Summaries & Narrative
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# instance.set_field('strengths', resume_scoring_result.get('strengths', ''))
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# instance.set_field('weaknesses', resume_scoring_result.get('weaknesses', ''))
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# instance.set_field('job_fit_narrative', resume_scoring_result.get('job_fit_narrative', ''))
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# instance.set_field('recommendation', resume_scoring_result.get('recommendation', ''))
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# # Structured Data
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# instance.set_field('criteria_checklist', resume_scoring_result.get('criteria_checklist', {})) # Set default for dict
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# instance.set_field('language_fluency', resume_scoring_result.get('language_fluency', [])) # Set default for list
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# instance.set_field('category', resume_scoring_result.get('category', 'Uncategorized')) # Use 'category' key
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# instance.is_resume_parsed = True
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# instance.save(update_fields=['ai_analysis_data', 'is_resume_parsed','parsed_summary'])
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# logger.info(f"Successfully scored resume for candidate {instance.id}")
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# except Exception as e:
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# instance.is_resume_parsed = False
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# instance.save(update_fields=['is_resume_parsed'])
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# logger.error(f"Failed to score resume for candidate:{instance.pk} {e}")
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def handle_reume_parsing_and_scoring(pk):
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"""
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Optimized Django-Q task to parse a resume, score the candidate against a job,
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and atomically save the results.
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"""
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# --- 1. Robust Object Retrieval (Prevents looping on DoesNotExist) ---
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try:
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instance = Candidate.objects.get(pk=pk)
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except Candidate.DoesNotExist:
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# Exit gracefully if the candidate was deleted after the task was queued
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logger.warning(f"Candidate matching query does not exist for pk={pk}. Exiting task.")
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print(f"Candidate matching query does not exist for pk={pk}. Exiting task.")
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return
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logger.info(f"Scoring resume for candidate {pk}")
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print(f"Scoring resume for candidate {pk}")
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# --- 2. I/O and Initial Data Check ---
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try:
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file_path = instance.resume.path
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if not os.path.exists(file_path):
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logger.warning(f"Resume file not found: {file_path}")
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print(f"Resume file not found: {file_path}")
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# Consider marking the task as unsuccessful but don't re-queue
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return
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resume_text = extract_text_from_pdf(file_path)
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job_detail = f"{instance.job.description} {instance.job.qualifications}"
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except Exception as e:
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logger.error(f"Error during initial data retrieval/parsing for candidate {instance.pk}: {e}")
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print(f"Error during initial data retrieval/parsing for candidate {instance.pk}: {e}")
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return
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# --- 3. Single, Combined LLM Prompt (Major Cost & Latency Optimization) ---
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prompt = f"""
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You are an expert AI system functioning as both a Resume Parser and a Technical Recruiter.
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Your task is to:
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1. **PARSE**: Extract all key-value information from the provided RESUME TEXT into a clean JSON structure under the key 'parsed_data'.
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2. **SCORE**: Analyze the parsed data against the JOB CRITERIA and generate a comprehensive score and analysis under the key 'scoring_data'.
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**JOB CRITERIA:**
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{job_detail}
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**RESUME TEXT:**
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{resume_text}
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**STRICT JSON OUTPUT INSTRUCTIONS:**
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Output a single, valid JSON object with ONLY the following two top-level keys:
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1. "parsed_data": {{
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"full_name": "Full name of the candidate",
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"current_title": "Most recent or current job title",
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"location": "City and state",
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"contact": "Phone number and email",
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"linkedin": "LinkedIn profile URL",
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"github": "GitHub or portfolio URL",
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"summary": "Brief professional profile or summary (1–2 sentences)",
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"education": [{{
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"institution": "Institution name",
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"degree": "Degree name",
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"year": "Year of graduation",
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"gpa": "GPA (if provided)",
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"relevant_courses": ["list", "of", "courses"]
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}}],
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"skills": {{
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"category_1": ["skill_a", "skill_b"],
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"uncategorized": ["tool_x"]
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}},
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"experience": [{{
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"company": "Company name",
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"job_title": "Job Title",
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"location": "Location",
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"start_date": "YYYY-MM",
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"end_date": "YYYY-MM or Present",
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"key_achievements": ["concise", "bullet", "points"]
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}}],
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"projects": [{{
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"name": "Project name",
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"year": "Year",
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"technologies_used": ["list", "of", "tech"],
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"brief_description": "description"
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}}]
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}}
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2. "scoring_data": {{
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"match_score": "Score 0-100",
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"strengths": "Brief summary of strengths",
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"weaknesses": "Brief summary of weaknesses",
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"years_of_experience": "Total years of experience (float, e.g., 6.5)",
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"criteria_checklist": {{ "Python": "Met", "AWS": "Not Mentioned"}},
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"category": "Most fitting professional field (e.g., Data Science)",
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"most_recent_job_title": "Candidate's most recent job title",
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"recommendation": "Detailed hiring recommendation narrative",
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"top_3_keywords": ["keyword1", "keyword2", "keyword3"],
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"job_fit_narrative": "Single, concise summary sentence",
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"language_fluency": ["language: fluency_level"],
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"screening_stage_rating": "Standardized rating (e.g., A - Highly Qualified)",
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"min_req_met_bool": "Boolean (true/false)",
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"soft_skills_score": "Score 0-100 for inferred non-technical skills",
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"experience_industry_match": "Score 0-100 for industry relevance"
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}}
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If a top-level key or its required fields are missing, set the field to null, an empty list, or an empty object as appropriate.
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Output only valid JSON—no markdown, no extra text.
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"""
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try:
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result = ai_handler(prompt)
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if result['status'] == 'error':
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logger.error(f"AI handler returned error for candidate {instance.pk}")
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print(f"AI handler returned error for candidate {instance.pk}")
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return
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# Ensure the result is parsed as a Python dict (if ai_handler returns a JSON string)
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data = result['data']
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if isinstance(data, str):
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data = json.loads(data)
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print(data)
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parsed_summary = data.get('parsed_data', {})
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scoring_result = data.get('scoring_data', {})
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except Exception as e:
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logger.error(f"AI handler failed for candidate {instance.pk}: {e}")
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print(f"AI handler failed for candidate {instance.pk}: {e}")
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return
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# --- 4. Atomic Database Update (Ensures data integrity) ---
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with transaction.atomic():
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# Map JSON keys to model fields with appropriate defaults
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update_map = {
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'match_score': ('match_score', 0),
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'years_of_experience': ('years_of_experience', 0.0),
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'soft_skills_score': ('soft_skills_score', 0),
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'experience_industry_match': ('experience_industry_match', 0),
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'min_req_met_bool': ('min_req_met_bool', False),
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'screening_stage_rating': ('screening_stage_rating', 'N/A'),
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'most_recent_job_title': ('most_recent_job_title', 'N/A'),
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'top_3_keywords': ('top_3_keywords', []),
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'strengths': ('strengths', ''),
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'weaknesses': ('weaknesses', ''),
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'job_fit_narrative': ('job_fit_narrative', ''),
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'recommendation': ('recommendation', ''),
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'criteria_checklist': ('criteria_checklist', {}),
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'language_fluency': ('language_fluency', []),
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'category': ('category', 'N/A'),
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}
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# Apply scoring results to the instance
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for model_field, (json_key, default_value) in update_map.items():
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instance.ai_analysis_data[model_field] = scoring_result.get(json_key, default_value)
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# instance.set_field(model_field, scoring_result.get(json_key, default_value))
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# Apply parsing results
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instance.parsed_summary = json.dumps(parsed_summary)
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instance.is_resume_parsed = True
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instance.save(update_fields=['ai_analysis_data','parsed_summary', 'is_resume_parsed'])
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logger.info(f"Successfully scored and saved analysis for candidate {instance.id}")
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print(f"Successfully scored and saved analysis for candidate {instance.id}")
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def create_interview_and_meeting(
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candidate_id,
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job_id,
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schedule_id,
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slot_date,
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slot_time,
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duration
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):
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"""
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Synchronous task for a single interview slot, dispatched by django-q.
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"""
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try:
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candidate = Candidate.objects.get(pk=candidate_id)
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job = JobPosting.objects.get(pk=job_id)
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schedule = InterviewSchedule.objects.get(pk=schedule_id)
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interview_datetime = datetime.combine(slot_date, slot_time)
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meeting_topic = f"Interview for {job.title} - {candidate.name}"
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# 1. External API Call (Slow)
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result = create_zoom_meeting(meeting_topic, interview_datetime, duration)
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if result["status"] == "success":
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# 2. Database Writes (Slow)
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zoom_meeting = ZoomMeeting.objects.create(
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topic=meeting_topic,
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start_time=interview_datetime,
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duration=duration,
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meeting_id=result["meeting_details"]["meeting_id"],
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join_url=result["meeting_details"]["join_url"],
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zoom_gateway_response=result["zoom_gateway_response"],
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host_email=result["meeting_details"]["host_email"],
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password=result["meeting_details"]["password"]
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)
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ScheduledInterview.objects.create(
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candidate=candidate,
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job=job,
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zoom_meeting=zoom_meeting,
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schedule=schedule,
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interview_date=slot_date,
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interview_time=slot_time
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)
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# Log success or use Django-Q result system for monitoring
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logger.info(f"Successfully scheduled interview for {candidate.name}")
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return True # Task succeeded
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else:
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# Handle Zoom API failure (e.g., log it or notify administrator)
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logger.error(f"Zoom API failed for {candidate.name}: {result['message']}")
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return False # Task failed
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except Exception as e:
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# Catch any unexpected errors during database lookups or processing
|
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logger.error(f"Critical error scheduling interview: {e}")
|
||
return False # Task failed
|
||
|
||
|
||
def handle_zoom_webhook_event(payload):
|
||
"""
|
||
Background task to process a Zoom webhook event and update the local ZoomMeeting status.
|
||
It handles: created, updated, started, ended, and deleted events.
|
||
"""
|
||
event_type = payload.get('event')
|
||
object_data = payload['payload']['object']
|
||
|
||
# Zoom often uses a long 'id' for the scheduled meeting and sometimes a 'uuid'.
|
||
# We rely on the unique 'id' that maps to your ZoomMeeting.meeting_id field.
|
||
meeting_id_zoom = str(object_data.get('id'))
|
||
print(meeting_id_zoom)
|
||
if not meeting_id_zoom:
|
||
logger.warning(f"Webhook received without a valid Meeting ID: {event_type}")
|
||
return False
|
||
|
||
try:
|
||
# Use filter().first() to avoid exceptions if the meeting doesn't exist yet,
|
||
# and to simplify the logic flow.
|
||
meeting_instance = ZoomMeeting.objects.filter(meeting_id=meeting_id_zoom).first()
|
||
print(meeting_instance)
|
||
# --- 1. Creation and Update Events ---
|
||
if event_type == 'meeting.updated':
|
||
if meeting_instance:
|
||
# Update key fields from the webhook payload
|
||
meeting_instance.topic = object_data.get('topic', meeting_instance.topic)
|
||
|
||
# Check for and update status and time details
|
||
# if event_type == 'meeting.created':
|
||
# meeting_instance.status = 'scheduled'
|
||
# elif event_type == 'meeting.updated':
|
||
# Only update time fields if they are in the payload
|
||
print(object_data)
|
||
meeting_instance.start_time = object_data.get('start_time', meeting_instance.start_time)
|
||
meeting_instance.duration = object_data.get('duration', meeting_instance.duration)
|
||
meeting_instance.timezone = object_data.get('timezone', meeting_instance.timezone)
|
||
|
||
meeting_instance.status = object_data.get('status', meeting_instance.status)
|
||
|
||
meeting_instance.save(update_fields=['topic', 'start_time', 'duration', 'timezone', 'status'])
|
||
|
||
# --- 2. Status Change Events (Start/End) ---
|
||
elif event_type == 'meeting.started':
|
||
if meeting_instance:
|
||
meeting_instance.status = 'started'
|
||
meeting_instance.save(update_fields=['status'])
|
||
|
||
elif event_type == 'meeting.ended':
|
||
if meeting_instance:
|
||
meeting_instance.status = 'ended'
|
||
meeting_instance.save(update_fields=['status'])
|
||
|
||
# --- 3. Deletion Event (User Action) ---
|
||
elif event_type == 'meeting.deleted':
|
||
if meeting_instance:
|
||
try:
|
||
meeting_instance.status = 'cancelled'
|
||
meeting_instance.save(update_fields=['status'])
|
||
except Exception as e:
|
||
logger.error(f"Failed to mark Zoom meeting as cancelled: {e}")
|
||
|
||
return True
|
||
|
||
except Exception as e:
|
||
logger.error(f"Failed to process Zoom webhook for {event_type} (ID: {meeting_id_zoom}): {e}", exc_info=True)
|
||
return False
|
||
|
||
def linkedin_post_task(job_slug, access_token):
|
||
# for linked post background tasks
|
||
|
||
job=get_object_or_404(JobPosting,slug=job_slug)
|
||
|
||
try:
|
||
service=LinkedInService()
|
||
service.access_token=access_token
|
||
# long running task
|
||
result=service.create_job_post(job)
|
||
|
||
#update the jobposting object with the final result
|
||
if result['success']:
|
||
job.posted_to_linkedin=True
|
||
job.linkedin_post_id=result['post_id']
|
||
job.linkedin_post_url=result['post_url']
|
||
job.linkedin_post_status='SUCCESSS'
|
||
job.linkedin_posted_at=timezone.now()
|
||
else:
|
||
error_msg=result.get('error',"Unknown API error")
|
||
job.linkedin_post_status = 'FAILED'
|
||
logger.error(f"LinkedIn post failed for job {job_slug}: {error_msg}")
|
||
job.save()
|
||
return result['success']
|
||
except Exception as e:
|
||
logger.error(f"Critical error in LinkedIn task for job {job_slug}: {e}", exc_info=True)
|
||
# Update job status with the critical error
|
||
job.linkedin_post_status = f"CRITICAL_ERROR: {str(e)}"
|
||
job.save()
|
||
return False
|
||
|
||
|
||
|
||
|