refactor(dashboard): switch a NiceGUI, rimuovi legacy Streamlit
NiceGUI è la dashboard ufficiale (port 8080, dark/neon palette, 3 pagine:
/, /convergence, /genomes). La porta è ora parametrica via env
SWARM_DASHBOARD_PORT, letta in ui.run() — Docker la usa anche per
healthcheck e label Traefik.
docker-compose.yml: entrypoint del servizio dashboard cambiato da
streamlit a python -m multi_swarm.dashboard.nicegui_app. Default porta
8080 ovunque (.env, .env.example, compose, healthcheck).
Rimossi i file legacy della vecchia GUI Streamlit:
- src/multi_swarm/dashboard/streamlit_app.py
- src/multi_swarm/dashboard/aquarium.py (helper canvas HTML5)
- src/multi_swarm/dashboard/pages/{01_overview,02_ga_convergence,
03_genomes,04_aquarium}.py
- tests/integration/test_streamlit_smoke.py
pyproject.toml: rimossa la dep streamlit; uv.lock rigenerato (10 deps
transitive eliminate: pydeck, watchdog, jsonschema, pillow, ecc.).
README aggiornato (architettura, comando dashboard, sezione Dashboard
ora descrive NiceGUI con riferimento al deploy Docker via Traefik).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
Adriano Dal Pastro
+2
-2
@@ -26,8 +26,8 @@ DB_PATH=./runs.db
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# Docker / Traefik (usati SOLO da docker-compose.yml)
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# Docker / Traefik (usati SOLO da docker-compose.yml)
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# Dominio base: traefik espone la dashboard su swarm.${DOMAIN_NAME}
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# Dominio base: traefik espone la dashboard su swarm.${DOMAIN_NAME}
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DOMAIN_NAME=tielogic.xyz
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DOMAIN_NAME=tielogic.xyz
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# Porta interna della Streamlit dashboard (Traefik fa il TLS davanti)
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# Porta interna della NiceGUI dashboard (Traefik fa il TLS davanti)
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SWARM_DASHBOARD_PORT=8501
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SWARM_DASHBOARD_PORT=8080
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|
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# Paper-trading runner — override del command nel compose (opzionali)
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# Paper-trading runner — override del command nel compose (opzionali)
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PAPER_RUN_NAME=phase3-papertrade-prod
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PAPER_RUN_NAME=phase3-papertrade-prod
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@@ -75,17 +75,11 @@ src/multi_swarm/
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├── orchestrator/
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├── orchestrator/
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│ └── run.py End-to-end pipeline + persistence
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│ └── run.py End-to-end pipeline + persistence
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└── dashboard/
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└── dashboard/
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├── streamlit_app.py Hub multipage
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├── nicegui_app.py NiceGUI dashboard (overview / convergence / genomes)
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├── data.py Lettura runs.db per le pagine
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└── data.py Lettura runs.db per le pagine
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├── aquarium.py Helper canvas HTML5 (fish data + JS template)
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└── pages/
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├── 01_overview.py Run + metriche aggregate
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├── 02_ga_convergence.py Fitness convergence + entropy plot
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├── 03_genomes.py Top-10 + ispezione system_prompt
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└── 04_aquarium.py Acquario 2D con click → info + lineage
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```
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```
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Stack: Python 3.13, uv, pytest+pytest-mock+responses, openai SDK (verso OpenRouter), requests+tenacity, pandas+numpy+scipy, sqlmodel+sqlite, streamlit+plotly.
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Stack: Python 3.13, uv, pytest+pytest-mock+responses, openai SDK (verso OpenRouter), requests+tenacity, pandas+numpy+scipy, sqlmodel+sqlite, nicegui+plotly.
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## Setup
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## Setup
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@@ -150,17 +144,18 @@ uv run python scripts/run_phase1.py \
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--population-size 20 --n-generations 10
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--population-size 20 --n-generations 10
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# Dashboard
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# Dashboard
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DB_PATH=./runs.db uv run streamlit run src/multi_swarm/dashboard/streamlit_app.py
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DB_PATH=./runs.db uv run python -m multi_swarm.dashboard.nicegui_app
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```
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```
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## Dashboard
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## Dashboard
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Streamlit multipage su `http://localhost:8501` (override con `--server.port`):
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NiceGUI dashboard (dark/neon palette) su `http://localhost:8080` (override con env `SWARM_DASHBOARD_PORT`):
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- **Overview**: lista runs, status, costo, metriche aggregate evaluations (parse success %, top fitness, median).
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- **Overview** (`/`): lista runs, status, costo, metriche aggregate evaluations (parse success %, top fitness, median).
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- **GA Convergence**: fitness median/max/p90 per generazione, entropy con hline a soglia gate (0.5).
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- **GA Convergence** (`/convergence`): fitness median/max/p90 per generazione, entropy con hline a soglia gate (0.5).
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- **Genomes**: top-10 ordinati per fitness, click su row per ispezione system_prompt + raw_text JSON strategy.
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- **Genomes** (`/genomes`): top-K ordinati per fitness, click su riga per ispezione system_prompt + raw_text JSON strategy.
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- **Aquarium**: visualizzazione 2D canvas HTML5 con un pesce per agente; dimensione ∝ fitness, colore per cognitive_style, halo sui top-3, click su pesce → panel info completo + lineage BFS (parents → grandparents → ...).
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In produzione gira dentro Docker dietro Traefik su `https://swarm.${DOMAIN_NAME}` — vedi `docker-compose.yml`.
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## Costi tipici Phase 1
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## Costi tipici Phase 1
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+5
-9
@@ -81,20 +81,16 @@ services:
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- ./data:/app/data:ro
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- ./data:/app/data:ro
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- ./series:/app/series:ro
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- ./series:/app/series:ro
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entrypoint:
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entrypoint:
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- streamlit
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- python
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- run
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- -m
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- /app/src/multi_swarm/dashboard/streamlit_app.py
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- multi_swarm.dashboard.nicegui_app
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- --server.address=0.0.0.0
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- --server.port=${SWARM_DASHBOARD_PORT:-8501}
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- --server.headless=true
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- --browser.gatherUsageStats=false
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command: []
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command: []
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healthcheck:
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healthcheck:
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test:
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test:
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- "CMD"
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- "CMD"
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- "python"
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- "python"
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- "-c"
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- "-c"
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- "import os, urllib.request; urllib.request.urlopen(f'http://localhost:{os.environ.get(\"SWARM_DASHBOARD_PORT\",\"8501\")}/_stcore/health', timeout=3).close()"
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- "import os, urllib.request; urllib.request.urlopen(f'http://localhost:{os.environ.get(\"SWARM_DASHBOARD_PORT\",\"8080\")}/', timeout=3).close()"
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interval: 30s
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interval: 30s
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timeout: 5s
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timeout: 5s
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retries: 3
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retries: 3
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@@ -106,5 +102,5 @@ services:
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- traefik.http.routers.multi-swarm-dashboard.tls=true
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- traefik.http.routers.multi-swarm-dashboard.tls=true
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- traefik.http.routers.multi-swarm-dashboard.entrypoints=websecure
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- traefik.http.routers.multi-swarm-dashboard.entrypoints=websecure
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- traefik.http.routers.multi-swarm-dashboard.tls.certresolver=mytlschallenge
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- traefik.http.routers.multi-swarm-dashboard.tls.certresolver=mytlschallenge
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- "traefik.http.services.multi-swarm-dashboard.loadbalancer.server.port=${SWARM_DASHBOARD_PORT:-8501}"
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- "traefik.http.services.multi-swarm-dashboard.loadbalancer.server.port=${SWARM_DASHBOARD_PORT:-8080}"
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- com.centurylinklabs.watchtower.enable=true
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- com.centurylinklabs.watchtower.enable=true
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@@ -16,7 +16,6 @@ dependencies = [
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"requests>=2.32",
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"requests>=2.32",
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"tenacity>=9.0",
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"tenacity>=9.0",
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"pyyaml>=6.0",
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"pyyaml>=6.0",
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"streamlit>=1.40",
|
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"plotly>=5.24",
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"plotly>=5.24",
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"pyarrow>=18.0",
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"pyarrow>=18.0",
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"nicegui>=3.11.1",
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"nicegui>=3.11.1",
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@@ -1,590 +0,0 @@
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"""Aquarium 2D visualization helpers.
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Builds fish records (with full genome attributes + ancestor lineage) and
|
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renders a self-contained HTML/JS canvas animation, embeddable in Streamlit
|
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via ``streamlit.components.v1.html``. Includes a click handler that opens
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an info panel showing genome details and BFS ancestor levels.
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"""
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from __future__ import annotations
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import json
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import math
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from typing import Any
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import pandas as pd # type: ignore[import-untyped]
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# Color palette per cognitive style. Default fallback for unknown styles is grey.
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STYLE_COLORS: dict[str, str] = {
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"physicist": "#4cc9f0",
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"biologist": "#52b788",
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"historian": "#e76f51",
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"meteorologist": "#ffd166",
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"ecologist": "#a78bfa",
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"engineer": "#fb6f92",
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}
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DEFAULT_COLOR: str = "#94a3b8"
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def _is_nan(v: Any) -> bool:
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try:
|
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return bool(pd.isna(v))
|
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except (TypeError, ValueError):
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return False
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def _safe_float(v: Any, default: float = 0.0) -> float:
|
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if v is None or _is_nan(v):
|
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return default
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try:
|
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return float(v)
|
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except (TypeError, ValueError):
|
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return default
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|
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|
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def _safe_int(v: Any, default: int = 0) -> int:
|
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if v is None or _is_nan(v):
|
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return default
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try:
|
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return int(v)
|
|
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except (TypeError, ValueError):
|
|
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return default
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|
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|
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def _safe_str(v: Any, default: str = "") -> str:
|
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if v is None or _is_nan(v):
|
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return default
|
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return str(v)
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def _safe_list(v: Any) -> list[Any]:
|
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if v is None:
|
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return []
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if isinstance(v, list):
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return list(v)
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# pandas may store python lists in object cells; if it's e.g. a numpy array,
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|
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# falling back to list() is fine. NaN scalar is excluded by _is_nan.
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if _is_nan(v):
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return []
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try:
|
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return list(v)
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except TypeError:
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return []
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|
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|
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def build_lineage_index(
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genomes_df: pd.DataFrame, evals_df: pd.DataFrame
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) -> dict[str, dict[str, Any]]:
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"""Build ``{genome_id: attrs}`` for every genome in the run.
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|
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``genomes_df`` must come from ``genomes_df(repo, run_id)`` (no gen filter):
|
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columns include ``id``, ``generation_idx``, ``system_prompt``,
|
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``feature_access``, ``temperature``, ``top_p``, ``model_tier``,
|
|
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``lookback_window``, ``cognitive_style``, ``parent_ids``, ``generation``.
|
|
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|
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``evals_df`` must come from ``evaluations_df(repo, run_id)``: columns
|
|
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include ``genome_id``, ``fitness``, ``dsr``, ``sharpe``, ``max_dd``,
|
|
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``n_trades``.
|
|
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"""
|
|
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if genomes_df.empty:
|
|
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return {}
|
|
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|
|
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if evals_df is None or evals_df.empty:
|
|
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merged = genomes_df.copy()
|
|
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for col in ("fitness", "dsr", "sharpe", "max_dd", "n_trades"):
|
|
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if col not in merged.columns:
|
|
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merged[col] = 0.0 if col != "n_trades" else 0
|
|
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else:
|
|
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merged = genomes_df.merge(
|
|
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evals_df,
|
|
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left_on="id",
|
|
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right_on="genome_id",
|
|
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how="left",
|
|
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suffixes=("", "_eval"),
|
|
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)
|
|
||||||
|
|
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index: dict[str, dict[str, Any]] = {}
|
|
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for _, row in merged.iterrows():
|
|
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gid = _safe_str(row.get("id"))
|
|
||||||
if not gid:
|
|
||||||
continue
|
|
||||||
# ``generation`` is the genome's evolutionary generation (from payload).
|
|
||||||
# If absent, fall back to ``generation_idx`` (column added by the
|
|
||||||
# repository). Defensive: both may be missing in edge cases.
|
|
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gen_val: Any = row.get("generation")
|
|
||||||
if gen_val is None or _is_nan(gen_val):
|
|
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gen_val = row.get("generation_idx", 0)
|
|
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index[gid] = {
|
|
||||||
"id": gid,
|
|
||||||
"generation": _safe_int(gen_val, 0),
|
|
||||||
"fitness": _safe_float(row.get("fitness"), 0.0),
|
|
||||||
"dsr": _safe_float(row.get("dsr"), 0.0),
|
|
||||||
"sharpe": _safe_float(row.get("sharpe"), 0.0),
|
|
||||||
"max_dd": _safe_float(row.get("max_dd"), 0.0),
|
|
||||||
"n_trades": _safe_int(row.get("n_trades"), 0),
|
|
||||||
"cognitive_style": _safe_str(row.get("cognitive_style"), ""),
|
|
||||||
"system_prompt": _safe_str(row.get("system_prompt"), ""),
|
|
||||||
"temperature": _safe_float(row.get("temperature"), 0.0),
|
|
||||||
"lookback_window": _safe_int(row.get("lookback_window"), 0),
|
|
||||||
"feature_access": _safe_list(row.get("feature_access")),
|
|
||||||
"model_tier": _safe_str(row.get("model_tier"), ""),
|
|
||||||
"parent_ids": _safe_list(row.get("parent_ids")),
|
|
||||||
}
|
|
||||||
return index
|
|
||||||
|
|
||||||
|
|
||||||
def trace_ancestors(
|
|
||||||
genome_id: str,
|
|
||||||
lineage_index: dict[str, dict[str, Any]],
|
|
||||||
max_levels: int = 5,
|
|
||||||
) -> list[list[dict[str, Any]]]:
|
|
||||||
"""BFS over ``parent_ids`` returning levels of ancestors.
|
|
||||||
|
|
||||||
``levels[0]`` = direct parents, ``levels[1]`` = grandparents, etc. Each
|
|
||||||
entry is a small dict (no ``system_prompt``, to keep JSON payload light):
|
|
||||||
``{id, generation, fitness, cognitive_style}``. Cycles are guarded via a
|
|
||||||
``seen`` set; missing parents (not in this run) are stubbed with sentinel
|
|
||||||
values so the lineage display still renders the relationship.
|
|
||||||
"""
|
|
||||||
levels: list[list[dict[str, Any]]] = []
|
|
||||||
root = lineage_index.get(genome_id, {})
|
|
||||||
current_ids: list[str] = list(root.get("parent_ids", []))
|
|
||||||
seen: set[str] = {genome_id}
|
|
||||||
for _ in range(max_levels):
|
|
||||||
if not current_ids:
|
|
||||||
break
|
|
||||||
level_entries: list[dict[str, Any]] = []
|
|
||||||
next_ids: list[str] = []
|
|
||||||
for pid in current_ids:
|
|
||||||
if pid in seen:
|
|
||||||
continue
|
|
||||||
seen.add(pid)
|
|
||||||
entry = lineage_index.get(pid)
|
|
||||||
if entry is None:
|
|
||||||
level_entries.append(
|
|
||||||
{
|
|
||||||
"id": pid,
|
|
||||||
"generation": -1,
|
|
||||||
"fitness": 0.0,
|
|
||||||
"cognitive_style": "",
|
|
||||||
}
|
|
||||||
)
|
|
||||||
continue
|
|
||||||
level_entries.append(
|
|
||||||
{
|
|
||||||
"id": entry["id"],
|
|
||||||
"generation": entry["generation"],
|
|
||||||
"fitness": entry["fitness"],
|
|
||||||
"cognitive_style": entry["cognitive_style"],
|
|
||||||
}
|
|
||||||
)
|
|
||||||
next_ids.extend(entry.get("parent_ids", []))
|
|
||||||
if not level_entries:
|
|
||||||
break
|
|
||||||
levels.append(level_entries)
|
|
||||||
current_ids = next_ids
|
|
||||||
return levels
|
|
||||||
|
|
||||||
|
|
||||||
def build_fish_dataset(
|
|
||||||
active_df: pd.DataFrame,
|
|
||||||
lineage_index: dict[str, dict[str, Any]] | None = None,
|
|
||||||
max_lineage_levels: int = 5,
|
|
||||||
) -> list[dict[str, Any]]:
|
|
||||||
"""Build full fish records for each active genome.
|
|
||||||
|
|
||||||
For every row in ``active_df`` the matching entry in ``lineage_index`` is
|
|
||||||
looked up by ``genome_id`` (or ``id``) and attached together with the BFS
|
|
||||||
ancestor levels. Rows whose id is not in the index are skipped.
|
|
||||||
|
|
||||||
Backward-compat: if ``lineage_index`` is ``None`` (legacy call site, e.g.
|
|
||||||
test fixtures with simple merged DataFrames) we synthesize a minimal
|
|
||||||
lineage from ``active_df`` itself so the function still returns useful
|
|
||||||
fish records.
|
|
||||||
"""
|
|
||||||
if active_df.empty:
|
|
||||||
return []
|
|
||||||
|
|
||||||
if lineage_index is None:
|
|
||||||
# Legacy path: build a tiny index from the active df only.
|
|
||||||
synth: dict[str, dict[str, Any]] = {}
|
|
||||||
for _, row in active_df.iterrows():
|
|
||||||
gid = _safe_str(row.get("genome_id") or row.get("id"))
|
|
||||||
if not gid:
|
|
||||||
continue
|
|
||||||
fitness_val = _safe_float(row.get("fitness"), float("nan"))
|
|
||||||
if math.isnan(fitness_val):
|
|
||||||
continue
|
|
||||||
synth[gid] = {
|
|
||||||
"id": gid,
|
|
||||||
"generation": _safe_int(row.get("generation"), 0),
|
|
||||||
"fitness": fitness_val,
|
|
||||||
"dsr": _safe_float(row.get("dsr"), 0.0),
|
|
||||||
"sharpe": _safe_float(row.get("sharpe"), 0.0),
|
|
||||||
"max_dd": _safe_float(row.get("max_dd"), 0.0),
|
|
||||||
"n_trades": _safe_int(row.get("n_trades"), 0),
|
|
||||||
"cognitive_style": _safe_str(row.get("cognitive_style"), "unknown"),
|
|
||||||
"system_prompt": _safe_str(row.get("system_prompt"), ""),
|
|
||||||
"temperature": _safe_float(row.get("temperature"), 0.0),
|
|
||||||
"lookback_window": _safe_int(row.get("lookback_window"), 0),
|
|
||||||
"feature_access": _safe_list(row.get("feature_access")),
|
|
||||||
"model_tier": _safe_str(row.get("model_tier"), ""),
|
|
||||||
"parent_ids": _safe_list(row.get("parent_ids")),
|
|
||||||
}
|
|
||||||
lineage_index = synth
|
|
||||||
|
|
||||||
fish: list[dict[str, Any]] = []
|
|
||||||
for _, row in active_df.iterrows():
|
|
||||||
gid = _safe_str(row.get("genome_id") or row.get("id"))
|
|
||||||
if not gid:
|
|
||||||
continue
|
|
||||||
attrs = lineage_index.get(gid)
|
|
||||||
if attrs is None:
|
|
||||||
continue
|
|
||||||
if math.isnan(attrs.get("fitness", 0.0)):
|
|
||||||
continue
|
|
||||||
ancestors = trace_ancestors(gid, lineage_index, max_lineage_levels)
|
|
||||||
record = {**attrs, "ancestors": ancestors}
|
|
||||||
fish.append(record)
|
|
||||||
return fish
|
|
||||||
|
|
||||||
|
|
||||||
def build_aquarium_html(
|
|
||||||
fish: list[dict[str, Any]],
|
|
||||||
canvas_w: int = 1000,
|
|
||||||
canvas_h: int = 600,
|
|
||||||
) -> str:
|
|
||||||
"""Build the self-contained HTML/JS string for the aquarium canvas.
|
|
||||||
|
|
||||||
The output embeds a click handler: tapping a fish opens an info panel
|
|
||||||
(top-right of the canvas) showing its genome attributes and BFS ancestor
|
|
||||||
levels. Labels are no longer rendered on the canvas itself.
|
|
||||||
"""
|
|
||||||
fish_json = json.dumps(fish)
|
|
||||||
palette_json = json.dumps(STYLE_COLORS)
|
|
||||||
default_color = DEFAULT_COLOR
|
|
||||||
|
|
||||||
# All braces inside <style>/<script> are escaped to literals using {{ }}
|
|
||||||
# so we can use Python f-string substitution for the few JSON payloads.
|
|
||||||
return f"""
|
|
||||||
<div style="position:relative;width:100%;height:{canvas_h}px;">
|
|
||||||
<canvas id="aquarium" width="{canvas_w}" height="{canvas_h}"
|
|
||||||
style="width:100%;height:{canvas_h}px;border-radius:12px;
|
|
||||||
background:linear-gradient(180deg,#0a2540 0%,#1a4d80 100%);
|
|
||||||
display:block;cursor:pointer;"></canvas>
|
|
||||||
<div id="fish-info-panel" style="
|
|
||||||
position:absolute;
|
|
||||||
top:12px;
|
|
||||||
right:12px;
|
|
||||||
width:340px;
|
|
||||||
max-height:580px;
|
|
||||||
overflow-y:auto;
|
|
||||||
background:rgba(8,16,32,0.92);
|
|
||||||
color:#e2e8f0;
|
|
||||||
border-radius:10px;
|
|
||||||
padding:14px 16px;
|
|
||||||
font-family:system-ui,-apple-system,sans-serif;
|
|
||||||
font-size:12px;
|
|
||||||
line-height:1.5;
|
|
||||||
border:1px solid rgba(255,255,255,0.1);
|
|
||||||
backdrop-filter:blur(6px);
|
|
||||||
-webkit-backdrop-filter:blur(6px);
|
|
||||||
display:none;
|
|
||||||
z-index:10;
|
|
||||||
">
|
|
||||||
<div id="fish-info-content"></div>
|
|
||||||
<button id="fish-info-close" style="
|
|
||||||
position:absolute;top:8px;right:10px;
|
|
||||||
background:transparent;color:#94a3b8;border:none;
|
|
||||||
cursor:pointer;font-size:16px;
|
|
||||||
">×</button>
|
|
||||||
</div>
|
|
||||||
</div>
|
|
||||||
<script>
|
|
||||||
(function() {{
|
|
||||||
const FISH_DATA = {fish_json};
|
|
||||||
const STYLE_COLORS = {palette_json};
|
|
||||||
const DEFAULT_COLOR = {json.dumps(default_color)};
|
|
||||||
|
|
||||||
const canvas = document.getElementById('aquarium');
|
|
||||||
if (!canvas) return;
|
|
||||||
const ctx = canvas.getContext('2d');
|
|
||||||
const W = canvas.width;
|
|
||||||
const H = canvas.height;
|
|
||||||
|
|
||||||
const panel = document.getElementById('fish-info-panel');
|
|
||||||
const panelContent = document.getElementById('fish-info-content');
|
|
||||||
const closeBtn = document.getElementById('fish-info-close');
|
|
||||||
if (closeBtn) {{
|
|
||||||
closeBtn.addEventListener('click', function() {{
|
|
||||||
panel.style.display = 'none';
|
|
||||||
}});
|
|
||||||
}}
|
|
||||||
|
|
||||||
// Normalize fitness for sizing.
|
|
||||||
let maxFit = 0.0;
|
|
||||||
for (const f of FISH_DATA) {{
|
|
||||||
if (typeof f.fitness === 'number' && f.fitness > maxFit) maxFit = f.fitness;
|
|
||||||
}}
|
|
||||||
|
|
||||||
function lerp(a, b, t) {{ return a + (b - a) * t; }}
|
|
||||||
|
|
||||||
function radiusFor(fitness) {{
|
|
||||||
if (maxFit <= 0) return 8;
|
|
||||||
const t = Math.max(0.05, Math.min(1.0, fitness / maxFit));
|
|
||||||
return lerp(8, 40, t);
|
|
||||||
}}
|
|
||||||
|
|
||||||
function colorFor(style) {{
|
|
||||||
return STYLE_COLORS[style] || DEFAULT_COLOR;
|
|
||||||
}}
|
|
||||||
|
|
||||||
// Init fish state. Each entry keeps a reference to the original data dict
|
|
||||||
// so the click handler can show full attributes + ancestors.
|
|
||||||
const fishState = FISH_DATA.map(function(f, idx) {{
|
|
||||||
const r = radiusFor(f.fitness);
|
|
||||||
return {{
|
|
||||||
data: f,
|
|
||||||
color: colorFor(f.cognitive_style),
|
|
||||||
radius: r,
|
|
||||||
x: Math.random() * (W - 2 * r) + r,
|
|
||||||
y: Math.random() * (H - 2 * r) + r,
|
|
||||||
vx: (Math.random() - 0.5) * 1.5,
|
|
||||||
vy: (Math.random() - 0.5) * 1.0,
|
|
||||||
rank: idx,
|
|
||||||
}};
|
|
||||||
}});
|
|
||||||
|
|
||||||
// Bubbles for ambience.
|
|
||||||
const N_BUBBLES = 25;
|
|
||||||
const bubbles = Array.from({{length: N_BUBBLES}}, function() {{ return {{
|
|
||||||
x: Math.random() * W,
|
|
||||||
y: Math.random() * H,
|
|
||||||
r: 1 + Math.random() * 3,
|
|
||||||
vy: 0.3 + Math.random() * 0.7,
|
|
||||||
}}; }});
|
|
||||||
|
|
||||||
function drawBubble(b) {{
|
|
||||||
ctx.beginPath();
|
|
||||||
ctx.arc(b.x, b.y, b.r, 0, Math.PI * 2);
|
|
||||||
ctx.fillStyle = 'rgba(255,255,255,0.18)';
|
|
||||||
ctx.fill();
|
|
||||||
}}
|
|
||||||
|
|
||||||
function updateBubble(b) {{
|
|
||||||
b.y -= b.vy;
|
|
||||||
if (b.y < -10) {{
|
|
||||||
b.y = H + 5;
|
|
||||||
b.x = Math.random() * W;
|
|
||||||
}}
|
|
||||||
}}
|
|
||||||
|
|
||||||
function drawFish(f) {{
|
|
||||||
const facingLeft = f.vx < 0;
|
|
||||||
ctx.save();
|
|
||||||
ctx.translate(f.x, f.y);
|
|
||||||
if (facingLeft) ctx.scale(-1, 1);
|
|
||||||
|
|
||||||
// Halo for top-3 fish.
|
|
||||||
if (f.rank < 3) {{
|
|
||||||
const grad = ctx.createRadialGradient(0, 0, f.radius * 0.5, 0, 0, f.radius * 2.0);
|
|
||||||
grad.addColorStop(0, f.color + 'aa');
|
|
||||||
grad.addColorStop(1, f.color + '00');
|
|
||||||
ctx.fillStyle = grad;
|
|
||||||
ctx.beginPath();
|
|
||||||
ctx.arc(0, 0, f.radius * 2.0, 0, Math.PI * 2);
|
|
||||||
ctx.fill();
|
|
||||||
}}
|
|
||||||
|
|
||||||
// Body (ellipse).
|
|
||||||
ctx.fillStyle = f.color;
|
|
||||||
ctx.beginPath();
|
|
||||||
ctx.ellipse(0, 0, f.radius, f.radius * 0.6, 0, 0, Math.PI * 2);
|
|
||||||
ctx.fill();
|
|
||||||
|
|
||||||
// Tail.
|
|
||||||
ctx.beginPath();
|
|
||||||
ctx.moveTo(-f.radius, 0);
|
|
||||||
ctx.lineTo(-f.radius * 1.6, -f.radius * 0.5);
|
|
||||||
ctx.lineTo(-f.radius * 1.6, f.radius * 0.5);
|
|
||||||
ctx.closePath();
|
|
||||||
ctx.fill();
|
|
||||||
|
|
||||||
// Eye.
|
|
||||||
ctx.fillStyle = '#ffffff';
|
|
||||||
ctx.beginPath();
|
|
||||||
ctx.arc(f.radius * 0.45, -f.radius * 0.15, Math.max(1.5, f.radius * 0.12), 0, Math.PI * 2);
|
|
||||||
ctx.fill();
|
|
||||||
ctx.fillStyle = '#1a1a1a';
|
|
||||||
ctx.beginPath();
|
|
||||||
ctx.arc(f.radius * 0.50, -f.radius * 0.15, Math.max(0.8, f.radius * 0.06), 0, Math.PI * 2);
|
|
||||||
ctx.fill();
|
|
||||||
|
|
||||||
ctx.restore();
|
|
||||||
}}
|
|
||||||
|
|
||||||
function updateFish(f) {{
|
|
||||||
f.vx += (Math.random() - 0.5) * 0.05;
|
|
||||||
f.vy += (Math.random() - 0.5) * 0.05;
|
|
||||||
|
|
||||||
const speed = Math.hypot(f.vx, f.vy);
|
|
||||||
const maxSpeed = 1.5;
|
|
||||||
if (speed > maxSpeed) {{
|
|
||||||
f.vx = (f.vx / speed) * maxSpeed;
|
|
||||||
f.vy = (f.vy / speed) * maxSpeed;
|
|
||||||
}}
|
|
||||||
|
|
||||||
f.x += f.vx;
|
|
||||||
f.y += f.vy;
|
|
||||||
|
|
||||||
if (f.x < f.radius) {{ f.x = f.radius; f.vx = -f.vx; }}
|
|
||||||
if (f.x > W - f.radius) {{ f.x = W - f.radius; f.vx = -f.vx; }}
|
|
||||||
if (f.y < f.radius) {{ f.y = f.radius; f.vy = -f.vy; }}
|
|
||||||
if (f.y > H - f.radius) {{ f.y = H - f.radius; f.vy = -f.vy; }}
|
|
||||||
}}
|
|
||||||
|
|
||||||
function frame() {{
|
|
||||||
ctx.clearRect(0, 0, W, H);
|
|
||||||
|
|
||||||
ctx.strokeStyle = 'rgba(255,255,255,0.04)';
|
|
||||||
ctx.lineWidth = 1;
|
|
||||||
for (let i = 0; i < 6; i++) {{
|
|
||||||
const y = (H / 6) * i + (Date.now() / 50 % (H / 6));
|
|
||||||
ctx.beginPath();
|
|
||||||
ctx.moveTo(0, y);
|
|
||||||
ctx.lineTo(W, y);
|
|
||||||
ctx.stroke();
|
|
||||||
}}
|
|
||||||
|
|
||||||
for (const b of bubbles) {{
|
|
||||||
updateBubble(b);
|
|
||||||
drawBubble(b);
|
|
||||||
}}
|
|
||||||
for (const f of fishState) {{
|
|
||||||
updateFish(f);
|
|
||||||
drawFish(f);
|
|
||||||
}}
|
|
||||||
requestAnimationFrame(frame);
|
|
||||||
}}
|
|
||||||
|
|
||||||
// CLICK HANDLER: hit-test in canvas pixel space (account for CSS scaling).
|
|
||||||
canvas.addEventListener('click', function(e) {{
|
|
||||||
const rect = canvas.getBoundingClientRect();
|
|
||||||
const scaleX = canvas.width / rect.width;
|
|
||||||
const scaleY = canvas.height / rect.height;
|
|
||||||
const cx = (e.clientX - rect.left) * scaleX;
|
|
||||||
const cy = (e.clientY - rect.top) * scaleY;
|
|
||||||
let best = null;
|
|
||||||
let bestDist = Infinity;
|
|
||||||
for (const f of fishState) {{
|
|
||||||
const dx = cx - f.x;
|
|
||||||
const dy = cy - f.y;
|
|
||||||
const d = Math.sqrt(dx * dx + dy * dy);
|
|
||||||
const hit = Math.max(f.radius + 6, 14);
|
|
||||||
if (d < hit && d < bestDist) {{
|
|
||||||
best = f;
|
|
||||||
bestDist = d;
|
|
||||||
}}
|
|
||||||
}}
|
|
||||||
if (best) showFishInfo(best);
|
|
||||||
}});
|
|
||||||
|
|
||||||
const ROW_STYLE = 'display:flex;justify-content:space-between;'
|
|
||||||
+ 'padding:2px 0;border-bottom:1px solid rgba(255,255,255,0.05);';
|
|
||||||
const PROMPT_STYLE = 'margin-top:10px;padding:8px;'
|
|
||||||
+ 'background:rgba(255,255,255,0.04);border-radius:6px;'
|
|
||||||
+ 'font-size:11px;font-style:italic;color:#cbd5e1;';
|
|
||||||
const ANC_HEAD_STYLE = 'margin:14px 0 6px 0;color:#94a3b8;'
|
|
||||||
+ 'text-transform:uppercase;font-size:10px;letter-spacing:1px;';
|
|
||||||
const ANC_ROW_STYLE = 'display:flex;align-items:center;padding:4px 6px;'
|
|
||||||
+ 'margin-bottom:2px;background:rgba(255,255,255,0.03);'
|
|
||||||
+ 'border-radius:4px;border-left:3px solid ';
|
|
||||||
const NO_ANC_STYLE = 'margin-top:10px;font-size:10px;color:#64748b;';
|
|
||||||
const DASH = '\\u2014';
|
|
||||||
|
|
||||||
function metricRow(label, value) {{
|
|
||||||
return '<div style="' + ROW_STYLE + '">'
|
|
||||||
+ '<span style="color:#94a3b8;">' + label + '</span>'
|
|
||||||
+ '<span style="color:#e2e8f0;">' + value + '</span></div>';
|
|
||||||
}}
|
|
||||||
|
|
||||||
function escapeHtml(s) {{
|
|
||||||
const div = document.createElement('div');
|
|
||||||
div.appendChild(document.createTextNode(String(s)));
|
|
||||||
return div.innerHTML;
|
|
||||||
}}
|
|
||||||
|
|
||||||
function fmt(v, dp) {{
|
|
||||||
if (v === null || v === undefined || typeof v !== 'number' || isNaN(v)) {{
|
|
||||||
return DASH;
|
|
||||||
}}
|
|
||||||
return v.toFixed(dp);
|
|
||||||
}}
|
|
||||||
|
|
||||||
function showFishInfo(fish) {{
|
|
||||||
const data = fish.data;
|
|
||||||
const styleColor = STYLE_COLORS[data.cognitive_style] || DEFAULT_COLOR;
|
|
||||||
let html = '';
|
|
||||||
const idShort = String(data.id || '').slice(0, 8);
|
|
||||||
html += '<h4 style="margin:0 0 10px 0;color:' + styleColor + ';">';
|
|
||||||
html += escapeHtml(idShort)
|
|
||||||
+ ' <span style="color:#94a3b8;font-weight:normal;font-size:11px;">'
|
|
||||||
+ 'gen ' + escapeHtml(data.generation) + '</span>';
|
|
||||||
html += '</h4>';
|
|
||||||
html += metricRow('fitness', fmt(data.fitness, 3));
|
|
||||||
html += metricRow('DSR', fmt(data.dsr, 3));
|
|
||||||
html += metricRow('Sharpe', fmt(data.sharpe, 3));
|
|
||||||
html += metricRow('max DD', fmt(data.max_dd, 3));
|
|
||||||
const trades = data.n_trades == null ? 0 : data.n_trades;
|
|
||||||
html += metricRow('trades', escapeHtml(trades));
|
|
||||||
html += metricRow('style', escapeHtml(data.cognitive_style || DASH));
|
|
||||||
html += metricRow('tier', escapeHtml(data.model_tier || DASH));
|
|
||||||
html += metricRow('temp', fmt(data.temperature, 2));
|
|
||||||
const lookback = data.lookback_window == null ? DASH : data.lookback_window;
|
|
||||||
html += metricRow('lookback', escapeHtml(lookback));
|
|
||||||
const feats = (data.feature_access || []).join(', ');
|
|
||||||
html += metricRow('features', escapeHtml(feats || DASH));
|
|
||||||
if (data.system_prompt) {{
|
|
||||||
html += '<div style="' + PROMPT_STYLE + '">'
|
|
||||||
+ escapeHtml(data.system_prompt) + '</div>';
|
|
||||||
}}
|
|
||||||
if (data.ancestors && data.ancestors.length > 0) {{
|
|
||||||
html += '<h5 style="' + ANC_HEAD_STYLE + '">Discendenza</h5>';
|
|
||||||
data.ancestors.forEach(function(level, idx) {{
|
|
||||||
html += '<div style="margin-bottom:8px;">';
|
|
||||||
html += '<div style="font-size:10px;color:#64748b;margin-bottom:4px;">'
|
|
||||||
+ 'Gen N\\u2212' + (idx + 1) + '</div>';
|
|
||||||
level.forEach(function(ancestor) {{
|
|
||||||
const c = STYLE_COLORS[ancestor.cognitive_style] || DEFAULT_COLOR;
|
|
||||||
const aShort = String(ancestor.id || '').slice(0, 8);
|
|
||||||
html += '<div style="' + ANC_ROW_STYLE + c + ';">';
|
|
||||||
html += '<code style="color:' + c + ';font-size:10px;">'
|
|
||||||
+ escapeHtml(aShort) + '</code>';
|
|
||||||
const af = ancestor.fitness;
|
|
||||||
const fitTxt = (typeof af === 'number' && !isNaN(af))
|
|
||||||
? af.toFixed(2) : DASH;
|
|
||||||
html += '<span style="margin-left:auto;color:#94a3b8;font-size:10px;">'
|
|
||||||
+ 'fit ' + fitTxt + '</span>';
|
|
||||||
html += '</div>';
|
|
||||||
}});
|
|
||||||
html += '</div>';
|
|
||||||
}});
|
|
||||||
}} else {{
|
|
||||||
html += '<div style="' + NO_ANC_STYLE + '">'
|
|
||||||
+ 'Genoma di prima generazione (no ancestors)</div>';
|
|
||||||
}}
|
|
||||||
panelContent.innerHTML = html;
|
|
||||||
panel.style.display = 'block';
|
|
||||||
}}
|
|
||||||
|
|
||||||
if (fishState.length === 0) {{
|
|
||||||
ctx.fillStyle = 'rgba(255,255,255,0.7)';
|
|
||||||
ctx.font = '16px sans-serif';
|
|
||||||
ctx.textAlign = 'center';
|
|
||||||
ctx.fillText('Acquario vuoto: nessun genoma da mostrare.', W / 2, H / 2);
|
|
||||||
}} else {{
|
|
||||||
requestAnimationFrame(frame);
|
|
||||||
}}
|
|
||||||
}})();
|
|
||||||
</script>
|
|
||||||
"""
|
|
||||||
@@ -868,7 +868,7 @@ def main() -> None:
|
|||||||
app.on_startup(lambda: print(f"DB: {Path(DB_PATH).resolve()}"))
|
app.on_startup(lambda: print(f"DB: {Path(DB_PATH).resolve()}"))
|
||||||
ui.run(
|
ui.run(
|
||||||
host="0.0.0.0",
|
host="0.0.0.0",
|
||||||
port=8080,
|
port=int(os.environ.get("SWARM_DASHBOARD_PORT", "8080")),
|
||||||
title="Multi-Swarm Dashboard",
|
title="Multi-Swarm Dashboard",
|
||||||
reload=False,
|
reload=False,
|
||||||
show=False,
|
show=False,
|
||||||
|
|||||||
@@ -1,84 +0,0 @@
|
|||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
from datetime import datetime
|
|
||||||
|
|
||||||
import streamlit as st
|
|
||||||
|
|
||||||
from multi_swarm.dashboard.data import (
|
|
||||||
evaluations_df,
|
|
||||||
generations_df,
|
|
||||||
get_repo,
|
|
||||||
get_run_overview,
|
|
||||||
list_runs_df,
|
|
||||||
)
|
|
||||||
|
|
||||||
st.title("Overview")
|
|
||||||
|
|
||||||
db_path = st.session_state.get("db_path", "./runs.db")
|
|
||||||
repo = get_repo(db_path)
|
|
||||||
|
|
||||||
runs = list_runs_df(repo)
|
|
||||||
if runs.empty:
|
|
||||||
st.info("Nessuna run nel database. Esegui `scripts/run_phase1.py` per generarne una.")
|
|
||||||
st.stop()
|
|
||||||
|
|
||||||
st.subheader("Tutte le run")
|
|
||||||
st.dataframe(runs[["id", "name", "started_at", "completed_at", "status", "total_cost_usd"]])
|
|
||||||
|
|
||||||
selected = st.selectbox("Seleziona run per dettaglio", runs["id"].tolist())
|
|
||||||
overview = get_run_overview(repo, selected)
|
|
||||||
|
|
||||||
# --- Progress live ---
|
|
||||||
cfg = overview["config"]
|
|
||||||
pop_size = int(cfg.get("population_size", 0))
|
|
||||||
n_gens = int(cfg.get("n_generations", 0))
|
|
||||||
evals = evaluations_df(repo, selected)
|
|
||||||
gens = generations_df(repo, selected)
|
|
||||||
|
|
||||||
evals_done = len(evals)
|
|
||||||
evals_total = max(pop_size * n_gens, 1)
|
|
||||||
gens_done = int(gens["completed_at"].notna().sum()) if not gens.empty else 0
|
|
||||||
|
|
||||||
status_emoji = {"running": "🟢", "completed": "✅", "failed": "❌"}.get(overview["status"], "⚪")
|
|
||||||
top_fit = float(evals["fitness"].max()) if not evals.empty else float("nan")
|
|
||||||
|
|
||||||
st.subheader(f"{status_emoji} Progresso run")
|
|
||||||
st.progress(
|
|
||||||
min(gens_done / max(n_gens, 1), 1.0),
|
|
||||||
text=f"Generations: {gens_done}/{n_gens}",
|
|
||||||
)
|
|
||||||
st.progress(
|
|
||||||
min(evals_done / evals_total, 1.0),
|
|
||||||
text=f"Evaluations: {evals_done}/{evals_total} ({100*evals_done/evals_total:.1f}%)",
|
|
||||||
)
|
|
||||||
pcol1, pcol2, pcol3 = st.columns(3)
|
|
||||||
pcol1.metric("Top fitness", f"{top_fit:.4f}" if evals_done else "—")
|
|
||||||
pcol2.metric("Median fitness", f"{evals['fitness'].median():.4f}" if evals_done else "—")
|
|
||||||
pcol3.metric("Cost so far", f"${overview['total_cost_usd']:.4f}")
|
|
||||||
|
|
||||||
ref_col1, ref_col2 = st.columns([1, 4])
|
|
||||||
if ref_col1.button("🔄 Refresh"):
|
|
||||||
st.rerun()
|
|
||||||
ref_col2.caption(f"Last update: {datetime.now().strftime('%H:%M:%S')}")
|
|
||||||
|
|
||||||
st.divider()
|
|
||||||
|
|
||||||
col1, col2, col3, col4 = st.columns(4)
|
|
||||||
col1.metric("Status", overview["status"])
|
|
||||||
col2.metric("Cost (USD)", f"{overview['total_cost_usd']:.4f}")
|
|
||||||
col3.metric("Started", overview["started_at"])
|
|
||||||
col4.metric("Completed", overview["completed_at"] or "—")
|
|
||||||
|
|
||||||
st.subheader("Statistiche evaluations")
|
|
||||||
col5, col6, col7, col8 = st.columns(4)
|
|
||||||
if not evals.empty:
|
|
||||||
parse_success = 100 * (evals["parse_error"].isna().sum() / len(evals))
|
|
||||||
col5.metric("Evaluations totali", len(evals))
|
|
||||||
col6.metric("Parse success %", f"{parse_success:.1f}%")
|
|
||||||
col7.metric("Top fitness", f"{evals['fitness'].max():.3f}")
|
|
||||||
col8.metric("Median fitness", f"{evals['fitness'].median():.3f}")
|
|
||||||
else:
|
|
||||||
col5.metric("Evaluations totali", 0)
|
|
||||||
|
|
||||||
st.subheader("Config")
|
|
||||||
st.json(overview["config"])
|
|
||||||
@@ -1,68 +0,0 @@
|
|||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
import plotly.graph_objects as go # type: ignore[import-untyped]
|
|
||||||
import streamlit as st
|
|
||||||
|
|
||||||
from multi_swarm.dashboard.data import generations_df, get_repo, list_runs_df
|
|
||||||
|
|
||||||
st.title("GA Convergence")
|
|
||||||
|
|
||||||
db_path = st.session_state.get("db_path", "./runs.db")
|
|
||||||
repo = get_repo(db_path)
|
|
||||||
|
|
||||||
runs = list_runs_df(repo)
|
|
||||||
if runs.empty:
|
|
||||||
st.info("Nessuna run.")
|
|
||||||
st.stop()
|
|
||||||
|
|
||||||
selected = st.selectbox("Run", runs["id"].tolist())
|
|
||||||
gens = generations_df(repo, selected)
|
|
||||||
if gens.empty:
|
|
||||||
st.warning("Nessuna generazione registrata per questa run.")
|
|
||||||
st.stop()
|
|
||||||
|
|
||||||
fig = go.Figure()
|
|
||||||
fig.add_trace(
|
|
||||||
go.Scatter(
|
|
||||||
x=gens["generation_idx"],
|
|
||||||
y=gens["fitness_median"],
|
|
||||||
name="median",
|
|
||||||
mode="lines+markers",
|
|
||||||
)
|
|
||||||
)
|
|
||||||
fig.add_trace(
|
|
||||||
go.Scatter(
|
|
||||||
x=gens["generation_idx"],
|
|
||||||
y=gens["fitness_max"],
|
|
||||||
name="max",
|
|
||||||
mode="lines+markers",
|
|
||||||
)
|
|
||||||
)
|
|
||||||
fig.add_trace(
|
|
||||||
go.Scatter(
|
|
||||||
x=gens["generation_idx"],
|
|
||||||
y=gens["fitness_p90"],
|
|
||||||
name="p90",
|
|
||||||
mode="lines+markers",
|
|
||||||
)
|
|
||||||
)
|
|
||||||
fig.update_layout(
|
|
||||||
xaxis_title="generation",
|
|
||||||
yaxis_title="fitness",
|
|
||||||
title="Fitness convergence",
|
|
||||||
)
|
|
||||||
st.plotly_chart(fig, use_container_width=True)
|
|
||||||
|
|
||||||
st.subheader("Entropy")
|
|
||||||
fig2 = go.Figure()
|
|
||||||
fig2.add_trace(go.Scatter(x=gens["generation_idx"], y=gens["entropy"], mode="lines+markers"))
|
|
||||||
fig2.add_hline(y=0.5, line_dash="dash", annotation_text="gate threshold (0.5)")
|
|
||||||
fig2.update_layout(
|
|
||||||
xaxis_title="generation",
|
|
||||||
yaxis_title="entropy",
|
|
||||||
title="Diversity (fitness entropy)",
|
|
||||||
)
|
|
||||||
st.plotly_chart(fig2, use_container_width=True)
|
|
||||||
|
|
||||||
st.subheader("Tabella generazioni")
|
|
||||||
st.dataframe(gens)
|
|
||||||
@@ -1,72 +0,0 @@
|
|||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
import streamlit as st
|
|
||||||
|
|
||||||
from multi_swarm.dashboard.data import (
|
|
||||||
evaluations_df,
|
|
||||||
genomes_df,
|
|
||||||
get_repo,
|
|
||||||
list_runs_df,
|
|
||||||
)
|
|
||||||
|
|
||||||
st.title("Genomes")
|
|
||||||
|
|
||||||
db_path = st.session_state.get("db_path", "./runs.db")
|
|
||||||
repo = get_repo(db_path)
|
|
||||||
|
|
||||||
runs = list_runs_df(repo)
|
|
||||||
if runs.empty:
|
|
||||||
st.info("Nessuna run.")
|
|
||||||
st.stop()
|
|
||||||
|
|
||||||
selected = st.selectbox("Run", runs["id"].tolist())
|
|
||||||
evals = evaluations_df(repo, selected)
|
|
||||||
genomes = genomes_df(repo, selected)
|
|
||||||
|
|
||||||
if evals.empty:
|
|
||||||
st.warning("Nessuna evaluation.")
|
|
||||||
st.stop()
|
|
||||||
|
|
||||||
merged = evals.merge(
|
|
||||||
genomes, left_on="genome_id", right_on="id", how="left", suffixes=("", "_g")
|
|
||||||
)
|
|
||||||
top = merged.sort_values("fitness", ascending=False).head(10)
|
|
||||||
|
|
||||||
st.subheader("Top-10 genomi (per fitness)")
|
|
||||||
display_cols = [
|
|
||||||
"genome_id",
|
|
||||||
"fitness",
|
|
||||||
"dsr",
|
|
||||||
"sharpe",
|
|
||||||
"max_dd",
|
|
||||||
"n_trades",
|
|
||||||
"cognitive_style",
|
|
||||||
"temperature",
|
|
||||||
"lookback_window",
|
|
||||||
"feature_access",
|
|
||||||
]
|
|
||||||
existing = [c for c in display_cols if c in top.columns]
|
|
||||||
st.dataframe(top[existing])
|
|
||||||
|
|
||||||
st.subheader("Ispezione genoma")
|
|
||||||
gid = st.selectbox("Seleziona genome_id", top["genome_id"].tolist())
|
|
||||||
row = merged[merged["genome_id"] == gid].iloc[0]
|
|
||||||
|
|
||||||
col1, col2 = st.columns(2)
|
|
||||||
with col1:
|
|
||||||
st.metric("fitness", f"{row['fitness']:.3f}")
|
|
||||||
st.metric("DSR", f"{row['dsr']:.3f}")
|
|
||||||
st.metric("Sharpe", f"{row['sharpe']:.3f}")
|
|
||||||
with col2:
|
|
||||||
st.metric("max DD", f"{row['max_dd']:.3f}")
|
|
||||||
st.metric("trades", int(row["n_trades"]))
|
|
||||||
st.metric("style", str(row.get("cognitive_style", "—")))
|
|
||||||
|
|
||||||
st.subheader("System prompt")
|
|
||||||
st.code(row.get("system_prompt", "—"))
|
|
||||||
|
|
||||||
st.subheader("Raw LLM output")
|
|
||||||
st.code(row.get("raw_text", "—"))
|
|
||||||
|
|
||||||
if row.get("parse_error"):
|
|
||||||
st.error(f"Parse error: {row['parse_error']}")
|
|
||||||
@@ -1,87 +0,0 @@
|
|||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
import streamlit as st
|
|
||||||
import streamlit.components.v1 as components
|
|
||||||
|
|
||||||
from multi_swarm.dashboard.aquarium import (
|
|
||||||
STYLE_COLORS,
|
|
||||||
build_aquarium_html,
|
|
||||||
build_fish_dataset,
|
|
||||||
build_lineage_index,
|
|
||||||
)
|
|
||||||
from multi_swarm.dashboard.data import (
|
|
||||||
evaluations_df,
|
|
||||||
genomes_df,
|
|
||||||
get_repo,
|
|
||||||
list_runs_df,
|
|
||||||
)
|
|
||||||
|
|
||||||
st.title("Aquarium 2D")
|
|
||||||
st.caption(
|
|
||||||
"Pesci colorati per stile cognitivo, dimensione proporzionale a fitness. "
|
|
||||||
"Click su un pesce per dettaglio + discendenza."
|
|
||||||
)
|
|
||||||
|
|
||||||
db_path = st.session_state.get("db_path", "./runs.db")
|
|
||||||
repo = get_repo(db_path)
|
|
||||||
|
|
||||||
runs = list_runs_df(repo)
|
|
||||||
if runs.empty:
|
|
||||||
st.info("Nessuna run nel database.")
|
|
||||||
st.stop()
|
|
||||||
|
|
||||||
selected_run = st.selectbox("Run", runs["id"].tolist())
|
|
||||||
|
|
||||||
# Fetch ALL genomes of the run (no gen filter): needed to build the lineage
|
|
||||||
# index across generations. The active set is filtered afterwards.
|
|
||||||
all_genomes = genomes_df(repo, selected_run)
|
|
||||||
all_evals = evaluations_df(repo, selected_run)
|
|
||||||
|
|
||||||
if all_genomes.empty:
|
|
||||||
st.warning("Nessun genoma per questa run.")
|
|
||||||
st.stop()
|
|
||||||
|
|
||||||
available_gens = sorted(all_genomes["generation_idx"].unique().tolist())
|
|
||||||
selected_gen = st.selectbox(
|
|
||||||
"Generazione",
|
|
||||||
available_gens,
|
|
||||||
index=len(available_gens) - 1, # default ultima
|
|
||||||
)
|
|
||||||
|
|
||||||
active_genomes = all_genomes[all_genomes["generation_idx"] == selected_gen]
|
|
||||||
active_evals = (
|
|
||||||
all_evals[all_evals["genome_id"].isin(active_genomes["id"])]
|
|
||||||
if not all_evals.empty
|
|
||||||
else all_evals
|
|
||||||
)
|
|
||||||
if not active_evals.empty:
|
|
||||||
active_merged = active_genomes.merge(
|
|
||||||
active_evals,
|
|
||||||
left_on="id",
|
|
||||||
right_on="genome_id",
|
|
||||||
how="left",
|
|
||||||
suffixes=("", "_eval"),
|
|
||||||
)
|
|
||||||
else:
|
|
||||||
active_merged = active_genomes.copy()
|
|
||||||
active_merged["genome_id"] = active_merged["id"]
|
|
||||||
|
|
||||||
lineage = build_lineage_index(all_genomes, all_evals)
|
|
||||||
fish = build_fish_dataset(active_merged, lineage, max_lineage_levels=5)
|
|
||||||
|
|
||||||
if not fish:
|
|
||||||
st.warning("Nessun agente attivo in questa generazione.")
|
|
||||||
st.stop()
|
|
||||||
|
|
||||||
st.caption(f"{len(fish)} agenti in generazione {selected_gen}")
|
|
||||||
|
|
||||||
html_str = build_aquarium_html(fish, canvas_w=1000, canvas_h=600)
|
|
||||||
components.html(html_str, height=620, scrolling=False)
|
|
||||||
|
|
||||||
with st.expander("Legenda colori"):
|
|
||||||
legend_md = "\n".join(
|
|
||||||
f"- <span style='color:{color};font-weight:bold;'>●</span> "
|
|
||||||
f"`{style}`"
|
|
||||||
for style, color in STYLE_COLORS.items()
|
|
||||||
)
|
|
||||||
st.markdown(legend_md, unsafe_allow_html=True)
|
|
||||||
@@ -1,22 +0,0 @@
|
|||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
import os
|
|
||||||
from pathlib import Path
|
|
||||||
|
|
||||||
import streamlit as st
|
|
||||||
|
|
||||||
st.set_page_config(page_title="Multi-Swarm Phase 1", layout="wide")
|
|
||||||
st.title("Multi-Swarm Coevolutivo — Phase 1 dashboard")
|
|
||||||
st.markdown(
|
|
||||||
"""
|
|
||||||
Naviga le pagine nel menu a sinistra:
|
|
||||||
- **Overview**: ultima run e stato globale.
|
|
||||||
- **GA Convergence**: fitness per generazione.
|
|
||||||
- **Genomes**: top-K genomi e ispezione qualitativa.
|
|
||||||
- **Aquarium**: visualizzazione 2D dei genomi come pesci animati.
|
|
||||||
"""
|
|
||||||
)
|
|
||||||
|
|
||||||
db_path = os.environ.get("DB_PATH", "./runs.db")
|
|
||||||
st.session_state["db_path"] = db_path
|
|
||||||
st.caption(f"DB path: `{Path(db_path).resolve()}`")
|
|
||||||
@@ -1,310 +0,0 @@
|
|||||||
import importlib
|
|
||||||
|
|
||||||
import pandas as pd
|
|
||||||
|
|
||||||
|
|
||||||
def test_streamlit_app_imports():
|
|
||||||
importlib.import_module("multi_swarm.dashboard.data")
|
|
||||||
|
|
||||||
|
|
||||||
def test_dashboard_data_helpers_signatures():
|
|
||||||
from multi_swarm.dashboard import data
|
|
||||||
|
|
||||||
assert hasattr(data, "list_runs_df")
|
|
||||||
assert hasattr(data, "generations_df")
|
|
||||||
assert hasattr(data, "evaluations_df")
|
|
||||||
assert hasattr(data, "genomes_df")
|
|
||||||
|
|
||||||
|
|
||||||
def test_aquarium_helper_builds_html_with_click_handler():
|
|
||||||
from multi_swarm.dashboard.aquarium import build_aquarium_html
|
|
||||||
|
|
||||||
fish = [
|
|
||||||
{
|
|
||||||
"id": "abc123",
|
|
||||||
"fitness": 0.8,
|
|
||||||
"cognitive_style": "physicist",
|
|
||||||
"n_trades": 30,
|
|
||||||
"dsr": 0.7,
|
|
||||||
"sharpe": 1.2,
|
|
||||||
"max_dd": 0.1,
|
|
||||||
"system_prompt": "test",
|
|
||||||
"temperature": 0.9,
|
|
||||||
"lookback_window": 200,
|
|
||||||
"feature_access": ["close"],
|
|
||||||
"model_tier": "C",
|
|
||||||
"generation": 1,
|
|
||||||
"parent_ids": [],
|
|
||||||
"ancestors": [],
|
|
||||||
}
|
|
||||||
]
|
|
||||||
html = build_aquarium_html(fish, canvas_w=800, canvas_h=400)
|
|
||||||
assert "canvas" in html
|
|
||||||
assert "abc123" in html # fish id present in JSON payload
|
|
||||||
assert "addEventListener('click'" in html
|
|
||||||
assert "fish-info-panel" in html
|
|
||||||
assert "showFishInfo" in html
|
|
||||||
assert "Discendenza" in html
|
|
||||||
assert "requestAnimationFrame" in html
|
|
||||||
|
|
||||||
|
|
||||||
def test_aquarium_build_fish_dataset_legacy_path():
|
|
||||||
from multi_swarm.dashboard.aquarium import build_fish_dataset
|
|
||||||
|
|
||||||
df = pd.DataFrame(
|
|
||||||
[
|
|
||||||
{
|
|
||||||
"genome_id": "low",
|
|
||||||
"fitness": 0.1,
|
|
||||||
"cognitive_style": "physicist",
|
|
||||||
"n_trades": 1,
|
|
||||||
"dsr": 0.0,
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"genome_id": "high",
|
|
||||||
"fitness": 0.9,
|
|
||||||
"cognitive_style": "biologist",
|
|
||||||
"n_trades": 10,
|
|
||||||
"dsr": 0.5,
|
|
||||||
},
|
|
||||||
]
|
|
||||||
)
|
|
||||||
out = build_fish_dataset(df)
|
|
||||||
ids = {f["id"] for f in out}
|
|
||||||
assert ids == {"low", "high"}
|
|
||||||
high = next(f for f in out if f["id"] == "high")
|
|
||||||
assert high["cognitive_style"] == "biologist"
|
|
||||||
assert high["ancestors"] == []
|
|
||||||
|
|
||||||
|
|
||||||
def test_aquarium_build_fish_dataset_drops_nan_fitness():
|
|
||||||
from multi_swarm.dashboard.aquarium import build_fish_dataset
|
|
||||||
|
|
||||||
df = pd.DataFrame(
|
|
||||||
[
|
|
||||||
{
|
|
||||||
"genome_id": "ok",
|
|
||||||
"fitness": 0.4,
|
|
||||||
"cognitive_style": "historian",
|
|
||||||
"n_trades": 2,
|
|
||||||
"dsr": 0.1,
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"genome_id": "bad",
|
|
||||||
"fitness": float("nan"),
|
|
||||||
"cognitive_style": "ecologist",
|
|
||||||
"n_trades": 0,
|
|
||||||
"dsr": 0.0,
|
|
||||||
},
|
|
||||||
]
|
|
||||||
)
|
|
||||||
out = build_fish_dataset(df)
|
|
||||||
assert len(out) == 1
|
|
||||||
assert out[0]["id"] == "ok"
|
|
||||||
|
|
||||||
|
|
||||||
def test_aquarium_empty_input_returns_empty():
|
|
||||||
from multi_swarm.dashboard.aquarium import build_aquarium_html, build_fish_dataset
|
|
||||||
|
|
||||||
assert build_fish_dataset(pd.DataFrame()) == []
|
|
||||||
html = build_aquarium_html([], canvas_w=400, canvas_h=200)
|
|
||||||
assert "canvas" in html
|
|
||||||
assert "Acquario vuoto" in html
|
|
||||||
|
|
||||||
|
|
||||||
def test_build_lineage_index_returns_dict_keyed_by_id():
|
|
||||||
from multi_swarm.dashboard.aquarium import build_lineage_index
|
|
||||||
|
|
||||||
genomes = pd.DataFrame(
|
|
||||||
[
|
|
||||||
{
|
|
||||||
"id": "g1",
|
|
||||||
"generation_idx": 0,
|
|
||||||
"generation": 0,
|
|
||||||
"system_prompt": "x",
|
|
||||||
"feature_access": ["close"],
|
|
||||||
"temperature": 0.9,
|
|
||||||
"top_p": 0.95,
|
|
||||||
"model_tier": "C",
|
|
||||||
"lookback_window": 100,
|
|
||||||
"cognitive_style": "physicist",
|
|
||||||
"parent_ids": [],
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"id": "g2",
|
|
||||||
"generation_idx": 1,
|
|
||||||
"generation": 1,
|
|
||||||
"system_prompt": "y",
|
|
||||||
"feature_access": ["close", "volume"],
|
|
||||||
"temperature": 1.0,
|
|
||||||
"top_p": 0.95,
|
|
||||||
"model_tier": "C",
|
|
||||||
"lookback_window": 200,
|
|
||||||
"cognitive_style": "biologist",
|
|
||||||
"parent_ids": ["g1"],
|
|
||||||
},
|
|
||||||
]
|
|
||||||
)
|
|
||||||
evals = pd.DataFrame(
|
|
||||||
[
|
|
||||||
{
|
|
||||||
"genome_id": "g1",
|
|
||||||
"fitness": 0.5,
|
|
||||||
"dsr": 0.6,
|
|
||||||
"sharpe": 1.2,
|
|
||||||
"max_dd": 0.1,
|
|
||||||
"n_trades": 30,
|
|
||||||
"parse_error": None,
|
|
||||||
"raw_text": "",
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"genome_id": "g2",
|
|
||||||
"fitness": 0.7,
|
|
||||||
"dsr": 0.8,
|
|
||||||
"sharpe": 1.5,
|
|
||||||
"max_dd": 0.05,
|
|
||||||
"n_trades": 40,
|
|
||||||
"parse_error": None,
|
|
||||||
"raw_text": "",
|
|
||||||
},
|
|
||||||
]
|
|
||||||
)
|
|
||||||
idx = build_lineage_index(genomes, evals)
|
|
||||||
assert "g1" in idx and "g2" in idx
|
|
||||||
assert idx["g2"]["parent_ids"] == ["g1"]
|
|
||||||
assert idx["g2"]["fitness"] == 0.7
|
|
||||||
assert idx["g1"]["cognitive_style"] == "physicist"
|
|
||||||
assert idx["g2"]["feature_access"] == ["close", "volume"]
|
|
||||||
|
|
||||||
|
|
||||||
def test_trace_ancestors_walks_levels():
|
|
||||||
from multi_swarm.dashboard.aquarium import trace_ancestors
|
|
||||||
|
|
||||||
idx = {
|
|
||||||
"child": {
|
|
||||||
"id": "child",
|
|
||||||
"parent_ids": ["p1", "p2"],
|
|
||||||
"fitness": 0.8,
|
|
||||||
"generation": 2,
|
|
||||||
"cognitive_style": "physicist",
|
|
||||||
},
|
|
||||||
"p1": {
|
|
||||||
"id": "p1",
|
|
||||||
"parent_ids": ["gp1"],
|
|
||||||
"fitness": 0.5,
|
|
||||||
"generation": 1,
|
|
||||||
"cognitive_style": "biologist",
|
|
||||||
},
|
|
||||||
"p2": {
|
|
||||||
"id": "p2",
|
|
||||||
"parent_ids": [],
|
|
||||||
"fitness": 0.3,
|
|
||||||
"generation": 1,
|
|
||||||
"cognitive_style": "engineer",
|
|
||||||
},
|
|
||||||
"gp1": {
|
|
||||||
"id": "gp1",
|
|
||||||
"parent_ids": [],
|
|
||||||
"fitness": 0.2,
|
|
||||||
"generation": 0,
|
|
||||||
"cognitive_style": "historian",
|
|
||||||
},
|
|
||||||
}
|
|
||||||
levels = trace_ancestors("child", idx, max_levels=5)
|
|
||||||
assert len(levels) == 2
|
|
||||||
assert {a["id"] for a in levels[0]} == {"p1", "p2"}
|
|
||||||
assert {a["id"] for a in levels[1]} == {"gp1"}
|
|
||||||
|
|
||||||
|
|
||||||
def test_trace_ancestors_handles_cycles():
|
|
||||||
from multi_swarm.dashboard.aquarium import trace_ancestors
|
|
||||||
|
|
||||||
# Pathological cycle: a <-> b. Should terminate cleanly.
|
|
||||||
idx = {
|
|
||||||
"a": {
|
|
||||||
"id": "a",
|
|
||||||
"parent_ids": ["b"],
|
|
||||||
"fitness": 0.1,
|
|
||||||
"generation": 1,
|
|
||||||
"cognitive_style": "physicist",
|
|
||||||
},
|
|
||||||
"b": {
|
|
||||||
"id": "b",
|
|
||||||
"parent_ids": ["a"],
|
|
||||||
"fitness": 0.2,
|
|
||||||
"generation": 0,
|
|
||||||
"cognitive_style": "biologist",
|
|
||||||
},
|
|
||||||
}
|
|
||||||
levels = trace_ancestors("a", idx, max_levels=5)
|
|
||||||
# a -> b at level 0; b's only parent is a, already seen -> stop.
|
|
||||||
assert len(levels) == 1
|
|
||||||
assert levels[0][0]["id"] == "b"
|
|
||||||
|
|
||||||
|
|
||||||
def test_trace_ancestors_no_parents_returns_empty():
|
|
||||||
from multi_swarm.dashboard.aquarium import trace_ancestors
|
|
||||||
|
|
||||||
idx = {
|
|
||||||
"solo": {
|
|
||||||
"id": "solo",
|
|
||||||
"parent_ids": [],
|
|
||||||
"fitness": 0.4,
|
|
||||||
"generation": 0,
|
|
||||||
"cognitive_style": "engineer",
|
|
||||||
},
|
|
||||||
}
|
|
||||||
assert trace_ancestors("solo", idx) == []
|
|
||||||
|
|
||||||
|
|
||||||
def test_build_fish_dataset_attaches_ancestors():
|
|
||||||
from multi_swarm.dashboard.aquarium import build_fish_dataset, build_lineage_index
|
|
||||||
|
|
||||||
genomes = pd.DataFrame(
|
|
||||||
[
|
|
||||||
{
|
|
||||||
"id": "p",
|
|
||||||
"generation_idx": 0,
|
|
||||||
"generation": 0,
|
|
||||||
"system_prompt": "p",
|
|
||||||
"feature_access": ["close"],
|
|
||||||
"temperature": 0.8,
|
|
||||||
"top_p": 0.9,
|
|
||||||
"model_tier": "C",
|
|
||||||
"lookback_window": 100,
|
|
||||||
"cognitive_style": "physicist",
|
|
||||||
"parent_ids": [],
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"id": "c",
|
|
||||||
"generation_idx": 1,
|
|
||||||
"generation": 1,
|
|
||||||
"system_prompt": "c",
|
|
||||||
"feature_access": ["close"],
|
|
||||||
"temperature": 0.8,
|
|
||||||
"top_p": 0.9,
|
|
||||||
"model_tier": "C",
|
|
||||||
"lookback_window": 120,
|
|
||||||
"cognitive_style": "biologist",
|
|
||||||
"parent_ids": ["p"],
|
|
||||||
},
|
|
||||||
]
|
|
||||||
)
|
|
||||||
evals = pd.DataFrame(
|
|
||||||
[
|
|
||||||
{"genome_id": "p", "fitness": 0.3, "dsr": 0.0, "sharpe": 0.0,
|
|
||||||
"max_dd": 0.0, "n_trades": 0},
|
|
||||||
{"genome_id": "c", "fitness": 0.6, "dsr": 0.0, "sharpe": 0.0,
|
|
||||||
"max_dd": 0.0, "n_trades": 0},
|
|
||||||
]
|
|
||||||
)
|
|
||||||
lineage = build_lineage_index(genomes, evals)
|
|
||||||
|
|
||||||
active = genomes[genomes["generation_idx"] == 1].merge(
|
|
||||||
evals, left_on="id", right_on="genome_id", how="left"
|
|
||||||
)
|
|
||||||
fish = build_fish_dataset(active, lineage)
|
|
||||||
assert len(fish) == 1
|
|
||||||
assert fish[0]["id"] == "c"
|
|
||||||
assert len(fish[0]["ancestors"]) == 1
|
|
||||||
assert fish[0]["ancestors"][0][0]["id"] == "p"
|
|
||||||
@@ -111,22 +111,6 @@ wheels = [
|
|||||||
{ url = "https://files.pythonhosted.org/packages/fb/76/641ae371508676492379f16e2fa48f4e2c11741bd63c48be4b12a6b09cba/aiosignal-1.4.0-py3-none-any.whl", hash = "sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e", size = 7490, upload-time = "2025-07-03T22:54:42.156Z" },
|
{ url = "https://files.pythonhosted.org/packages/fb/76/641ae371508676492379f16e2fa48f4e2c11741bd63c48be4b12a6b09cba/aiosignal-1.4.0-py3-none-any.whl", hash = "sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e", size = 7490, upload-time = "2025-07-03T22:54:42.156Z" },
|
||||||
]
|
]
|
||||||
|
|
||||||
[[package]]
|
|
||||||
name = "altair"
|
|
||||||
version = "6.1.0"
|
|
||||||
source = { registry = "https://pypi.org/simple" }
|
|
||||||
dependencies = [
|
|
||||||
{ name = "jinja2" },
|
|
||||||
{ name = "jsonschema" },
|
|
||||||
{ name = "narwhals" },
|
|
||||||
{ name = "packaging" },
|
|
||||||
{ name = "typing-extensions", marker = "python_full_version < '3.15'" },
|
|
||||||
]
|
|
||||||
sdist = { url = "https://files.pythonhosted.org/packages/3a/1e/365a9144db3254f86f1b974660b9ede1e9a38c9dc0730e4a9b1192eec5d6/altair-6.1.0.tar.gz", hash = "sha256:dda699216cf85b040d968ae5a569ad45957616811e38760a85e5118269daca67", size = 765519, upload-time = "2026-04-21T13:08:46.44Z" }
|
|
||||||
wheels = [
|
|
||||||
{ url = "https://files.pythonhosted.org/packages/ce/63/5dacc8d8306c715088b897a479e551bc0779fd2f0f26c97fec5e36542b4e/altair-6.1.0-py3-none-any.whl", hash = "sha256:fdf5fd939512e5b2fc4441c82dfd2635e706defbd037db0ac429ef5ddce66c3b", size = 796996, upload-time = "2026-04-21T13:08:48.549Z" },
|
|
||||||
]
|
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "annotated-doc"
|
name = "annotated-doc"
|
||||||
version = "0.0.4"
|
version = "0.0.4"
|
||||||
@@ -226,24 +210,6 @@ wheels = [
|
|||||||
{ url = "https://files.pythonhosted.org/packages/99/37/e8730c3587a65eb5645d4aba2d27aae48e8003614d6aaf15dda67f702f1f/bidict-0.23.1-py3-none-any.whl", hash = "sha256:5dae8d4d79b552a71cbabc7deb25dfe8ce710b17ff41711e13010ead2abfc3e5", size = 32764, upload-time = "2024-02-18T19:09:04.156Z" },
|
{ url = "https://files.pythonhosted.org/packages/99/37/e8730c3587a65eb5645d4aba2d27aae48e8003614d6aaf15dda67f702f1f/bidict-0.23.1-py3-none-any.whl", hash = "sha256:5dae8d4d79b552a71cbabc7deb25dfe8ce710b17ff41711e13010ead2abfc3e5", size = 32764, upload-time = "2024-02-18T19:09:04.156Z" },
|
||||||
]
|
]
|
||||||
|
|
||||||
[[package]]
|
|
||||||
name = "blinker"
|
|
||||||
version = "1.9.0"
|
|
||||||
source = { registry = "https://pypi.org/simple" }
|
|
||||||
sdist = { url = "https://files.pythonhosted.org/packages/21/28/9b3f50ce0e048515135495f198351908d99540d69bfdc8c1d15b73dc55ce/blinker-1.9.0.tar.gz", hash = "sha256:b4ce2265a7abece45e7cc896e98dbebe6cead56bcf805a3d23136d145f5445bf", size = 22460, upload-time = "2024-11-08T17:25:47.436Z" }
|
|
||||||
wheels = [
|
|
||||||
{ url = "https://files.pythonhosted.org/packages/10/cb/f2ad4230dc2eb1a74edf38f1a38b9b52277f75bef262d8908e60d957e13c/blinker-1.9.0-py3-none-any.whl", hash = "sha256:ba0efaa9080b619ff2f3459d1d500c57bddea4a6b424b60a91141db6fd2f08bc", size = 8458, upload-time = "2024-11-08T17:25:46.184Z" },
|
|
||||||
]
|
|
||||||
|
|
||||||
[[package]]
|
|
||||||
name = "cachetools"
|
|
||||||
version = "7.1.1"
|
|
||||||
source = { registry = "https://pypi.org/simple" }
|
|
||||||
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|
||||||
{ url = "https://files.pythonhosted.org/packages/07/f6/d0e5b343768e8bcb4cda79f0f2f55051bf26177ecd5651f84c07567461cf/watchdog-6.0.0-py3-none-win32.whl", hash = "sha256:07df1fdd701c5d4c8e55ef6cf55b8f0120fe1aef7ef39a1c6fc6bc2e606d517a", size = 79065, upload-time = "2024-11-01T14:07:09.525Z" },
|
|
||||||
{ url = "https://files.pythonhosted.org/packages/db/d9/c495884c6e548fce18a8f40568ff120bc3a4b7b99813081c8ac0c936fa64/watchdog-6.0.0-py3-none-win_amd64.whl", hash = "sha256:cbafb470cf848d93b5d013e2ecb245d4aa1c8fd0504e863ccefa32445359d680", size = 79070, upload-time = "2024-11-01T14:07:10.686Z" },
|
|
||||||
{ url = "https://files.pythonhosted.org/packages/33/e8/e40370e6d74ddba47f002a32919d91310d6074130fe4e17dabcafc15cbf1/watchdog-6.0.0-py3-none-win_ia64.whl", hash = "sha256:a1914259fa9e1454315171103c6a30961236f508b9b623eae470268bbcc6a22f", size = 79067, upload-time = "2024-11-01T14:07:11.845Z" },
|
|
||||||
]
|
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "watchfiles"
|
name = "watchfiles"
|
||||||
version = "1.1.1"
|
version = "1.1.1"
|
||||||
|
|||||||
Reference in New Issue
Block a user