Add CSB 311 dashboard (minimal changes)

[bhuelsman]

"Closing this PR as requested. Created new PR #69 with minimal changes that only add CSB functionality without modifying existing code structure."

• Added /csb-dashboard route (frontend page)
• Added /csb API endpoint (JSON data)
• Added csb.htm dashboard file
• Added csb_full_pipeline.py for data ingestion
• Added link to index page

How to Run

Run docker containers

1. Load CSB and JSON Data (one-time setup)

cd src/write_service/consumers
python3 csb_full_pipeline.py
python3 json_consumer.py

Output: ✓ Pipeline complete! Active records: 92

2. Start Backend

FROM PROJECT ROOT

python3 -m src.read_service.app 

Server runs on: http://localhost:5001

If there are issues, likely with docker, change app.py to run on 5003 and run app again and go to: http://localhost:5003

3. Access the Application

For Non-Technical Users:

• Main page: http://localhost:5001/
  • Landing page with links to all dashboards
• CSB Dashboard: http://localhost:5001/csb-dashboard
  • Interactive visualization of 311 service requests
  • Filter by ward, service type, search terms
  • View statistics and active requests

For Developers:

• CSB API: http://localhost:5001/csb
  • Returns JSON array of active service requests
• ARPA API: http://localhost:5001/api/arpa
  • Returns ARPA funding data
• Swagger Docs: http://localhost:5001/swagger
  • Complete API documentation

Dashboard Capabilities

• Real-time filtering: Search across service names, descriptions, and neighborhoods
• Ward filtering: View requests by specific ward (01-14)
• Service type filtering: Filter by service categories (sidewalk repair, tree inspection, etc.)
• Statistics: Live counts of total requests, service types, and wards
• Data attribution: Links to original St. Louis Open Data source

Data Pipeline Features

• Single command: One script handles download → process → store
• Automatic cleanup: Uses temp directories, no CSV files in codebase
• Incremental updates: Marks old data inactive, adds new data
• Error handling: Robust parsing of various date formats
• Batch processing: Efficient bulk inserts (1000 records at a time)