822 lines
34 KiB
Python
822 lines
34 KiB
Python
import re
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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 Application
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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 InterviewSchedule,ScheduledInterview,ZoomMeetingDetails
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# Add python-docx import for Word document processing
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try:
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from docx import Document
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DOCX_AVAILABLE = True
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except ImportError:
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DOCX_AVAILABLE = False
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logger = logging.getLogger(__name__)
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logger.warning("python-docx not available. Word document processing will be disabled.")
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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 = 'x-ai/grok-code-fast-1'
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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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# from google import genai
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# client = genai.Client(api_key="AIzaSyDkwYmvRe5ieTjQi1ClSzD5z5roTwaFsmY")
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# def google_ai(text):
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# response = client.models.generate_content(
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# model="gemini-2.5-flash", contents=text
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# )
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# return response
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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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"""Extract text from PDF files"""
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print("PDF 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 extract_text_from_word(file_path):
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"""Extract text from Word documents (.docx)"""
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if not DOCX_AVAILABLE:
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raise ImportError("python-docx is not installed. Please install it with: pip install python-docx")
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print("Word text extraction")
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text = ""
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try:
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doc = Document(file_path)
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# Extract text from paragraphs
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for paragraph in doc.paragraphs:
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text += paragraph.text + "\n"
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# Extract text from tables
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for table in doc.tables:
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for row in table.rows:
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for cell in row.cells:
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text += cell.text + "\t"
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text += "\n"
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# Extract text from headers and footers
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for section in doc.sections:
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# Header
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if section.header:
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for paragraph in section.header.paragraphs:
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text += "[HEADER] " + paragraph.text + "\n"
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# Footer
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if section.footer:
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for paragraph in section.footer.paragraphs:
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text += "[FOOTER] " + paragraph.text + "\n"
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except Exception as e:
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logger.error(f"Word extraction failed: {e}")
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raise
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return text.strip()
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def extract_text_from_document(file_path):
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"""Extract text from documents based on file type"""
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if not os.path.exists(file_path):
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raise FileNotFoundError(f"File not found: {file_path}")
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file_ext = os.path.splitext(file_path)[1].lower()
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if file_ext == '.pdf':
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return extract_text_from_pdf(file_path)
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elif file_ext == '.docx':
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return extract_text_from_word(file_path)
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else:
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raise ValueError(f"Unsupported file type: {file_ext}. Only .pdf and .docx files are supported.")
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def format_job_description(pk):
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job_posting = JobPosting.objects.get(pk=pk)
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print(job_posting)
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prompt = f"""
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You are a dual-purpose AI assistant specializing in content formatting and social media copywriting for job announcements.
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**JOB POSTING DATA (Raw Input):**
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---
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**JOB DESCRIPTION:**
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{job_posting.description}
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**QUALIFICATIONS:**
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{job_posting.qualifications}
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**BENEFITS:**
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{job_posting.benefits}
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**APPLICATION INSTRUCTIONS:**
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{job_posting.application_instructions}
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**APPLICATION DEADLINE:**
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{job_posting.application_deadline}
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**HASHTAGS: for search and reach:**
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{job_posting.hash_tags}
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**APPLICATION URL: for career page only if it is provided**
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{job_posting.application_url}
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---
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**TASK 1: HTML Formatting (Two Blocks)**
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1. **Format the Job Description:** Organize and format the raw JOB DESCRIPTION and BENEFITS data into clear, readable sections using `<h2>` headings and `<ul>`/`<li>` bullet points. Encapsulate the entire formatted block within a single `<div>`.
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2. **Format the Qualifications:** Organize and format the raw QUALIFICATIONS data into clear, readable sections using `<h2>` headings and `<ul>`/`<li>` bullet points. Encapsulate the entire formatted block within a single `<div>`.
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3. **Format the Benefits:** Organize and format the raw Requirements data into clear, readable sections using `<h2>` headings and `<ul>`/`<li>` bullet points. Encapsulate the entire formatted block within a single `<div>`.
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4. **Application Instructions:** Organize and format the raw Requirements data into clear, readable sections using `<h2>` headings and `<ul>`/`<li>` bullet points. Encapsulate the entire formatted block within a single `<div>`.
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**TASK 2: LinkedIn Post Creation**
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1. **Write the Post:** Create an engaging, professional, and concise LinkedIn post (maximum 1300 characters) summarizing the opportunity.
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2. **Encourage Action:** The post must have a strong call-to-action (CTA) encouraging applications.
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3. **Use Hashtags:** Integrate relevant industry, role, and company hashtags (including any provided in the raw input) naturally at the end of the post.
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**STRICT JSON OUTPUT INSTRUCTIONS:**
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Output a **single, valid JSON object** with **ONLY** the following three top-level key-value pairs.
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* The values for `html_job_description` and `html_qualifications` MUST be the complete, formatted HTML strings (including all tags).
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* The value for `linkedin_post` MUST be the complete, final LinkedIn post as a single string not greater than 3000 characters.
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**Output Keys:**
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1. `html_job_description`
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2. `html_qualifications`
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3. 'html_benefits'
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4. 'html_application_instructions'
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5. `linkedin_post_data`
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**Do not include any other text, explanation, or markdown outside of the final JSON object.**
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"""
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result = ai_handler(prompt)
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print(f"REsults: {result}")
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if result['status'] == 'error':
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logger.error(f"AI handler returned error for candidate {job_posting.pk}")
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print(f"AI handler returned error for candidate {job_posting.pk}")
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return
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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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job_posting.description = data.get('html_job_description')
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job_posting.qualifications = data.get('html_qualifications')
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job_posting.benefits=data.get('html_benefits')
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job_posting.application_instructions=data.get('html_application_instruction')
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job_posting.linkedin_post_formated_data=data.get('linkedin_post_data')
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job_posting.save(update_fields=['description', 'qualifications','linkedin_post_formated_data'])
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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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print(res)
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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 safe_cast_to_float(value, default=0.0):
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"""Safely converts a value (int, float, or string) to a float."""
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if isinstance(value, (int, float)):
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return float(value)
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if isinstance(value, str):
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# Remove non-numeric characters except the decimal point
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cleaned_value = re.sub(r'[^\d.]', '', value)
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try:
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# Ensure we handle empty strings after cleaning
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return float(cleaned_value) if cleaned_value else default
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except ValueError:
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return default
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return default
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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 = Application.objects.get(pk=pk)
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except Application.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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# Use the new unified document parser
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resume_text = extract_text_from_document(file_path)
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job_detail = f"{instance.job.description} {instance.job.qualifications}"
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||
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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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print(resume_text)
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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 'resume_data', preserving the original text and it's formatting and dont add any extra text.
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2. **SCORE**: Analyze the parsed data against the JOB CRITERIA and generate a comprehensive score and analysis under the key 'analysis_data'.
|
||
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**JOB CRITERIA:**
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{job_detail}
|
||
|
||
**RESUME TEXT:**
|
||
{resume_text}
|
||
|
||
**STRICT JSON OUTPUT INSTRUCTIONS:**
|
||
Output a single, valid JSON object with ONLY the following two top-level keys:
|
||
|
||
|
||
1. "resume_data": {{
|
||
"full_name": "Full name of the candidate",
|
||
"current_title": "Most recent or current job title",
|
||
"location": "City and state",
|
||
"contact": "Phone number and email",
|
||
"linkedin": "LinkedIn profile URL",
|
||
"github": "GitHub or portfolio URL",
|
||
"summary": "Brief professional profile or summary (1–2 sentences)",
|
||
"education": [{{
|
||
"institution": "Institution name",
|
||
"degree": "Degree name",
|
||
"year": "Year of graduation" (if provided) or '',
|
||
"gpa": "GPA (if provided)",
|
||
"relevant_courses": ["list", "of", "courses"](if provided) or []
|
||
}}],
|
||
"skills": {{
|
||
"category_1": ["skill_a", "skill_b"],
|
||
"uncategorized": ["tool_x"]
|
||
}},
|
||
"experience": [{{
|
||
"company": "Company name",
|
||
"job_title": "Job Title",
|
||
"location": "Location",
|
||
"start_date": "YYYY-MM",
|
||
"end_date": "YYYY-MM or Present",
|
||
"key_achievements": ["concise bullet points"] (if provided) or []
|
||
}}],
|
||
"projects": [{{
|
||
"name": "Project name",
|
||
"year": "Year",
|
||
"technologies_used": ["list", "of", "tech"] (if provided) or [],
|
||
"brief_description": "description"
|
||
}}]
|
||
}}
|
||
|
||
2. "analysis_data": {{
|
||
"match_score": "Integer Score 0-100",
|
||
"strengths": "Brief summary of strengths",
|
||
"weaknesses": "Brief summary of weaknesses",
|
||
"years_of_experience": "Total years of experience (float, e.g., 6.5)",
|
||
"criteria_checklist": List of job requirements if any {{ "Python": "Met", "AWS": "Not Met"}} only output the criteria_checklist in one of ('Met','Not Met') don't output any extra text,
|
||
"category": "Most fitting professional field (e.g., Data Science), only output the category name and no other text example ('Software Development', 'correct') , ('Software Development and devops','wrong') ('Software Development / Backend Development','wrong')",
|
||
"most_recent_job_title": "Candidate's most recent job title",
|
||
"recommendation": "Detailed hiring recommendation narrative",
|
||
"top_3_keywords": ["keyword1", "keyword2", "keyword3"],
|
||
"job_fit_narrative": "Single, concise summary sentence",
|
||
"language_fluency": ["language: fluency_level"],
|
||
"screening_stage_rating": "Standardized rating (Highly Qualified, Qualified , Partially Qualified, Not Qualified)",
|
||
"min_req_met_bool": "Boolean (true/false)",
|
||
"soft_skills_score": "Integer Score 0-100 for inferred non-technical skills",
|
||
"experience_industry_match": "Integer Score 0-100 for industry relevance",
|
||
"seniority_level_match": "Integer Score 0-100 for alignment with JD's seniority level",
|
||
"red_flags": ["List of any potential concerns (if any): e.g., 'Employment gap 1 year', 'Frequent job hopping', 'Missing required certification'"],
|
||
"employment_stability_score": "Integer Score 0-100 (Higher is more stable/longer tenure) (if possible)",
|
||
"transferable_skills_narrative": "A brief sentence describing the relevance of non-core experience (if applicable).",
|
||
"cultural_fit_keywords": ["A list of 3-5 keywords extracted from the resume (if possible) (e.g., 'team-player', 'mentored', 'cross-functional')"]
|
||
}}
|
||
|
||
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.
|
||
|
||
Output only valid JSON—no markdown, no extra text.
|
||
"""
|
||
|
||
try:
|
||
result = ai_handler(prompt)
|
||
if result['status'] == 'error':
|
||
logger.error(f"AI handler returned error for candidate {instance.pk}")
|
||
print(f"AI handler returned error for candidate {instance.pk}")
|
||
return
|
||
# Ensure the result is parsed as a Python dict (if ai_handler returns a JSON string)
|
||
data = result['data']
|
||
if isinstance(data, str):
|
||
data = json.loads(data)
|
||
print(data)
|
||
|
||
# parsed_summary = data.get('parsed_data', {})
|
||
# scoring_result = data.get('scoring_data', {})
|
||
|
||
except Exception as e:
|
||
logger.error(f"AI handler failed for candidate {instance.pk}: {e}")
|
||
print(f"AI handler failed for candidate {instance.pk}: {e}")
|
||
return
|
||
|
||
# --- 4. Atomic Database Update (Ensures data integrity) ---
|
||
with transaction.atomic():
|
||
|
||
# Map JSON keys to model fields with appropriate defaults
|
||
# update_map = {
|
||
# 'match_score': ('match_score', 0),
|
||
# 'years_of_experience': ('years_of_experience', 0.0),
|
||
# 'soft_skills_score': ('soft_skills_score', 0),
|
||
# 'experience_industry_match': ('experience_industry_match', 0),
|
||
|
||
# 'min_req_met_bool': ('min_req_met_bool', False),
|
||
# 'screening_stage_rating': ('screening_stage_rating', 'N/A'),
|
||
# 'most_recent_job_title': ('most_recent_job_title', 'N/A'),
|
||
# 'top_3_keywords': ('top_3_keywords', []),
|
||
|
||
# 'strengths': ('strengths', ''),
|
||
# 'weaknesses': ('weaknesses', ''),
|
||
# 'job_fit_narrative': ('job_fit_narrative', ''),
|
||
# 'recommendation': ('recommendation', ''),
|
||
|
||
# 'criteria_checklist': ('criteria_checklist', {}),
|
||
# 'language_fluency': ('language_fluency', []),
|
||
# 'category': ('category', 'N/A'),
|
||
# }
|
||
|
||
# Apply scoring results to the instance
|
||
# for model_field, (json_key, default_value) in update_map.items():
|
||
# instance.ai_analysis_data[model_field] = scoring_result.get(json_key, default_value)
|
||
# instance.set_field(model_field, scoring_result.get(json_key, default_value))
|
||
# instance.set_field("match_score" , int(safe_cast_to_float(scoring_result.get('match_score', 0), default=0)))
|
||
# instance.set_field("years_of_experience" , safe_cast_to_float(scoring_result.get('years_of_experience', 0.0)))
|
||
# instance.set_field("soft_skills_score" , int(safe_cast_to_float(scoring_result.get('soft_skills_score', 0), default=0)))
|
||
# instance.set_field("experience_industry_match" , int(safe_cast_to_float(scoring_result.get('experience_industry_match', 0), default=0)))
|
||
|
||
# # Other Model Fields
|
||
# instance.set_field("min_req_met_bool" , scoring_result.get('min_req_met_bool', False))
|
||
# instance.set_field("screening_stage_rating" , scoring_result.get('screening_stage_rating', 'N/A'))
|
||
# instance.set_field("category" , scoring_result.get('category', 'N/A'))
|
||
# instance.set_field("most_recent_job_title" , scoring_result.get('most_recent_job_title', 'N/A'))
|
||
# instance.set_field("top_3_keywords" , scoring_result.get('top_3_keywords', []))
|
||
# instance.set_field("strengths" , scoring_result.get('strengths', ''))
|
||
# instance.set_field("weaknesses" , scoring_result.get('weaknesses', ''))
|
||
# instance.set_field("job_fit_narrative" , scoring_result.get('job_fit_narrative', ''))
|
||
# instance.set_field("recommendation" , scoring_result.get('recommendation', ''))
|
||
# instance.set_field("criteria_checklist" , scoring_result.get('criteria_checklist', {}))
|
||
# instance.set_field("language_fluency" , scoring_result.get('language_fluency', []))
|
||
|
||
|
||
# 2. Update the Full JSON Field (ai_analysis_data)
|
||
if instance.ai_analysis_data is None:
|
||
instance.ai_analysis_data = {}
|
||
|
||
# Save both structured outputs into the single JSONField for completeness
|
||
instance.ai_analysis_data = data
|
||
# instance.ai_analysis_data['parsed_data'] = parsed_summary
|
||
# instance.ai_analysis_data['scoring_data'] = scoring_result
|
||
|
||
# Apply parsing results
|
||
# instance.parsed_summary = json.dumps(parsed_summary)
|
||
instance.is_resume_parsed = True
|
||
|
||
instance.save(update_fields=['ai_analysis_data', 'is_resume_parsed'])
|
||
|
||
logger.info(f"Successfully scored and saved analysis for candidate {instance.id}")
|
||
print(f"Successfully scored and saved analysis for candidate {instance.id}")
|
||
|
||
|
||
from django.utils import timezone
|
||
def create_interview_and_meeting(
|
||
candidate_id,
|
||
job_id,
|
||
schedule_id,
|
||
slot_date,
|
||
slot_time,
|
||
duration
|
||
):
|
||
"""
|
||
Synchronous task for a single interview slot, dispatched by django-q.
|
||
"""
|
||
try:
|
||
candidate = Application.objects.get(pk=candidate_id)
|
||
job = JobPosting.objects.get(pk=job_id)
|
||
schedule = InterviewSchedule.objects.get(pk=schedule_id)
|
||
|
||
interview_datetime = timezone.make_aware(datetime.combine(slot_date, slot_time))
|
||
meeting_topic = f"Interview for {job.title} - {candidate.name}"
|
||
|
||
# 1. External API Call (Slow)
|
||
|
||
result = create_zoom_meeting(meeting_topic, interview_datetime, duration)
|
||
|
||
if result["status"] == "success":
|
||
# 2. Database Writes (Slow)
|
||
zoom_meeting = ZoomMeetingDetails.objects.create(
|
||
topic=meeting_topic,
|
||
start_time=interview_datetime,
|
||
duration=duration,
|
||
meeting_id=result["meeting_details"]["meeting_id"],
|
||
details_url=result["meeting_details"]["join_url"],
|
||
zoom_gateway_response=result["zoom_gateway_response"],
|
||
host_email=result["meeting_details"]["host_email"],
|
||
password=result["meeting_details"]["password"],
|
||
location_type="Remote"
|
||
)
|
||
ScheduledInterview.objects.create(
|
||
application=candidate,
|
||
job=job,
|
||
interview_location=zoom_meeting,
|
||
schedule=schedule,
|
||
interview_date=slot_date,
|
||
interview_time=slot_time
|
||
)
|
||
|
||
# Log success or use Django-Q result system for monitoring
|
||
logger.info(f"Successfully scheduled interview for {Application.name}")
|
||
return True # Task succeeded
|
||
else:
|
||
# Handle Zoom API failure (e.g., log it or notify administrator)
|
||
logger.error(f"Zoom API failed for {Application.name}: {result['message']}")
|
||
return False # Task failed
|
||
|
||
except Exception as e:
|
||
# Catch any unexpected errors during database lookups or processing
|
||
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 = ZoomMeetingDetails.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
|
||
|
||
|
||
def form_close(job_id):
|
||
job = get_object_or_404(JobPosting, pk=job_id)
|
||
job.is_active = False
|
||
job.template_form.is_active = False
|
||
job.save()
|
||
|
||
|
||
def sync_hired_candidates_task(job_slug):
|
||
"""
|
||
Django-Q background task to sync hired candidates to all configured sources.
|
||
|
||
Args:
|
||
job_slug (str): The slug of the job posting
|
||
|
||
Returns:
|
||
dict: Sync results with status and details
|
||
"""
|
||
from .candidate_sync_service import CandidateSyncService
|
||
from .models import JobPosting, IntegrationLog
|
||
|
||
logger.info(f"Starting background sync task for job: {job_slug}")
|
||
|
||
try:
|
||
# Get the job posting
|
||
job = JobPosting.objects.get(slug=job_slug)
|
||
|
||
# Initialize sync service
|
||
sync_service = CandidateSyncService()
|
||
print(sync_service)
|
||
|
||
# Perform the sync operation
|
||
results = sync_service.sync_hired_candidates_to_all_sources(job)
|
||
print(results)
|
||
# Log the sync operation
|
||
# IntegrationLog.objects.create(
|
||
# source=None, # This is a multi-source sync operation
|
||
# action=IntegrationLog.ActionChoices.SYNC,
|
||
# endpoint="multi_source_sync",
|
||
# method="BACKGROUND_TASK",
|
||
# request_data={"job_slug": job_slug, "candidate_count": job.accepted_candidates.count()},
|
||
# response_data=results,
|
||
# status_code="SUCCESS" if results.get('summary', {}).get('failed', 0) == 0 else "PARTIAL",
|
||
# ip_address="127.0.0.1", # Background task
|
||
# user_agent="Django-Q Background Task",
|
||
# processing_time=results.get('summary', {}).get('total_duration', 0)
|
||
# )
|
||
|
||
logger.info(f"Background sync completed for job {job_slug}: {results}")
|
||
return results
|
||
|
||
except JobPosting.DoesNotExist:
|
||
error_msg = f"Job posting not found: {job_slug}"
|
||
logger.error(error_msg)
|
||
|
||
# Log the error
|
||
IntegrationLog.objects.create(
|
||
source=None,
|
||
action=IntegrationLog.ActionChoices.ERROR,
|
||
endpoint="multi_source_sync",
|
||
method="BACKGROUND_TASK",
|
||
request_data={"job_slug": job_slug},
|
||
error_message=error_msg,
|
||
status_code="ERROR",
|
||
ip_address="127.0.0.1",
|
||
user_agent="Django-Q Background Task"
|
||
)
|
||
|
||
return {"status": "error", "message": error_msg}
|
||
|
||
except Exception as e:
|
||
error_msg = f"Unexpected error during sync: {str(e)}"
|
||
logger.error(error_msg, exc_info=True)
|
||
|
||
# Log the error
|
||
IntegrationLog.objects.create(
|
||
source=None,
|
||
action=IntegrationLog.ActionChoices.ERROR,
|
||
endpoint="multi_source_sync",
|
||
method="BACKGROUND_TASK",
|
||
request_data={"job_slug": job_slug},
|
||
error_message=error_msg,
|
||
status_code="ERROR",
|
||
ip_address="127.0.0.1",
|
||
user_agent="Django-Q Background Task"
|
||
)
|
||
|
||
return {"status": "error", "message": error_msg}
|
||
|
||
|
||
def sync_candidate_to_source_task(candidate_id, source_id):
|
||
"""
|
||
Django-Q background task to sync a single candidate to a specific source.
|
||
|
||
Args:
|
||
candidate_id (int): The ID of the candidate
|
||
source_id (int): The ID of the source
|
||
|
||
Returns:
|
||
dict: Sync result for this specific candidate-source pair
|
||
"""
|
||
from .candidate_sync_service import CandidateSyncService
|
||
from .models import Application, Source, IntegrationLog
|
||
|
||
logger.info(f"Starting sync task for candidate {candidate_id} to source {source_id}")
|
||
|
||
try:
|
||
# Get the candidate and source
|
||
application = Application.objects.get(pk=candidate_id)
|
||
source = Source.objects.get(pk=source_id)
|
||
|
||
# Initialize sync service
|
||
sync_service = CandidateSyncService()
|
||
|
||
# Perform the sync operation
|
||
result = sync_service.sync_candidate_to_source(application, source)
|
||
|
||
# Log the operation
|
||
IntegrationLog.objects.create(
|
||
source=source,
|
||
action=IntegrationLog.ActionChoices.SYNC,
|
||
endpoint=source.sync_endpoint or "unknown",
|
||
method=source.sync_method or "POST",
|
||
request_data={"candidate_id": candidate_id, "application_name": application.name},
|
||
response_data=result,
|
||
status_code="SUCCESS" if result.get('success') else "ERROR",
|
||
error_message=result.get('error') if not result.get('success') else None,
|
||
ip_address="127.0.0.1",
|
||
user_agent="Django-Q Background Task",
|
||
processing_time=result.get('duration', 0)
|
||
)
|
||
|
||
logger.info(f"Sync completed for candidate {candidate_id} to source {source_id}: {result}")
|
||
return result
|
||
|
||
except Application.DoesNotExist:
|
||
error_msg = f"Application not found: {candidate_id}"
|
||
logger.error(error_msg)
|
||
return {"success": False, "error": error_msg}
|
||
|
||
except Source.DoesNotExist:
|
||
error_msg = f"Source not found: {source_id}"
|
||
logger.error(error_msg)
|
||
return {"success": False, "error": error_msg}
|
||
|
||
except Exception as e:
|
||
error_msg = f"Unexpected error during sync: {str(e)}"
|
||
logger.error(error_msg, exc_info=True)
|
||
return {"success": False, "error": error_msg}
|
||
|
||
|
||
|
||
from django.conf import settings
|
||
from django.core.mail import EmailMultiAlternatives
|
||
from django.utils.html import strip_tags
|
||
|
||
def _task_send_individual_email(subject, body_message, recipient, attachments):
|
||
"""Internal helper to create and send a single email."""
|
||
|
||
from_email = getattr(settings, 'DEFAULT_FROM_EMAIL', 'noreply@kaauh.edu.sa')
|
||
is_html = '<' in body_message and '>' in body_message
|
||
|
||
if is_html:
|
||
plain_message = strip_tags(body_message)
|
||
email_obj = EmailMultiAlternatives(subject=subject, body=plain_message, from_email=from_email, to=[recipient])
|
||
email_obj.attach_alternative(body_message, "text/html")
|
||
else:
|
||
email_obj = EmailMultiAlternatives(subject=subject, body=body_message, from_email=from_email, to=[recipient])
|
||
|
||
if attachments:
|
||
for attachment in attachments:
|
||
if isinstance(attachment, tuple) and len(attachment) == 3:
|
||
filename, content, content_type = attachment
|
||
email_obj.attach(filename, content, content_type)
|
||
|
||
try:
|
||
email_obj.send(fail_silently=False)
|
||
return True
|
||
except Exception as e:
|
||
logger.error(f"Task failed to send email to {recipient}: {str(e)}", exc_info=True)
|
||
return False
|
||
|
||
|
||
def send_bulk_email_task(subject, message, recipient_list, attachments=None, hook='recruitment.tasks.email_success_hook'):
|
||
"""
|
||
Django-Q background task to send pre-formatted email to a list of recipients.
|
||
Receives arguments directly from the async_task call.
|
||
"""
|
||
logger.info(f"Starting bulk email task for {len(recipient_list)} recipients")
|
||
successful_sends = 0
|
||
total_recipients = len(recipient_list)
|
||
|
||
if not recipient_list:
|
||
return {'success': False, 'error': 'No recipients provided to task.'}
|
||
|
||
# Since the async caller sends one task per recipient, total_recipients should be 1.
|
||
for recipient in recipient_list:
|
||
# The 'message' is the custom message specific to this recipient.
|
||
if _task_send_individual_email(subject, message, recipient, attachments):
|
||
successful_sends += 1
|
||
|
||
if successful_sends > 0:
|
||
logger.info(f"Bulk email task completed successfully. Sent to {successful_sends}/{total_recipients} recipients.")
|
||
return {
|
||
'success': True,
|
||
'recipients_count': successful_sends,
|
||
'message': f"Sent successfully to {successful_sends} recipient(s)."
|
||
}
|
||
else:
|
||
logger.error(f"Bulk email task failed: No emails were sent successfully.")
|
||
return {'success': False, 'error': "No emails were sent successfully in the background task."}
|
||
|
||
|
||
def email_success_hook(task):
|
||
"""
|
||
The success hook must accept the Task object as the first and only required positional argument.
|
||
"""
|
||
if task.success:
|
||
logger.info(f"Task ID {task.id} succeeded. Result: {task.result}")
|
||
else:
|
||
logger.error(f"Task ID {task.id} failed. Error: {task.result}")
|
||
|