Job Opportunity Dashboard

Python · JSON data preparation · Browser-based reporting

A prototype for organising job opportunities, comparing them with keyword preferences and tracking application status.

The project combines a Python helper with a React dashboard delivered as a single HTML file. Job data is imported manually; the collection functions are scaffolding for future integrations.

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What the project demonstrates

• Structuring job records into a consistent JSON format.
• Adding manual records and checking existing URLs for duplicates.
• Calculating a transparent keyword-match score.
• Filtering opportunities by location, salary, source and status.
• Summarising filtered records and tracking application status in the dashboard.

The score is the percentage of supplied keywords found in the title or description. It is a rule-based match score, not a trained AI model or a probability of suitability.

Try the dashboard

1. Clone or download this repository.
2. Open job_dashboard.html in a browser.
3. Select Import Jobs and paste the contents of sample_jobs.json.
4. Enter comma-separated keywords and explore the filters.
5. Use the status controls to organise opportunities.

The dashboard loads React, React DOM and Babel from a public CDN, so it needs those resources to be available. It stores dashboard records in browser localStorage; the Python helper uses separate JSON files. There is no automatic synchronisation between them.

Use the Python helper

Python 3 and requests are required by the current source.

git clone https://github.com/HoangCuongMax/JobSearchMax.git
cd JobSearchMax
python3 -m venv .venv

Activate the environment with source .venv/bin/activate on macOS/Linux, or .venv\Scripts\Activate.ps1 in Windows PowerShell. Then:

python -m pip install requests
python job_crawler.py

The entry point creates the local data directory and runs the scaffold routine. It does not automatically fetch or populate live job listings.

For manual entry, use the helper from a Python session started in the repository folder:

from job_crawler import JobCrawler

crawler = JobCrawler()
crawler.add_manual_job({
    "title": "Data Analyst",
    "company": "Example organisation",
    "location": "Darwin, NT",
    "salary": "$80,000 - $100,000",
    "url": "https://example.com/jobs/data-analyst",
    "platform": "manual",
    "description": "Power BI, SQL and Python reporting"
})
print(crawler.export_for_dashboard(["Power BI", "SQL", "Python"]))

Paste the exported JSON into the dashboard to include the parsed salary fields and calculated scores.

Repository map

File	Purpose
job_crawler.py	Record creation, local JSON storage, simple salary parsing and keyword scoring
job_dashboard.html	Import, filtering, summaries and application tracking
sample_jobs.json	Example records for a demonstration
sources.json.example	Example source configuration
QUICKSTART.txt	Earlier setup notes; use this README for current scope and limitations

Current scope & limitations

• Seek and Indeed helpers print suggested search URLs; the custom-URL crawler is a placeholder.
• Scheduling the helper does not turn these placeholders into working ingestion connectors.
• Salary parsing is simple numeric extraction and does not normalise currencies, hourly rates or shorthand such as "100k".
• Keyword scores depend on the supplied terms and do not measure professional suitability.
• Data is stored separately in the browser and local files. Back up records before clearing browser storage.
• Import validation, persistence behaviour and a fully tested end-to-end workflow need further development.

Next development steps

Add schema validation, automated checks for imports and persistence, improved salary normalisation, and a documented permitted ingestion source. Measure the resulting workflow before claiming time savings or collection coverage.