- promopt template for searching & scrape
- real and fake job posting list with links in a table format
- explain and build new features like cv parsing & look for exact job macthes
- applied button so applied jobs will not be searched again
- Job Search and Query Generation Generate Search Queries: Uses keywords (e.g., from a CV or user input) to generate concise job search queries via the generate_search_queries function in search_agent.py. Propose Additional Queries: Suggests more queries based on existing ones using the propose_additional_queries function in crawler_agent.py. Search Jobs: Performs web searches for job postings using DuckDuckGo via the search_jobs function in search_agent.py.
- Job Portal Management Known Portals: Maintains a list of known job portals in known_portals.json. Portal Classification: Determines if a domain is a job portal using the is_job_portal function in portal_agent.py. Update Portals: Adds new job portals to the list after validation via the update_portals function in portal_agent.py.
- Job Post Scraping Scrape Job Details: Extracts job titles and URLs from job postings using the scrape_job_post function in scraper_agent.py.
- CV Parsing Extract Text: Reads text from uploaded CVs in PDF, DOCX, or CSV formats using functions in resume_parser.py. Extract Keywords: Extracts key phrases from the CV text for query generation.
- Training Links Management Save Training Links: Stores job links for deduplication and training purposes in training_links.json via save_training_links in training_agent.py. Load Training Links: Loads previously saved job links for deduplication.
- Chat Interface Chat History: Maintains a chat history in chat_history.json to display past interactions. Free-form Chat: Allows users to input job search queries directly. Manual Search Panel: Provides a form for users to specify job roles, experience, and location for manual searches.
- Bookmarks Save Bookmarks: Allows users to bookmark job postings, stored in bookmarks.json. Manage Bookmarks: Displays and clears bookmarks via the sidebar.
- Streamlit-based UI The application is built using Streamlit, providing an interactive web interface for users to upload CVs, view job search results, and interact with the assistant.
- Dockerized Deployment The application is containerized using Docker, with a Dockerfile and docker-compose.yml for easy deployment.
- Environment Configuration Uses environment variables (e.g., OLLAMA_HOST) for API endpoints and configurations. This codebase is designed to automate job searches, enhance user experience with AI-driven query generation, and provide a streamlined interface for job seekers.