wehub-resource-sync
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591 lines
17 KiB
Python
591 lines
17 KiB
Python
"""Data explorer — a FastMCPApp example with tables, charts, and filtering.
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Demonstrates the full FastMCPApp stack:
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- @app.ui() entry point with a tabbed data exploration interface
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- @app.tool() backend tools for analysis, summaries, and filtering
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- DataTable with sorting, search, and pagination
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- BarChart and PieChart for data visualization
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- Metric cards for summary statistics
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- Select-driven filtering with CallTool
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- State management with PrefabApp state dict and Rx()
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Usage:
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uv run python explorer_server.py # HTTP (default)
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uv run python explorer_server.py --stdio # stdio for MCP clients
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"""
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from __future__ import annotations
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from prefab_ui.actions import SetState, ShowToast
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from prefab_ui.actions.mcp import CallTool
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from prefab_ui.app import PrefabApp
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from prefab_ui.components import (
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Badge,
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Button,
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Card,
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CardContent,
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Column,
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DataTable,
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DataTableColumn,
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Grid,
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Heading,
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Metric,
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Muted,
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Row,
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Select,
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SelectOption,
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Separator,
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Tab,
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Tabs,
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Text,
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)
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from prefab_ui.components.charts import BarChart, ChartSeries, PieChart
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from prefab_ui.rx import ERROR, RESULT, STATE, Rx
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from fastmcp import FastMCP, FastMCPApp
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# ---------------------------------------------------------------------------
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# Sample data
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# ---------------------------------------------------------------------------
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SALES_DATA: list[dict] = [
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{
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"date": "2025-01-05",
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"product": "Widget A",
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"region": "North",
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"amount": 1200,
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"quantity": 10,
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},
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{
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"date": "2025-01-12",
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"product": "Widget B",
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"region": "South",
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"amount": 850,
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"quantity": 7,
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},
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{
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"date": "2025-01-18",
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"product": "Gadget X",
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"region": "East",
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"amount": 2300,
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"quantity": 15,
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},
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{
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"date": "2025-01-25",
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"product": "Gadget Y",
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"region": "West",
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"amount": 1750,
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"quantity": 12,
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},
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{
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"date": "2025-02-02",
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"product": "Widget A",
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"region": "East",
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"amount": 1400,
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"quantity": 11,
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},
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{
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"date": "2025-02-09",
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"product": "Widget B",
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"region": "North",
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"amount": 920,
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"quantity": 8,
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},
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{
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"date": "2025-02-15",
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"product": "Gadget X",
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"region": "South",
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"amount": 2100,
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"quantity": 14,
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},
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{
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"date": "2025-02-22",
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"product": "Gadget Y",
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"region": "West",
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"amount": 1600,
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"quantity": 11,
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},
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{
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"date": "2025-03-01",
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"product": "Widget A",
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"region": "South",
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"amount": 1350,
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"quantity": 10,
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},
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{
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"date": "2025-03-08",
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"product": "Widget B",
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"region": "West",
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"amount": 780,
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"quantity": 6,
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},
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{
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"date": "2025-03-14",
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"product": "Gadget X",
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"region": "North",
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"amount": 2500,
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"quantity": 17,
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},
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{
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"date": "2025-03-21",
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"product": "Gadget Y",
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"region": "East",
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"amount": 1900,
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"quantity": 13,
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},
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{
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"date": "2025-04-03",
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"product": "Widget A",
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"region": "West",
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"amount": 1100,
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"quantity": 9,
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},
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{
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"date": "2025-04-10",
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"product": "Widget B",
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"region": "East",
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"amount": 960,
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"quantity": 8,
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},
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{
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"date": "2025-04-17",
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"product": "Gadget X",
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"region": "South",
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"amount": 2400,
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"quantity": 16,
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},
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{
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"date": "2025-04-24",
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"product": "Gadget Y",
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"region": "North",
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"amount": 1850,
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"quantity": 12,
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},
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{
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"date": "2025-05-01",
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"product": "Widget A",
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"region": "North",
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"amount": 1500,
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"quantity": 12,
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},
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{
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"date": "2025-05-08",
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"product": "Widget B",
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"region": "South",
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"amount": 890,
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"quantity": 7,
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},
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{
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"date": "2025-05-15",
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"product": "Gadget X",
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"region": "West",
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"amount": 2200,
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"quantity": 15,
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},
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{
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"date": "2025-05-22",
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"product": "Gadget Y",
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"region": "East",
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"amount": 1700,
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"quantity": 11,
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},
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{
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"date": "2025-06-05",
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"product": "Widget A",
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"region": "East",
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"amount": 1300,
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"quantity": 10,
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},
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{
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"date": "2025-06-12",
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"product": "Widget B",
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"region": "North",
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"amount": 1050,
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"quantity": 9,
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},
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{
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"date": "2025-06-19",
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"product": "Gadget X",
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"region": "North",
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"amount": 2600,
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"quantity": 18,
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},
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{
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"date": "2025-06-26",
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"product": "Gadget Y",
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"region": "South",
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"amount": 1650,
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"quantity": 11,
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},
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{
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"date": "2025-07-03",
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"product": "Widget A",
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"region": "South",
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"amount": 1450,
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"quantity": 11,
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},
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{
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"date": "2025-07-10",
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"product": "Widget B",
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"region": "West",
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"amount": 830,
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"quantity": 7,
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},
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{
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"date": "2025-07-17",
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"product": "Gadget X",
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"region": "East",
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"amount": 2350,
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"quantity": 16,
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},
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{
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"date": "2025-07-24",
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"product": "Gadget Y",
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"region": "West",
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"amount": 1800,
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"quantity": 12,
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},
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{
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"date": "2025-08-01",
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"product": "Widget A",
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"region": "West",
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"amount": 1250,
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"quantity": 10,
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},
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{
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"date": "2025-08-08",
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"product": "Widget B",
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"region": "East",
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"amount": 970,
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"quantity": 8,
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},
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{
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"date": "2025-08-15",
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"product": "Gadget X",
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"region": "South",
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"amount": 2450,
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"quantity": 16,
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},
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{
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"date": "2025-08-22",
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"product": "Gadget Y",
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"region": "North",
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"amount": 1950,
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"quantity": 13,
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},
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]
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REGIONS = ["All", "North", "South", "East", "West"]
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PRODUCTS = ["All", "Widget A", "Widget B", "Gadget X", "Gadget Y"]
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# ---------------------------------------------------------------------------
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# Helpers
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# ---------------------------------------------------------------------------
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def _filter_rows(
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rows: list[dict],
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region: str = "All",
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product: str = "All",
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) -> list[dict]:
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filtered = rows
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if region != "All":
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filtered = [r for r in filtered if r["region"] == region]
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if product != "All":
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filtered = [r for r in filtered if r["product"] == product]
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return filtered
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def _compute_summary(rows: list[dict]) -> dict:
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if not rows:
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return {
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"count": 0,
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"total_amount": 0,
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"avg_amount": 0,
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"min_amount": 0,
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"max_amount": 0,
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"total_quantity": 0,
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}
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amounts = [r["amount"] for r in rows]
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return {
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"count": len(rows),
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"total_amount": sum(amounts),
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"avg_amount": round(sum(amounts) / len(amounts)),
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"min_amount": min(amounts),
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"max_amount": max(amounts),
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"total_quantity": sum(r["quantity"] for r in rows),
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}
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def _aggregate_by(rows: list[dict], key: str) -> list[dict]:
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totals: dict[str, int] = {}
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for row in rows:
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label = row[key]
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totals[label] = totals.get(label, 0) + row["amount"]
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return [{key: label, "amount": total} for label, total in sorted(totals.items())]
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# ---------------------------------------------------------------------------
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# App
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# ---------------------------------------------------------------------------
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app = FastMCPApp("Data Explorer")
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@app.tool()
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def analyze_data(region: str = "All", product: str = "All") -> dict:
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"""Filter and analyze sales data. Returns rows, summary, and chart data."""
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filtered = _filter_rows(SALES_DATA, region, product)
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return {
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"rows": filtered,
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"summary": _compute_summary(filtered),
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"by_region": _aggregate_by(filtered, "region"),
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"by_product": _aggregate_by(filtered, "product"),
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}
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@app.tool(model=True)
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def get_summary() -> dict:
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"""Return summary statistics for the full dataset."""
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return _compute_summary(SALES_DATA)
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@app.tool()
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def filter_data(region: str = "All", product: str = "All") -> list[dict]:
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"""Filter sales data by region and/or product."""
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return _filter_rows(SALES_DATA, region, product)
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@app.ui()
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def data_explorer() -> PrefabApp:
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"""Open the data explorer. Browse, filter, and visualize sales data."""
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initial = analyze_data()
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with Column(gap=6, css_class="p-6") as view:
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Heading("Sales Data Explorer")
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Muted(f"{len(SALES_DATA)} records loaded")
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Separator()
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# ----- Filters -----
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with Row(gap=4, align="center"):
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Text("Filters", css_class="font-semibold")
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with Select(
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name="selected_region",
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placeholder="Region",
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value="All",
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on_change=[
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SetState("loading", True),
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CallTool(
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analyze_data,
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arguments={
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"region": STATE.selected_region,
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"product": STATE.selected_product,
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},
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on_success=[
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SetState("rows", RESULT.rows),
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SetState("summary", RESULT.summary),
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SetState("by_region", RESULT.by_region),
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SetState("by_product", RESULT.by_product),
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SetState("loading", False),
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ShowToast("Data updated", variant="success"),
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],
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on_error=[
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SetState("loading", False),
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ShowToast(ERROR, variant="error"),
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],
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),
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],
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):
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for region in REGIONS:
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SelectOption(value=region, label=region)
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with Select(
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name="selected_product",
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placeholder="Product",
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value="All",
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on_change=[
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SetState("loading", True),
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CallTool(
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analyze_data,
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arguments={
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"region": STATE.selected_region,
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"product": STATE.selected_product,
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},
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on_success=[
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SetState("rows", RESULT.rows),
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SetState("summary", RESULT.summary),
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SetState("by_region", RESULT.by_region),
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SetState("by_product", RESULT.by_product),
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SetState("loading", False),
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ShowToast("Data updated", variant="success"),
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],
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on_error=[
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SetState("loading", False),
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ShowToast(ERROR, variant="error"),
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],
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),
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],
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):
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for product in PRODUCTS:
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SelectOption(value=product, label=product)
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Button(
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Rx("loading").then("Loading...", "Reset"),
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disabled=Rx("loading"),
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on_click=[
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SetState("selected_region", "All"),
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SetState("selected_product", "All"),
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SetState("loading", True),
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CallTool(
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analyze_data,
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arguments={"region": "All", "product": "All"},
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on_success=[
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SetState("rows", RESULT.rows),
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SetState("summary", RESULT.summary),
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SetState("by_region", RESULT.by_region),
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SetState("by_product", RESULT.by_product),
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SetState("loading", False),
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],
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on_error=[
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SetState("loading", False),
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ShowToast(ERROR, variant="error"),
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],
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),
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],
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)
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Separator()
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# ----- Tabs -----
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with Tabs():
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# ---- Summary ----
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with Tab("Summary"):
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with Grid(columns=3, gap=4):
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with Card():
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with CardContent():
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Metric(
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label="Total Revenue",
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value=Rx("summary.total_amount"),
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)
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with Card():
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with CardContent():
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Metric(
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label="Average Sale",
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value=Rx("summary.avg_amount"),
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)
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with Card():
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with CardContent():
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Metric(
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label="Total Quantity",
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value=Rx("summary.total_quantity"),
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)
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with Grid(columns=3, gap=4, css_class="mt-4"):
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with Card():
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with CardContent():
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Metric(
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label="Transactions",
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value=Rx("summary.count"),
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)
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with Card():
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with CardContent():
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Metric(
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label="Min Sale",
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value=Rx("summary.min_amount"),
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)
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with Card():
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with CardContent():
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Metric(
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label="Max Sale",
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value=Rx("summary.max_amount"),
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)
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with Row(gap=2, css_class="mt-4"):
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Badge(f"Region: {STATE.selected_region}")
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Badge(f"Product: {STATE.selected_product}")
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# ---- Table ----
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with Tab("Table"):
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DataTable(
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columns=[
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DataTableColumn(key="date", header="Date", sortable=True),
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DataTableColumn(key="product", header="Product", sortable=True),
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DataTableColumn(key="region", header="Region", sortable=True),
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DataTableColumn(
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key="amount", header="Amount ($)", sortable=True
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),
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DataTableColumn(key="quantity", header="Qty", sortable=True),
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],
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rows="{{ rows }}",
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search=True,
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paginated=True,
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page_size=10,
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)
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# ---- Charts ----
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with Tab("Charts"):
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with Grid(columns=2, gap=6):
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with Column(gap=2):
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Heading("Revenue by Region", level=3)
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BarChart(
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data=Rx("by_region"),
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series=[ChartSeries(data_key="amount", label="Revenue")],
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x_axis="region",
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show_legend=True,
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)
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with Column(gap=2):
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Heading("Revenue by Product", level=3)
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BarChart(
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data=Rx("by_product"),
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series=[ChartSeries(data_key="amount", label="Revenue")],
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x_axis="product",
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show_legend=True,
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)
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Separator(css_class="my-4")
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with Grid(columns=2, gap=6):
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with Column(gap=2):
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Heading("Region Breakdown", level=3)
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PieChart(
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data=Rx("by_region"),
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data_key="amount",
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name_key="region",
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show_legend=True,
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inner_radius=60,
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)
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with Column(gap=2):
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Heading("Product Breakdown", level=3)
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PieChart(
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data=Rx("by_product"),
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data_key="amount",
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name_key="product",
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show_legend=True,
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inner_radius=60,
|
|
)
|
|
|
|
return PrefabApp(
|
|
view=view,
|
|
state={
|
|
"rows": initial["rows"],
|
|
"summary": initial["summary"],
|
|
"by_region": initial["by_region"],
|
|
"by_product": initial["by_product"],
|
|
"selected_region": "All",
|
|
"selected_product": "All",
|
|
"loading": False,
|
|
},
|
|
)
|
|
|
|
|
|
mcp = FastMCP("Data Explorer", providers=[app])
|
|
|
|
if __name__ == "__main__":
|
|
mcp.run(transport="http")
|