π 2025-07-10 β Session: Refactored Streamlit App for Job Search Visualization
π 00:00β23:50
π·οΈ Labels: Streamlit, Python, Data Visualization, Job Search, Refactoring
π Project: Dev
β Priority: MEDIUM
Session Goal
The primary goal of this session was to enhance the functionality and user experience of a Streamlit application designed for job search visualization. This involved refactoring the application structure, improving error handling, and integrating new features for data display and interactivity.
Key Activities
- Streamlit Logging Best Practices: Reviewed methods to ensure visibility of
print()statements and subprocess logs in the terminal during Streamlit app execution. - Structured Output for Job Recommendations: Planned a structured approach for displaying job recommendations using tables and heatmaps.
- Streamlit App UX and Data Display: Implemented a structured approach for building a Streamlit app with features like file selection, data loading, and match summaries using JSONL files.
- Configuration Error Resolution: Addressed a common Streamlit configuration error by adjusting the script order.
- Refactoring with
render_results_page()Function: Introduced a function to enhance clarity and functionality, focusing on modular design. - Compact Table View for Job Match Results: Developed a compact table view for job matches using DataFrame format.
- Live Editing Jinja2 Template: Added a feature for live editing of Jinja2 templates within the Streamlit app.
- JSON Editor Integration: Explored and implemented JSON editor alternatives for Streamlit, including schema-guided editable tabs.
Achievements
- Successfully refactored the Streamlit application to improve maintainability and user experience.
- Enhanced the applicationβs data visualization capabilities with new structured views and interactivity.
- Resolved configuration errors and improved the appβs startup sequence.
- Integrated a live editing feature for Jinja2 templates, enhancing flexibility for users.
Pending Tasks
- Further testing and validation of the JSON editor integration.
- Optimization of the job recommendation display logic to ensure clarity and usability.
- Continued refinement of error handling and logging practices.