Dashboard: Stadium Night design system, 5-page Streamlit app, warehouse exporter
- src/export/warehouse.py: reads scattered pipeline artifacts → 6 Parquet files (players, fixtures, predictions, lineups, votes, model_metrics) - dashboard/warehouse.py: read-only cached Parquet loader - .streamlit/config.toml: dark theme base, server config - dashboard/viz/template.py: Plotly 'fantabeto_dark' template (single source of truth) - Semantic palette: pitch_green #00D084, gold #FFC94D, red #FF4D5E, sky #38BDF8 - Space Grotesk headers, Inter body, tabular numerals - All 10 chart colors banned from default palette - dashboard/viz/components.py: KPI cards, role chips, section headers, CSS injection - dashboard/viz/charts.py: 10 pure chart functions (df → Figure) - percentile radar, fixture heatmap, regression comparison, bonus donut - card risk gauge, budget waterfall, value scatter, error violins - dashboard/viz/pitch.py: SVG pitch component — dark turf gradient, player badges sized by FV, gold captain ring, bench strip, formation label - 5 pages: - 01_matchday: KPI sparklines, fixture heatmap, start/sit grid, bump chart - 02_players: search, radar, regression, bonus/malus, card risk, news feed - 03_auction: budget slider, waterfall, value scatter, grid auction heatmap - 04_lineup: SVG pitch, what-if toggles, opponent mirror, MCTS captain - 05_lab: error violins, feature importance, calibration curve, backtest - dashboard/app.py: multi-page Streamlit entry with sidebar navigation - dashboard/tests/test_dashboard.py: 18 unit tests (warehouse, template, charts, pitch) - DASHBOARD.md: full architecture docs, design system reference - 49 total tests passing (31 existing + 18 dashboard)
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ramseshk
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# Fantabeto Dashboard
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**Project Al-Cihred** — Production-grade Streamlit dashboard for the 2026/27 Fantacalcio season.
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```bash
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streamlit run dashboard/app.py
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```
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---
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## Architecture
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```
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data/warehouse/ ← Versioned Parquet (read by dashboard)
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players.parquet 505 rows — roster + 25/26 stats + per-90 metrics
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fixtures.parquet 10 rows — matchday 1 home/away + formations
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predictions.parquet 505 rows — FV projections with std + starter prob
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lineups.parquet 466 rows — probable lineups with starter %
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votes.parquet 12,049 rows — historical per-matchday votes (25/26)
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model_metrics.parquet 1 row — RMSE, R², sample count
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dashboard/
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app.py ← Entry point: multi-page routing via sidebar
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warehouse.py ← Read-only Parquet loader (@st.cache_data)
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viz/
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template.py ← Plotly template "fantabeto_dark" + color palette
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charts.py ← Pure functions: df → Figure (heatmap, radar, etc.)
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pitch.py ← SVG pitch component with player badges
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components.py ← KPI cards, role chips, section headers, CSS
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pages/
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01_matchday.py ← Control Room: KPIs, fixture heatmap, start/sit
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02_players.py ← Intelligence: radar, regression, card risk, news
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03_auction.py ← War Room: waterfall, value scatter, grid heatmap
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04_lineup.py ← Optimizer: SVG pitch, what-if toggles, opponent
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05_lab.py ← Model Lab: error violins, calibration, backtest
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tests/
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test_dashboard.py 18 unit tests (warehouse, template, charts, pitch)
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src/export/warehouse.py ← Exporter: reads pipeline artifacts → Parquet
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.streamlit/config.toml ← Dark theme base, server config
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```
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---
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## Design System — Stadium Night
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| Property | Value |
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|---|---|
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| Background | `#0B0F17` |
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| Card bg | `#111827` at 70% opacity, 14px radius, blur 8px |
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| Border | `#1F2937` |
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| Pitch Green | `#00D084` — positive, bonus |
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| Gold | `#FFC94D` — captain, highlight |
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| Red | `#FF4D5E` — malus, risk |
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| Sky | `#38BDF8` — neutral data |
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| Violet | `#A78BFA` — uncertainty |
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| Type | Space Grotesk (headers), Inter (body), tabular numerals |
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| Charts | All use `fantabeto_dark` template. No default Plotly palette anywhere. |
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---
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## Pages
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### 1. Matchday Control Room
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- **KPI row**: projected points, players at risk (P(start) < 70%), matchday count, FV trend sparkline
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- **Fixture difficulty heatmap**: teams × gameweeks, colored by opponent xGA/FV strength
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- **Start/Sit grid**: top 20 players with role chips, H/A indicator, projected FV ± std, risk status (🟢 START / 🟡 DOUBT / 🔴 RISK)
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- **Bump chart**: projected rank trajectory across first 10 GWs
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### 2. Player Intelligence
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- **Search**: autocomplete on all 505 players, filtered by role/team
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- **Player header**: FV avg, games played, GW1 start %, role chip, FVM, QI
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- **Percentile radar**: 7 metrics normalized vs role average (goals, assists, xG, prog passes, tackles, interceptions, SoT%)
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- **Goals vs Expected**: bar chart with divergence annotation
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- **Bonus/malus donut**: goal bonus + assist bonus − card malus breakdown
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- **Card risk gauge**: yellow/red card per-game risk indicators
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- **News feed**: placeholder for RAG pipeline output (injuries, suspensions, tactical shifts)
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- **Historical votes**: last 10 matchday votes table
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### 3. Auction War Room
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- **Budget controls**: sliders for budget (300–700 cr) and role quotas
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- **KPI row**: players drafted, total spent, projected FV, avg price per player
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- **Budget waterfall**: Sankey-style allocation per role (GK/DEF/MID/FWD)
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- **Value scatter**: FV avg vs QI, bubble size = games played, top steals labeled, cost-per-FV isolines
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- **Grid auction heatmap**: top players × bid multipliers, colored by value surplus
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- **Recommended squad**: per-role player list with max bid, FV, and games played
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### 4. Lineup Optimizer
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- **What-if controls**: force IN/OUT a player, select formation (4-4-2, 4-3-3, 3-5-2, etc.)
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- **KPI row**: expected points (with captain), captain name, avg start %, win probability
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- **SVG pitch**: dark turf gradient, player badges sized by FV, gold captain ring, risk markers, bench strip, formation label
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- **Player list**: role-chipped players with FV and risk flags
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- **Opponent mirror**: per-duel edge arrows (🟢 advantage / 🔴 disadvantage / ⚪ neutral)
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### 5. Model Lab
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- **KPI row**: RMSE, R², training samples, feature count
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- **Error violins**: prediction error distribution by role
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- **Feature importance**: Pearson correlation with FV avg
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- **Calibration curve**: predicted vs observed uncertainty
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- **Backtest**: league average FV across 38 matchdays
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- **Architecture notes**: model overview, key insights, limitations
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---
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## Running
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```bash
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# First time: export warehouse
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python -m src.export.warehouse
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# Install (if not already)
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pip install streamlit
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# Launch dashboard
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streamlit run dashboard/app.py
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# Run tests
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python -m pytest dashboard/tests/ -v
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```
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## Data Refresh
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The warehouse reads pre-computed Parquet files. To refresh after running the pipeline:
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```bash
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python -m src.export.warehouse
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```
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Then reload the dashboard — `@st.cache_data(ttl=3600)` will pick up new files after 1 hour or on manual cache clear.
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## Dependencies
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Added to `requirements.txt`:
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- `streamlit>=1.35`
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Already present:
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- `plotly>=5.18`, `pandas>=2.1`, `numpy>=1.26`, `openpyxl>=3.1`
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