feat(dashboard): add interactive complaints analysis dashboard

[devin-ai-integration]

feat(dashboard): add interactive Streamlit complaints analysis dashboard

Summary

This PR adds an interactive Streamlit dashboard for analyzing customer complaints data from Supabase. The dashboard replaces the static HTML report with a live, filterable application featuring:

• Live Supabase connection with 5-minute data caching to reduce API calls
• Filter dropdowns for category, severity, status, customer segment, and date range
• Dynamic KPI cards that update based on applied filters (total complaints, avg resolution, repeat complainers, compensation stats)
• Interactive charts using Plotly:
  • Time series with daily/weekly/monthly toggle
  • Category breakdown bar chart
  • Severity distribution donut chart
  • Resolution time distribution
• Outlier highlights section (>60 days resolution, >£300 compensation, same-day resolution)
• Repeat complainers analysis (customers with 3+ complaints)

Review & Testing Checklist for Human

• Verify Supabase connection: The app hasn't been tested with live Supabase credentials. Set SUPABASE_URL and SUPABASE_KEY environment variables and run streamlit run dashboard/app.py to confirm data loads correctly
• Validate data schema: Confirm the santander_customer_complaints table has all expected columns (complaint_id, customer_id, category, severity, status, complaint_date, resolution_date, resolution_days, compensation_amount, customer_segment)
• Test compensation parsing: The code assumes compensation_amount is formatted as "£X.XX" (line 113). Verify this matches actual data format - if values are already numeric or use different formatting, this will fail
• Test filter combinations: Apply various filter combinations and verify statistics/charts update correctly
• Note: python-dotenv is in requirements.txt but not actually imported in app.py - environment variables must be set externally (e.g., via shell export or Streamlit secrets)

Recommended test plan:

1. Install dependencies: pip install -r dashboard/requirements.txt
2. Set environment variables: export SUPABASE_URL=... SUPABASE_KEY=...
3. Run: streamlit run dashboard/app.py
4. Test each filter individually, then in combination
5. Toggle time series aggregation (Daily/Weekly/Monthly)
6. Verify outlier counts match expectations

Notes

• Link to Devin run: https://app.devin.ai/sessions/83d8ee010f144b0b870952fd52e5f7be
• Requested by: Joao Esteves (joao.esteves@cognition.ai) / @joao-cognition

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