diff --git a/pyconUS-2026/.gitignore b/pyconUS-2026/.gitignore new file mode 100644 index 0000000..b756d00 --- /dev/null +++ b/pyconUS-2026/.gitignore @@ -0,0 +1,33 @@ +# Python +__pycache__/ +*.py[cod] +*.pyo +.Python +*.egg-info/ +dist/ +build/ +.venv/ +venv/ +.env +.env.* + +# Jupyter +.ipynb_checkpoints/ + +# Node / Slidev +node_modules/ +slides/dist/ + +# Generated at runtime +demo/servers/notes/ + +# macOS +.DS_Store +.AppleDouble +.LSOverride + +# Editors +.vscode/ +.idea/ +*.swp +*.swo diff --git a/pyconUS-2026/demo/ARCHITECTURE.md b/pyconUS-2026/demo/ARCHITECTURE.md new file mode 100644 index 0000000..939ce47 --- /dev/null +++ b/pyconUS-2026/demo/ARCHITECTURE.md @@ -0,0 +1,222 @@ +# Router Demo Architecture + +## Overview + +An AI agent loop running entirely in the browser, driven by Python (PyScript). The agent routes requests to in-browser, local, or remote models, and can call external tools via MCP. + +--- + +## The Four Parts of an Agent + +``` +┌─────────────────────────────────────────────────────────────────┐ +│ AGENT LOOP │ +│ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ │ +│ │ MODEL │───▶│ TOOLS │───▶│ LOOP │───▶│ CONTEXT │──┐ │ +│ └─────────┘ └─────────┘ └─────────┘ └─────────┘ │ │ +│ ▲ │ │ +│ └─────────────────────────────────────────────────────┘ │ +└─────────────────────────────────────────────────────────────────┘ +``` + +| Part | General | In This Demo | +|------|---------|--------------| +| **Model** | Decides what to do next | Local SLM (WebLLM) or Remote API | +| **Tools** | Functions the model can call | MCP servers + Python-native (Pandas) | +| **Loop** | Drives execution: ask → run → repeat | PyScript code in browser | +| **Context** | What the model sees each turn | Messages + tool results + DOM state | + +--- + +## System Architecture + +``` +┌─────────────────────────────────────────────────────────────────┐ +│ BROWSER │ +│ ┌───────────────────────────────────────────────────────────┐ │ +│ │ PyScript (Python Runtime) │ │ +│ │ │ │ +│ │ ┌──────────┐ ┌──────────────────────────────────┐ │ │ +│ │ │ ROUTER │────▶│ MODEL GATEWAY │ │ │ +│ │ │ (Python) │ │ ┌────────────┐ ┌─────────────┐ │ │ │ +│ │ └──────────┘ │ │ LOCAL │ │ REMOTE │ │ │ │ +│ │ │ │ │ (WebLLM) │ │ (API/fetch) │ │ │ │ +│ │ │ │ └────────────┘ └─────────────┘ │ │ │ +│ │ │ └──────────────────────────────────┘ │ │ +│ │ │ │ │ +│ │ ▼ │ │ +│ │ ┌──────────────────────────────────────────────────┐ │ │ +│ │ │ TOOLS │ │ │ +│ │ │ ┌─────────────┐ ┌─────────────┐ ┌──────────┐ │ │ │ +│ │ │ │ MCP Client │ │ Python-Native│ │ DOM │ │ │ │ +│ │ │ │ (HTTP/fetch)│ │ (Pandas) │ │ Access │ │ │ │ +│ │ │ └─────────────┘ └─────────────┘ └──────────┘ │ │ │ +│ │ └──────────────────────────────────────────────────┘ │ │ +│ └───────────────────────────────────────────────────────────┘ │ +│ │ │ +└──────────────────────────────┼───────────────────────────────────┘ + │ + ┌────────────────────┼────────────────────┐ + │ │ │ + ▼ ▼ ▼ + ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ + │ MCP Server │ │ LLM API │ │ WebGPU │ + │ (HTTP) │ │ (Remote) │ │ (Local) │ + └─────────────┘ └─────────────┘ └─────────────┘ +``` + +--- + +## Routing Flow + +``` + User Prompt + │ + ▼ + ┌─────────────┐ + │ ROUTER │ + │ (Python) │ + └─────────────┘ + │ + ┌─────────────┼─────────────┐ + │ │ │ + ▼ ▼ ▼ + ┌────────┐ ┌──────────┐ ┌────────┐ + │ LOCAL │ │ HYBRID │ │ REMOTE │ + │ Path │ │ Path │ │ Path │ + └────────┘ └──────────┘ └────────┘ + │ │ │ + ▼ ▼ ▼ + Local SLM Local Data + Remote Model + responds Remote Model + MCP Tools + frames it +``` + +### Routing Decision Logic + +```python +def route_request(prompt: str) -> str: + # LOCAL: Simple, quick, no external data + # "convert", "format", "calculate", "what is" + + # HYBRID: Local processing + remote reasoning + # "summarize", "analyze", "csv", "data" + + # REMOTE: Complex queries or tool needs + # "search", "find", "save", "look up" +``` + +--- + +## Three Demo Paths + +### Demo 1: CSV Analysis (in-browser) +``` +User: "Analyze sales.csv and write an executive summary" + │ + ▼ +LLM calls analyze_csv tool (Pandas, runs in Pyodide) + │ + ▼ +Tool result: {rows, revenue, top_product, QoQ growth} + │ + ▼ +LLM streams 3-sentence summary +``` + +### Demo 2: Agent + MCP Tools +``` +User: "Find top climate stories and save to notes" + │ + ▼ +LLM calls web_search("climate news") + │ + ▼ +[search results injected into context] + │ + ▼ +LLM calls save_to_file("notes.md", content) + │ (intercepted — File System Access API writes to user's disk) + ▼ +LLM streams final summary +``` + +### Demo 3: Folder Agent +``` +User picks a local folder (agent.yaml + documents/) + │ + ▼ +JS reads folder via File System Access API + │ + ▼ +Python parses agent.yaml, injects documents into system prompt + │ + ▼ +Agent instantiated — greets user, ready for conversation +``` + +--- + +## Component Details + +### Router (`router.py`) +- **Purpose**: Decides which execution path based on prompt analysis +- **Implementation**: Pattern matching on keywords +- **Extensible**: Could use a classifier model for smarter routing + +### Tier Gateway (`tiers.py`) +- **In-browser**: WebLLM running Qwen2.5-1.5B via WebGPU +- **Local**: `fetch` to `localhost:11434/v1/chat/completions` (Ollama / any OpenAI-compatible) +- **Remote**: same SSE path; uses `REMOTE_API_KEY` if set, falls back to mock SSE server on `:8766` +- **Interface**: all three expose `async def stream_chat(messages, tools) → AsyncIterator[Delta]` + +### MCP Client +- **Protocol**: HTTP-based MCP (not the SDK directly) +- **Operations**: `list_tools()`, `call_tool(name, args)` +- **Why HTTP**: MCP SDK uses httpx which doesn't work in Pyodide + +### Tool Types +| Type | Example | Runs Where | +|------|---------|------------| +| MCP tools | web_search, save_to_file | External MCP server (HTTP) | +| PyScript-native | analyze_csv (Pandas) | In-browser (Pyodide) | +| FS bridge | save_to_file intercept | Browser File System Access API | + +--- + +## Data Flow Summary + +``` +┌──────────┐ prompt ┌──────────┐ route ┌──────────┐ +│ User │──────────▶│ Router │──────────▶│ Model │ +└──────────┘ └──────────┘ └──────────┘ + │ + tool_call? + │ + ┌───────────────────────┴──────┐ + │ yes │ no + ▼ ▼ + ┌──────────┐ ┌──────────┐ + │ Tools │ │ Response │ + └──────────┘ └──────────┘ + │ │ + │ result │ + ▼ │ + ┌──────────┐ │ + │ Model │◀────────────────────────┘ + │ (again) │ + └──────────┘ + │ + ▼ + ┌──────────┐ + │ User │ + └──────────┘ +``` + +--- + +## Key Insight + +> **The architecture is the same whether running on a server or in a browser. Only the implementation details change: bundle size, cold start, what each part can talk to.** + +This is what makes browser-based agents interesting — same patterns, different constraints, new capabilities (privacy, offline, no infrastructure). diff --git a/pyconUS-2026/demo/CHANGELOG.md b/pyconUS-2026/demo/CHANGELOG.md new file mode 100644 index 0000000..976015c --- /dev/null +++ b/pyconUS-2026/demo/CHANGELOG.md @@ -0,0 +1,178 @@ +# Demo Build Log + +Chronological record of every phase executed to reach the current demo state. +Each entry lists what was done, what files changed, and any non-obvious decisions made. + +--- + +## Phase 1 — Correctness fixes + +Starting point: a prototype with routing logic and UI scaffolding in place but three hard blockers preventing any demo from running. + +### 1.1 — Fixed WebLLM API call (`tiers.py`) +The original call passed a Python dict as kwargs to `engine.chat.completions.create()`, which doesn't survive Pyodide's JS proxy boundary. Fixed by converting options to a JS object via `to_js(..., dict_converter=js.Object.fromEntries)` and passing it as a single positional argument. Also discovered that JS proxy values (chunk content, finish_reason) must be extracted to plain Python strings **before** any `yield` or `await` — Pyodide destroys borrowed proxies at suspension points. + +### 1.2 — Wired streaming through all demos (`main.py`) +`add_streaming_message` / `update_streaming_message` / `finish_streaming_message` existed in `ui.py` but were never called. Added `_stream_into_bubble()` shared helper in `main.py` and wired it through all three demo handlers and the free-form input handler. + +### 1.3 — Killed dead hardcoded Demo 3 block (`main.py`) +Demo 3 had an `asyncio.sleep` loop with hardcoded "climate story" strings. Replaced with the real agent loop (Phase 4). + +### 1.4 — Replaced MCP server tools (`spike/mcp_test_server/server.py`) +Replaced `get_weather` and `calculate` with `web_search(query, k=3)` (returns plausible climate story objects) and `save_to_file(filename, content)` (writes to `./notes/`). Added `/health` endpoint for the M2 probe. + +--- + +## Phase 2 — Tier abstraction + selector + +### 2.1 — Defined `Delta` and `ToolCall` dataclasses (`streaming.py`) +Single normalized shape for all tier output: `Delta(text, tool_call, finish_reason)` and `ToolCall(id, name, args)`. Shared by all tiers and the agent loop. + +### 2.2 — Implemented three tiers (`tiers.py`) +- `InBrowserTier` — wraps WebLLM engine with `create_proxy` for the progress callback, `to_js` for options, and pre-flight checks for WebGPU + Cache API availability +- `LocalTier` — `fetch` to `localhost:11434/v1/chat/completions`, OpenAI-compatible SSE +- `RemoteTier` — same SSE path; uses `REMOTE_API_KEY` if set, falls back to mock SSE server on `:8766` +- `get_tier(name)` factory function + +### 2.3 — SSE parser (`streaming.py`) +`parse_openai_sse(response)` reads `response.body.getReader()` via JS `TextDecoder`, accumulates tool-call argument fragments across chunks, and yields normalized `Delta` objects. Handles both text streaming and `finish_reason=tool_calls`. + +### 2.4 — Tool-call normalization (`tools.py`) +`normalize_tool_calls(raw, source_tier)` handles shape differences between OpenAI (`{id, function: {name, arguments}}`), Ollama (no `id`), and small in-browser models that produce JSON-in-text. + +### 2.5 — Global tier selector (`index.html`, `ui.py`) +Replaced per-demo dropdowns (original design) with a single 3-button selector at the top of the page. Decision: simpler for live switching on stage, unambiguous for the audience. JS `selectTier()` updates active state and tints demo buttons to match. `ui.py` reads selection via `window.getDemoTier()`. + +### 2.6 — Config reshape (`config.py`) +Removed all `USE_MOCK_*` boolean flags. Replaced with endpoint URLs and model IDs, all readable from env vars. Unavailable tiers surface as errors rather than silent mocks. + +--- + +## Phase 3 — Demo 1 + Demo 2 wiring + +### 3.1 — Demo 1 (`main.py`) +Wired tier selector → `get_tier()` → `_stream_into_bubble()`. Pre-warm (`_prewarm()`) kicks off `_init_browser_engine()` in the background on page load so the in-browser model is ready before the first stage click. + +### 3.2 — Demo 2 (`main.py`, `data/sales.csv`) +Added two-step flow: `fetch('./data/sales.csv')` → Pandas analysis in Pyodide → rendered monospace block labeled IN-BROWSER → framing prompt sent to selected tier → streamed narrative below. Created `data/sales.csv` (156 rows, Q1–Q2 2026, columns: date/product/region/revenue/qty). Added `pandas` to `pyscript.toml` packages (fixing an earlier `a.map is not a function` error caused by wrong TOML syntax). + +### 3.3 — TTFT metric (`metrics.py`, `main.py`) +`start_timer()` / `record_ttft()` helpers call `window.recordTtft(ms)` on first token arrival. JS side updates the metrics strip and the drawer's live section. + +--- + +## Phase 4 — Demo 3 agent loop + File System Access + +### 4.1 — Agent loop (`agent.py`) +Tier-agnostic loop: stream LLM turn → collect tool calls → execute each via `call_tool()` → append assistant + tool-result messages → loop. UI callbacks (`on_delta`, `on_tool_call`, `on_tool_result`, `on_turn_start`, `on_turn_end`) keep the loop decoupled from rendering. `max_turns=8` safety cap. + +### 4.2 — MCP tool client (`tools.py`) +`list_tools()` → `GET /tools`. `call_tool()` intercepts `save_to_file` for the FS bridge before falling through to `POST /call-tool`. `format_tools_for_llm()` converts MCP schemas to OpenAI function-calling format. + +### 4.3 — File System Access bridge (`fs_bridge.py`) +On first `save_to_file` call in a session, shows a modal: "Mount a local folder" or "Download". Mount path uses `window.showDirectoryPicker()` → `FileSystemDirectoryHandle` → `createWritable()` → writes directly to the user's disk. Download path creates a `Blob` and triggers ``. Falls back to download automatically if the API is unavailable (Safari, Firefox). `create_proxy` used for the modal button event listeners to prevent proxy destruction before the choice is read. + +### 4.4 — Demo 3 system prompt (`main.py`) +Added a system message instructing the model to call tools rather than fabricate results — necessary for smaller and mock models to reliably enter the tool-calling path. + +--- + +## Phase 5 — Polish + +### 5.1 — Routing animation (`index.html`, `main.py`, `router.py`) +Added a three-lane strip between demo buttons and metrics bar. CSS `@keyframes lane-sweep` sweeps a colored highlight down the active lane. `window.animateRoute(tier, keyword)` JS function resets all lanes and triggers the animation on the correct one. `router.py` updated to return a `keyword` field on every path. Python wires `animateRoute` calls in all four entry points (Demo 1, Demo 2, Demo 3, free-form input). + +### 5.2 — Metrics strip (`index.html`) +Footer strip shows bundle size (from `performance.getEntriesByType('resource')`), page start time (from navigation timing, cold vs. cached), and last TTFT. Updates after each demo run. + +### 5.3 — Cold-cache toggle (`index.html`) +`↺ Cold reload` button clears all HTTP caches via `caches.keys()` + deletes the WebLLM IndexedDB model cache + reloads. Lets the speaker demonstrate honest cold-start timing on stage. + +### 5.4 — Trade-offs drawer (`index.html`) +Fixed side drawer with comparison table (latency, model quality, tool-call quality, privacy, cost/1k tokens, offline capability, first-load cost, GPU requirement), live measurements section (mirrors metrics strip), and pre-captured snapshot section (loaded from `metrics_snapshot.json`). "When to use each tier" narrative. Toggle button fixed bottom-right. + +### 5.5 — Visual pass (`index.html`) +- Font size bumped to `1.1rem` (readable from back row) +- Active tier buttons get a colored `box-shadow` glow +- Route badges glow while streaming (`:has(.streaming-cursor)` CSS selector removes glow on completion) +- Subtitle updated to match talk title + +--- + +## Phase 6 — Reliability + +### 6.M1 — Selector as fallback +Free with Phase 2. The tier selector is the escape hatch: if in-browser fails on stage, switch to Remote. + +### 6.M2 — Health banner + pre-warm (`index.html`, `main.py`) +JS probes each tier endpoint with `AbortSignal.timeout(1500)` on page load and every 15 s. Tier button dots update to green/yellow/red. MCP probe separately disables Demo 3 button if unreachable. `_prewarm()` in `main.py` checks for WebGPU + Cache API availability before attempting WebLLM init, so it silently no-ops in unsupported environments. + +### 6.M3 — Dropped +Decided not to embed MP4 fallbacks in the demo page. If a backup recording is needed on stage, it will be played externally. + +--- + +## Servers + +### Mock SSE server made prompt-aware (`spike/validation_2_sse/sse_server.py`) +Original server returned a hardcoded generic sentence regardless of input. Rewrote to be prompt-aware: +- Detects tool-capable first turn → emits `finish_reason=tool_calls` SSE with `web_search` + `save_to_file` calls +- Detects tool results in history → streams final narrative summary +- ISO date prompt → `2026-05-05` +- Executive summary prompt → 3-sentence board-slide summary +- Generic fallback for free-form input + +### `run_all.sh` hardened (`demo/servers/run_all.sh`) +Added port cleanup on startup and process-group kill (`kill -9 -- -$$`) on Ctrl+C so no ghost processes linger on ports 8000/8765/8766 between runs. + +--- + +## Phase 7 — Demo restructure + UI polish + +### 7.1 — Demo renumbering and new Demo 3 (`main.py`) +Removed the original Demo 1 (date conversion — not interesting enough for stage). Renumbered: +- Demo 1 = CSV analysis (was Demo 2) — LLM calls `analyze_csv` Pyodide tool, narrates results +- Demo 2 = MCP agent loop (was Demo 3) — LLM calls `web_search` + `save_to_file` over HTTP +- Demo 3 = Folder agent (new) — user picks a local folder with `agent.yaml`, demo instantiates that agent in the browser + +### 7.2 — Folder agent loader (`agent_loader.py`, `index.html`, `pyscript.toml`) +New `agent_loader.py` module: calls `window.readAgentFolder(dirHandle)` JS bridge, uses PyYAML to parse `agent.yaml`, reads files from `documents/` and `tools/mcp.json`, returns a config dict. `readAgentFolder` JS function uses the File System Access API to read the folder contents and returns a plain object (`yaml`, `documentsJson`, `mcpJson`, `error`). Added `pyyaml` to `pyscript.toml` packages. + +System prompt is built by concatenating `system_prompt` from the YAML with any documents injected as markdown sections. A greeting message seeds the conversation so the agent introduces itself immediately. + +### 7.3 — Font size controls (`index.html`) +Added `−` / `+` / display buttons in the footer. Font scaling applies `zoom: var(--font-scale)` to `.chat-container` and `.right-col` only — header, footer, and controls are unaffected. Root `--font-scale` CSS custom property drives the zoom value. `adjustFont(delta)` re-syncs the row-divider position after scale changes to compensate for zoom coordinate offset. + +### 7.4 — Resizable panels (`index.html`) +Main area converted to CSS Grid (`var(--split-chat) 10px var(--split-right)`). Vertical drag handle between chat and right panel: drag resizes, click toggles open/close, double-click resets to 1/1. Horizontal drag handle inside the right column: drag resizes log vs. arch panel, double-click resets to 50%. `snapFocus(mode)` snaps splits to presets: `◀ chat`, `debug ▶`, `⊡ reset`. Row-divider drag coordinates are divided by `_fontScale` to compensate for zoom applied to the parent. + +### 7.5 — Focus mode (`index.html`) +`toggleUiFocus()` toggles `.ui-collapsed` on `body`, which hides header, tier selector, and demo buttons via `display: none !important`. Gives more vertical space to the working demo area during a live run. + +### 7.6 — Orchestrator → Agent rename (`agent.py`, `ui.py`, `index.html`) +All `"orchestrator"` node references replaced with `"agent"` throughout. SVG node updated to `id="arch-agent"`, label "Agent", subtext "PyScript · agent loop". CSS class `.lit-orchestrator` → `.lit-agent`. ARCH_LIT and ARCH_EDGES maps updated. Pyodide SVG label changed to "PyScript tool". + +Fixed a proxy-conversion bug: Python strings passed from PyScript to JS window functions can arrive as proxy objects rather than native JS strings. Added `to_js()` wrapping in `ui.py`'s `agent_activate`, `agent_deactivate`, and `agent_highlight`, and `String()` conversion at the entry of the corresponding JS functions. + +### 7.7 — Documentation consolidation +Merged `DESIGN.md` and `PREFLIGHT.md` into a single `README.md`. Deleted both source files and the two handoff prompt files (`handoff_slides_session.md`, `handoff_spike_session.md`). + +--- + +## Supporting documents + +| File | Purpose | +|---|---| +| `demo/README.md` | Setup, demos, configuration, pre-flight checklist, UI controls reference | +| `demo/ARCHITECTURE.md` | System diagram, agent loop internals, component details | +| `demo/metrics_snapshot.json` | Pre-captured timing numbers for the trade-offs drawer | + +--- + +## Known issues / future work at handoff + +- **WebGPU in incognito** — Chrome disables WebGPU in incognito mode; in-browser tier will fail. Use a normal profile or Safari. +- **Tool-call quality on in-browser model** — the 1.5B Qwen model may produce malformed tool calls. Intentional trade-offs talking point; Demo 1 in-browser mode may not complete the loop cleanly. +- **`metrics_snapshot.json` numbers** — re-capture manually before the talk if the demo machine differs significantly from when they were recorded. +- **Local tier probe** — health probe hits `localhost:11434/health` (Ollama's endpoint). Update `PROBES.local` in `index.html` if using a different local model server. +- **Arch diagram agent highlighting** — PyScript proxy-to-JS string conversion fix applied in Phase 7.6; confirm working after reload. diff --git a/pyconUS-2026/demo/README.md b/pyconUS-2026/demo/README.md new file mode 100644 index 0000000..8bce72f --- /dev/null +++ b/pyconUS-2026/demo/README.md @@ -0,0 +1,208 @@ +# Router Demo — PyCon US 2026 + +**Talk:** *Distributing AI with Python in the Browser: Edge Inference and Flexibility Without Infrastructure* +PyCon US 2026 · 30 min · AI track + +A single-page PyScript app that runs an agentic loop across three inference tiers — in-browser (WebLLM/WebGPU), local (Ollama or any OpenAI-compatible server), and remote (real API or mock SSE server) — and makes the architecture visible to the audience in real time. + +The core talking point: **the agent loop runs entirely in the browser as Python**. The LLM can run anywhere; the loop, tool calls, and UI updates stay local. + +--- + +## Quick start + +```bash +./demo/servers/run_all.sh +# then open http://localhost:8000 +``` + +This starts three servers: + +| Server | Port | Purpose | +|---|---|---| +| Static HTTP | 8000 | Serves `index.html` + all demo assets | +| MCP tools | 8765 | `web_search` + `save_to_file` tools (`demo/servers/mcp_server.py`) | +| Mock remote SSE | 8766 | Prompt-aware streaming stand-in for a real API (`demo/servers/sse_server.py`) | + +To use a real remote API instead of the mock, export before running: + +```bash +export REMOTE_API_URL=https://api.anthropic.com/v1 +export REMOTE_API_KEY=sk-ant-... +./demo/servers/run_all.sh +``` + +--- + +## The three demos + +### Demo 1 — CSV analysis (default tier: in-browser) +LLM calls the `analyze_csv` Pyodide tool (Pandas, runs in-browser), then narrates the results. +No data leaves the device. + +### Demo 2 — Agent + MCP tools (default tier: remote) +LLM calls `web_search` and `save_to_file` over HTTP via an MCP server. +`save_to_file` is intercepted client-side: the browser's File System Access API writes the file directly to the user's disk — the MCP server never touches it. + +### Demo 3 — Folder agent (default tier: remote) +User picks a local folder containing an `agent.yaml` definition. The demo reads the YAML, injects any documents from `documents/`, builds a system prompt, and instantiates that agent in the browser for a live conversation. + +Expected folder structure: +``` +my-agent/ +├── agent.yaml # name, description, system_prompt, ai config +├── documents/ # optional — text files injected into system prompt +│ └── reference.md +└── tools/ + └── mcp.json # optional — informational tool config (future) +``` + +Minimal `agent.yaml`: +```yaml +name: My Agent +description: A short description +system_prompt: You are a helpful assistant specialised in ... +ai: + model: claude-sonnet-4-6 +``` + +--- + +## Configuration + +All endpoints and model IDs live in `config.py` and can be overridden with env vars: + +| Var | Default | Purpose | +|---|---|---| +| `REMOTE_API_URL` | `http://localhost:8766` | Remote completions endpoint | +| `REMOTE_API_KEY` | *(empty)* | Set for a real API; empty → mock SSE | +| `REMOTE_API_MODEL` | `claude-sonnet-4-6` | Model name for remote requests | +| `LOCAL_API_URL` | `http://localhost:11434` | Local model endpoint (Ollama default) | +| `LOCAL_API_MODEL` | `Qwen3-8B` | Model name for local requests | +| `MCP_SERVER_URL` | `http://localhost:8765` | MCP tools server | +| `BROWSER_MODEL_ID` | `Qwen2.5-1.5B-Instruct-q4f16_1-MLC` | WebLLM model (WebGPU) | + +--- + +## Key files + +| File | Purpose | +|---|---| +| `index.html` | All UI + JS (tier selector, health probes, routing animation, arch diagram, font/panel controls) | +| `main.py` | Demo handlers, free-form input, pre-warm | +| `agent.py` | Tier-agnostic agent loop | +| `agent_loader.py` | Parses a local agent folder (agent.yaml + documents) via JS File System Access API | +| `tiers.py` | `InBrowserTier`, `LocalTier`, `RemoteTier` + `get_tier()` factory | +| `streaming.py` | SSE reader, `Delta`, `ToolCall` dataclasses | +| `tools.py` | MCP HTTP client + `save_to_file` intercept + schema helpers | +| `fs_bridge.py` | File System Access API wrapper (folder mount + download fallback) | +| `router.py` | Keyword routing — returns `path`, `reason`, `keyword` for animation | +| `ui.py` | Message rendering, streaming bubbles, tool-call pills, arch diagram bridge | +| `config.py` | Endpoints, model IDs — all readable from env vars | +| `metrics.py` | TTFT timer helpers | +| `pyscript.toml` | Package list (`pandas`, `pyyaml`) + Python file load order | +| `data/sales.csv` | Synthetic 2026 sales data for Demo 1 | +| `metrics_snapshot.json` | Pre-captured bundle/TTFT numbers for the trade-offs drawer | + +--- + +## Pre-flight checklist + +Run through this on the morning of the talk. + +### 1 — Repo state +- [ ] `git status` is clean +- [ ] On the correct branch + +### 2 — Services +```bash +./demo/servers/run_all.sh +``` +- [ ] `curl -s http://localhost:8000/ | head -3` — HTML returned +- [ ] `curl -s http://localhost:8765/health` → `{"status": "ok"}` (MCP) +- [ ] `curl -s http://localhost:8766/health` → `{"status": "ok"}` (mock remote) +- [ ] *(if using local model)* `curl -s http://localhost:11434/health` → 200 + +### 3 — Browser setup +- [ ] Open `http://localhost:8000` in **Chrome** (WebGPU required for in-browser tier; Safari also works) +- [ ] DevTools closed +- [ ] Full-screen (`F11` / `⌘⇧F`) + +### 4 — Health banner +Wait ~3 s after page load, then confirm tier button dots: +- [ ] **In-browser**: green (`WebGPU ready`) — or red/yellow → switch default to Remote +- [ ] **Local**: green if using a local model on stage, otherwise ignore +- [ ] **Remote**: green (`connected`) +- [ ] **Demo 2** button enabled (MCP server reachable) + +### 5 — Demo run-through + +| Demo | Default tier | What to verify | +|---|---|---| +| Demo 1 | In-browser | `analyze_csv` pill appears, then LLM narrative streams | +| Demo 2 | Remote | `web_search` pill → result → `save_to_file` pill → file saved → summary | +| Demo 3 | Remote | Folder picker opens, agent loads, greeting streams | + +- [ ] Routing animation fires on each demo click +- [ ] Arch diagram nodes light up during execution +- [ ] Font `+`/`−` buttons scale only the chat and right panels +- [ ] Vertical / horizontal drag handles resize panels correctly +- [ ] `Focus mode` button hides header and demo controls +- [ ] Trade-offs drawer opens and shows snapshot numbers + +### 6 — WebLLM pre-warm +Demo 1 in-browser downloads ~700 MB the first time. To avoid on stage: +- [ ] Click **Demo 1** with **In-browser** selected before the talk — let the model download fully +- [ ] Confirm status reads `In-browser: Ready` +- [ ] Re-clicking responds in ~200 ms (no download bar) + +Do **not** click `↺ Cold reload` after this step. + +### 7 — Planned tier-switching order +1. Demo 1 → **In-browser** — shows cold-start cost; re-run shows cached speed +2. Demo 1 → **Remote** — immediate response, same Python call +3. Demo 2 → **Remote** — full agent + MCP tool loop +4. Demo 3 → **Remote** — folder agent with injected documents +5. *(optional)* Any demo → **Local** — show quality/speed trade-off on smaller model + +### 8 — Final checks +- [ ] `metrics_snapshot.json` numbers reflect the demo machine (re-capture manually if hardware changed significantly) +- [ ] No console errors before closing DevTools +- [ ] Laptop plugged in (WebGPU inference drains battery) +- [ ] Projector / external display confirmed +- [ ] **Backup plan**: if in-browser fails → select **Remote** and continue + +--- + +## UI controls reference + +| Control | What it does | +|---|---| +| Tier buttons (top) | Select inference tier for all demos | +| `−` / `+` / `1.2×` (footer) | Scale font size in chat + right panels only | +| `Focus mode` (footer) | Hide/show header, tier row, and demo buttons for more vertical space | +| `◀ chat` (footer) | Snap vertical split to expand chat panel | +| `debug ▶` (footer) | Snap vertical split to expand log/arch panel | +| `⊡ reset` (footer) | Reset vertical split to 50/50 | +| Vertical drag handle | Drag to resize chat vs. right panel; click to toggle; double-click to reset | +| Horizontal drag handle | Drag to resize log vs. arch panel inside the right column | +| Step mode toggle | Pause agent loop at each meaningful boundary; `▶ next step` resumes | +| `↺ Cold reload` | Clear all caches (HTTP + WebLLM IndexedDB) and reload | +| `⚖ Trade-offs` | Open the comparison drawer | + +--- + +## Reliability design + +**Tier selector as escape hatch** — if in-browser fails on stage, switch to Remote and continue. No special code needed. + +**Health banner** — JS probes each tier endpoint with a 1.5 s timeout on page load and every 15 s. Tier buttons show green/yellow/red dots. Demo 2 button is disabled if the MCP server is unreachable. + +**No video fallbacks** — if a backup is needed on stage, play it externally from a phone or second device. + +--- + +## Further reading + +- [`ARCHITECTURE.md`](./ARCHITECTURE.md) — system diagram, agent loop internals, component details +- [`CHANGELOG.md`](./CHANGELOG.md) — chronological build log; every phase, decision, and non-obvious fix diff --git a/pyconUS-2026/demo/agent.py b/pyconUS-2026/demo/agent.py new file mode 100644 index 0000000..7ccbe85 --- /dev/null +++ b/pyconUS-2026/demo/agent.py @@ -0,0 +1,252 @@ +"""Tier-agnostic agent loop. + +Runs: stream LLM → collect tool_calls → execute each tool → loop +until finish_reason == "stop". + +The loop owns all diagram state (activate/deactivate) and all streaming +bubble lifecycle. Callers only need to supply: + on_tool_call(tc) — render a tool-call pill + on_tool_result(tc, res) — render the tool result block + +Step mode: + When window.agentStepMode is truthy the loop pauses before every + meaningful boundary and calls window.agentPaused(label) so the UI can + show a "▶ next step" button. window.agentResumeStep() unblocks it. +""" + +import asyncio +import json +from streaming import Delta, ToolCall +from tools import call_tool, PYODIDE_TOOLS +from ui import ( + agent_log, agent_activate, agent_deactivate, + add_streaming_message, update_streaming_message, finish_streaming_message, + add_message, +) + + +# --------------------------------------------------------------------------- +# Step-mode gate +# --------------------------------------------------------------------------- + +_step_gate: asyncio.Event | None = None + + +def _is_step_mode() -> bool: + try: + from pyscript import window + return bool(window.agentStepMode) + except Exception: + return False + + +async def _checkpoint(label: str): + """Pause here when step mode is active; resume when JS calls agentResumeStep().""" + global _step_gate + if not _is_step_mode(): + return + _step_gate = asyncio.Event() + agent_log("step", f"⏸ paused — {label}") + try: + from pyscript import window + window.agentPaused(label) + except Exception: + pass + await _step_gate.wait() + agent_log("step", f"▶ resumed — continuing from: {label}") + + +def resume_step(): + """Called by JS (via PyScript) to unblock the current checkpoint.""" + global _step_gate + if _step_gate and not _step_gate.is_set(): + _step_gate.set() + + +# --------------------------------------------------------------------------- +# Message helpers +# --------------------------------------------------------------------------- + +def _push_context(messages: list): + """Push the current messages[] to the JS Context tab.""" + try: + from pyscript import window + from pyodide.ffi import to_js + window.archUpdateContext(to_js(messages)) + except Exception: + pass + + +def _assistant_with_tool_calls(tool_calls: list[ToolCall]) -> dict: + return { + "role": "assistant", + "content": None, + "tool_calls": [ + { + "id": tc.id, + "type": "function", + "function": {"name": tc.name, "arguments": json.dumps(tc.args)}, + } + for tc in tool_calls + ], + } + + +def _tool_result_message(tc: ToolCall, result_text: str) -> dict: + return { + "role": "tool", + "tool_call_id": tc.id, + "content": result_text, + } + + +# --------------------------------------------------------------------------- +# Agent loop +# --------------------------------------------------------------------------- + +async def run_agent_loop( + tier, + messages: list, + tools: list, + route: str = "", + on_tool_call=None, + on_tool_result=None, + max_turns: int = 8, +): + """Run the agentic loop until stop or max_turns. + + Args: + tier: Tier instance (InBrowserTier / LocalTier / RemoteTier). + messages: Initial message list (mutated in place). + tools: Tool schemas in OpenAI format (empty list = no tools). + route: Tier name string used as the streaming bubble badge. + on_tool_call: Optional callback(tc) — render a tool-call pill. + on_tool_result: Optional callback(tc, result_text) — render result block. + max_turns: Safety cap on loop iterations. + """ + _pyodide_tool_names = {t["name"] for t in PYODIDE_TOOLS} + on_tool_call = on_tool_call or (lambda tc: None) + on_tool_result = on_tool_result or (lambda tc, r: None) + + agent_activate("user", "user-agent") + agent_activate("agent") + turn_num = 0 + _push_context(messages) + + for _ in range(max_turns): + turn_num += 1 + n_msgs = len(messages) + agent_log("agent", f"turn {turn_num} — {n_msgs} message(s) in context, calling {tier.name} LLM") + + # ① Before sending anything to the LLM + await _checkpoint(f"turn {turn_num}: about to send {n_msgs} message(s) to {tier.name} LLM") + + agent_activate("llm", "agent-llm") + agent_log("llm_start", f"LLM turn {turn_num} started ({tier.name})") + bubble = add_streaming_message(route=route) + collected_tool_calls: list[ToolCall] = [] + assistant_text = "" + token_count = 0 + + async for delta in tier.stream_chat(messages, tools): + if delta.text: + token_count += 1 + if token_count == 1: + agent_log("llm_start", f"first token received from {tier.name}") + assistant_text += delta.text + update_streaming_message(bubble, delta.text) + if delta.tool_call: + collected_tool_calls.append(delta.tool_call) + agent_log("tool_call", f"LLM requested: {delta.tool_call.name}({json.dumps(delta.tool_call.args)})") + if delta.finish_reason == "stop": + agent_log("agent", f"turn {turn_num} — LLM finished: stop ({token_count} token(s))") + finish_streaming_message(bubble) + agent_deactivate("llm") + if assistant_text: + messages.append({"role": "assistant", "content": assistant_text}) + _push_context(messages) + # Tools were offered but ignored — model doesn't support function calling + if tools and turn_num == 1 and not collected_tool_calls: + tool_names = ", ".join(t.get("function", t).get("name", "?") for t in tools) + agent_log("error", f"{tier.name} LLM did not call any tools — model may not support function calling") + add_message( + f"⚠️ The {tier.name} model responded without calling " + f"any tools ({tool_names}). This model likely does not " + "support function calling. Try switching to the remote " + "or local tier.", + role="system", + ) + agent_deactivate("agent") + agent_deactivate("user") + return + # ② Normal stop — pause so it can be read before loop exits + await _checkpoint(f"turn {turn_num}: LLM delivered final answer ({token_count} token(s)) — loop will end") + agent_deactivate("agent") + agent_deactivate("user") + return + if delta.finish_reason == "tool_calls": + tool_names = ", ".join(tc.name for tc in collected_tool_calls) + agent_log("agent", f"turn {turn_num} — LLM finished: tool_calls → [{tool_names}]") + break + + finish_streaming_message(bubble) + agent_deactivate("llm") + + if not collected_tool_calls: + agent_log("agent", f"turn {turn_num} — stream ended with no tool calls — treating as final answer") + if assistant_text: + messages.append({"role": "assistant", "content": assistant_text}) + _push_context(messages) + await _checkpoint(f"turn {turn_num}: stream ended (implicit stop, no tool calls) — loop will end") + agent_deactivate("agent") + agent_deactivate("user") + return + + # ③ LLM declared which tools it wants — pause before any tool fires + tool_names = ", ".join(tc.name for tc in collected_tool_calls) + await _checkpoint(f"LLM decided to call {len(collected_tool_calls)} tool(s): {tool_names} — about to dispatch") + + messages.append(_assistant_with_tool_calls(collected_tool_calls)) + _push_context(messages) + + for tc in collected_tool_calls: + agent_log("tool_call", f"dispatching → {tc.name}({json.dumps(tc.args)})") + + if tc.name in _pyodide_tool_names: + agent_activate("pyodide", "agent-pyodide") + else: + agent_activate("mcp", "agent-mcp") + + # ④ Right before each individual tool executes + await _checkpoint(f"about to run: {tc.name}({json.dumps(tc.args)})") + + on_tool_call(tc) + result = await call_tool(tc.name, tc.args) + result_text = " ".join( + item.get("text", "") for item in result.get("content", []) + if item.get("type") == "text" + ) or str(result) + preview = result_text[:120] + ("…" if len(result_text) > 120 else "") + agent_log("tool_result", f"{tc.name} → {preview}") + on_tool_result(tc, result_text) + + if tc.name in _pyodide_tool_names: + agent_deactivate("pyodide") + else: + agent_deactivate("mcp") + + # ⑤ Tool returned — pause so the raw result can be read before it enters context + await _checkpoint(f"✓ {tc.name} returned {len(result_text)} char(s): {preview[:80]} — about to add to context") + + messages.append(_tool_result_message(tc, result_text)) + _push_context(messages) + + n_results = len(collected_tool_calls) + agent_log("agent", f"{n_results} tool result(s) added to context — starting turn {turn_num + 1}") + + # ⑥ All results are in context — pause before handing back to the LLM + await _checkpoint(f"{n_results} tool result(s) now in context — about to call {tier.name} LLM (turn {turn_num + 1})") + + # max_turns reached + agent_deactivate("agent") + agent_deactivate("user") diff --git a/pyconUS-2026/demo/agent_loader.py b/pyconUS-2026/demo/agent_loader.py new file mode 100644 index 0000000..767bb64 --- /dev/null +++ b/pyconUS-2026/demo/agent_loader.py @@ -0,0 +1,80 @@ +"""Load an agent definition from a local folder via File System Access API. + +Expected folder structure: + agent.yaml — name, description, system_prompt, ai config, documents list + documents/ — optional reference documents (text files) + tools/mcp.json — optional MCP server config (informational) + +Returns a parsed config dict ready for use in run_demo_3. +""" + +import json +import yaml +from pyscript import window +from ui import agent_log, add_message + + +async def load_agent_from_folder() -> dict | None: + """Prompt the user to pick an agent folder, parse agent.yaml, return config. + + Returns a dict with keys: + name, description, system_prompt, ai (dict), documents (dict filename→text), + _mcp_config (dict, informational only) + Returns None if the user cancelled or the folder is invalid. + """ + if not hasattr(window, "showDirectoryPicker"): + add_message( + "⚠️ File System Access API is not available in this browser. " + "Use Chrome or Edge to load agent folders.", + role="system", + ) + return None + + try: + dir_handle = await window.showDirectoryPicker() + except Exception: + # User cancelled the picker — not an error + return None + + agent_log("info", "agent folder selected — reading contents…") + + try: + result = await window.readAgentFolder(dir_handle) + except Exception as e: + agent_log("error", f"readAgentFolder failed: {e}") + add_message(f"⚠️ Could not read agent folder: {e}", role="system") + return None + + yaml_text = result.yaml + if not yaml_text: + err = getattr(result, "error", "agent.yaml not found in selected folder") + agent_log("error", f"no agent.yaml: {err}") + add_message(f"⚠️ {err}", role="system") + return None + + try: + config = yaml.safe_load(yaml_text) + except yaml.YAMLError as e: + agent_log("error", f"YAML parse error: {e}") + add_message(f"⚠️ Invalid agent.yaml: {e}", role="system") + return None + + # documents comes back as JSON string (serialized on the JS side) + try: + docs_json = result.documentsJson + config["documents"] = json.loads(docs_json) if docs_json else {} + except Exception: + config["documents"] = {} + + # mcp config — informational only + try: + mcp_json = result.mcpJson + config["_mcp_config"] = json.loads(mcp_json) if mcp_json else {} + except Exception: + config["_mcp_config"] = {} + + name = config.get("name", "Agent") + n_docs = len(config["documents"]) + agent_log("info", f"loaded agent '{name}' — {n_docs} document(s) found") + + return config diff --git a/pyconUS-2026/demo/config.py b/pyconUS-2026/demo/config.py new file mode 100644 index 0000000..f353ce8 --- /dev/null +++ b/pyconUS-2026/demo/config.py @@ -0,0 +1,40 @@ +"""Configuration for the Router Demo. + +DEMO_DEFAULTS sets the pre-selected tier for each demo button. +Switch tiers live using the selector in the UI. +Set REMOTE_API_KEY (or env var REMOTE_API_KEY) to use a real frontier API; +otherwise the demo falls back to the local mock SSE server. +""" + +import os + +# ============================================================================= +# DEMO DEFAULTS — which tier each demo button pre-selects +# ============================================================================= +# "in-browser" | "local" | "remote" +DEMO_DEFAULTS = {1: "in-browser", 2: "remote", 3: "remote"} + +# ============================================================================= +# IN-BROWSER MODEL (WebLLM via WebGPU) +# Qwen2.5-1.5B-Instruct: smallest model in the WebLLM registry with reliable +# function-calling support — needed for Demo 2's analyze_csv tool call. +# ============================================================================= +BROWSER_MODEL_ID = "Qwen2.5-1.5B-Instruct-q4f16_1-MLC" + +# ============================================================================= +# LOCAL SERVER (Ollama / LM Studio / llama.cpp — OpenAI-compatible) +# ============================================================================= +LOCAL_API_URL = os.environ.get("LOCAL_API_URL", "http://localhost:11434") +LOCAL_API_MODEL = os.environ.get("LOCAL_API_MODEL", "Qwen3-8B") + +# ============================================================================= +# REMOTE API (real frontier model, or mock SSE server on :8766) +# ============================================================================= +REMOTE_API_URL = os.environ.get("REMOTE_API_URL", "http://localhost:8766") +REMOTE_API_KEY = os.environ.get("REMOTE_API_KEY", "") +REMOTE_API_MODEL = os.environ.get("REMOTE_API_MODEL", "claude-sonnet-4-6") + +# ============================================================================= +# MCP TOOLS SERVER +# ============================================================================= +MCP_SERVER_URL = os.environ.get("MCP_SERVER_URL", "http://localhost:8765") diff --git a/pyconUS-2026/demo/data/sales.csv b/pyconUS-2026/demo/data/sales.csv new file mode 100644 index 0000000..c8298e4 --- /dev/null +++ b/pyconUS-2026/demo/data/sales.csv @@ 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Pacific,23600,59 +2026-12-23,Data Connector,Europe,14600,29 +2026-12-26,Widget Lite,North America,9500,64 +2026-12-28,Analytics Suite,Europe,33900,22 +2026-12-30,Widget Pro,North America,23800,60 diff --git a/pyconUS-2026/demo/fs_bridge.py b/pyconUS-2026/demo/fs_bridge.py new file mode 100644 index 0000000..d8e4662 --- /dev/null +++ b/pyconUS-2026/demo/fs_bridge.py @@ -0,0 +1,133 @@ +"""File System Access API bridge for Demo 3's save_to_file tool. + +On the first save in a session, prompts the user: + "Download the file" or "Mount a local folder" + +Mounted-folder mode uses window.showDirectoryPicker() and writes +through the returned FileSystemDirectoryHandle — the file lands +on the user's actual disk with no backend involved. + +Falls back to download mode when the API is unavailable (Safari/Firefox). +""" + +from pyscript import window, document +from pyodide.ffi import create_proxy +import asyncio + + +_dir_handle = None # cached FileSystemDirectoryHandle +_save_mode = None # "download" | "folder" | None (not yet chosen) + + +def _fs_access_supported() -> bool: + return hasattr(window, "showDirectoryPicker") + + +async def _ask_save_mode() -> str: + """Show a modal and return 'download' or 'folder'.""" + if not _fs_access_supported(): + return "download" + + # Build modal + modal = document.createElement("div") + modal.id = "fs-modal" + modal.innerHTML = """ +
+
+

Where should the agent save the file?

+

+ The agent wants to write climate_notes.md to disk. + Choose how you'd like this to work. +

+
+ + +
+
+
""" + document.body.appendChild(modal) + + chosen = await _wait_for_choice() + document.body.removeChild(modal) + return chosen + + +async def _wait_for_choice() -> str: + result = [None] + + def on_folder(e): + result[0] = "folder" + + def on_download(e): + result[0] = "download" + + folder_proxy = create_proxy(on_folder) + download_proxy = create_proxy(on_download) + + document.getElementById("fs-folder").addEventListener("click", folder_proxy) + document.getElementById("fs-download").addEventListener("click", download_proxy) + + while result[0] is None: + await asyncio.sleep(0.05) + + folder_proxy.destroy() + download_proxy.destroy() + return result[0] + + +async def save_file(filename: str, content: str) -> str: + """Save content to a file. + + Returns a human-readable status string for the agent loop to pass + back to the LLM as the tool result. + """ + global _dir_handle, _save_mode + + if _save_mode is None: + _save_mode = await _ask_save_mode() + + if _save_mode == "folder": + if _dir_handle is None: + try: + _dir_handle = await window.showDirectoryPicker() + except Exception: + # User cancelled picker → fall back to download + _save_mode = "download" + + if _save_mode == "folder" and _dir_handle is not None: + try: + file_handle = await _dir_handle.getFileHandle(filename, {"create": True}) + writable = await file_handle.createWritable() + await writable.write(content) + await writable.close() + return f"Saved '{filename}' to your local folder." + except Exception as e: + return f"Folder write failed ({e}); try downloading instead." + + # Download mode + _trigger_download(filename, content) + return f"Downloaded '{filename}' to your Downloads folder." + + +def _trigger_download(filename: str, content: str): + blob = window.Blob.new([content], {"type": "text/markdown"}) + url = window.URL.createObjectURL(blob) + a = document.createElement("a") + a.href = url + a.download = filename + document.body.appendChild(a) + a.click() + document.body.removeChild(a) + window.URL.revokeObjectURL(url) diff --git a/pyconUS-2026/demo/index.html b/pyconUS-2026/demo/index.html new file mode 100644 index 0000000..cbbb5c0 --- /dev/null +++ b/pyconUS-2026/demo/index.html @@ -0,0 +1,2171 @@ + + + + AI Router Demo — PyCon US 2026 + + + + + + + + + + + + + + + +
+

Hybrid AI Router Demo

+

Edge inference & flexible routing · in-browser · local · remote · via PyScript

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Initializing…
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+ Bundle: — + · + Start: — + · + TTFT: — + + + + · + + + + + · + +
+ + 1.2× + +
+ · + + · + +
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+ Select a tier above and click a demo — or type your own message below. +
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+ + Agent log +
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+ Agent +
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+ + + + + + + + + + + + + + User + prompt + + + + + + + + + Agent + PyScript · agent loop + + + + + + + + + + + + + LLM + browser · local · remote + + + + + + PyScript tool + analyze_csv · Pandas + + + + + + MCP tools + web_search · save_to_file + + + + + + + + + context window (messages[]) + + +
+ + +
+
No context yet — run a demo to see messages[]
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No API calls yet — run a demo to capture requests & responses
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⚖ Trade-offs by Tier

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At a glance
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DimensionIn-browserLocalRemote
Latency (TTFT)~200ms~80ms~11ms
Model quality1–3B7–70Bfrontier
Tool-call qualitypoor (1B)good (8B+)excellent
Privacyfull — on devicefull — on devicedata leaves device
Cost / 1k tokens$0 (GPU amortised)$0 (local compute)$0.001–$0.03
Offline capable✓ after first load✓ always✗ needs network
First-load cost~700 MB model DLmodel pre-installednone
Requires GPUWebGPU (Chrome)CPU or GPUno
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Live measurements (this session)
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+ Bundle size + — +
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+ Page start time + — +
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+ Last TTFT + — +
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+ WebGPU available + — +
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Snapshot measurements (pre-captured)
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+ Loading… +
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When to use each
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+ In-browser + Simple, private, offline tasks. Date conversion, formatting, summarising local text. Best when data must never leave the device. +
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+ Local + Moderate reasoning on local data. Code generation, document analysis, tool-calling with capable models (8B+). Privacy + quality balance. +
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+ Remote + Multi-step agents, tool-calling loops, tasks that need frontier model quality. When latency > privacy and network is available. +
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+ +
+
+ + + + diff --git a/pyconUS-2026/demo/main.py b/pyconUS-2026/demo/main.py new file mode 100644 index 0000000..d8858d0 --- /dev/null +++ b/pyconUS-2026/demo/main.py @@ -0,0 +1,257 @@ +"""Router Demo — main entry point. + +Demo 1: LLM calls analyze_csv Pyodide tool, then narrates results (default tier: remote) +Demo 2: LLM calls MCP tools over HTTP — web_search + save_to_file (default tier: remote) +Demo 3: Load a local agent folder → instantiate and chat with that agent (default tier: remote) + +Each demo builds a messages list and calls run_agent_loop. +All streaming, bubble management, diagram state, and logging live in agent.py. +""" + +import asyncio +from pyscript import document + +from ui import add_message, clear_messages, update_status, get_selected_tier, agent_log +from router import route_request +from tiers import get_tier +from tools import list_tools, format_tools_for_llm, PYODIDE_TOOLS +from agent import run_agent_loop +from agent_loader import load_agent_from_folder + +# Shared conversation history — seeded by each demo, extended by send_message. +# run_agent_loop mutates this list in place (appending assistant + tool messages), +# so follow-up questions automatically have full context. +_history: list = [] +_current_tools: list = [] # tool schemas active for the current demo session + + +# ============================================================================= +# DEMO 1 — LLM calls analyze_csv (Pyodide tool) then narrates results +# ============================================================================= + +async def run_demo_1(event=None): + global _history, _current_tools + clear_messages() + tier_name = get_selected_tier() + tier = get_tier(tier_name) + + prompt = "Analyze the sales CSV at ./data/sales.csv and write a 3-sentence executive summary suitable for a board slide." + add_message(prompt, role="user") + + routing = route_request(prompt) + add_message( + f"Routing → {tier_name.upper()} + Pyodide tool · {routing['reason']}", + role="system", + ) + from pyscript import window as _win + _win.animateRoute(tier_name, "CSV") + + agent_log("route", f"keyword match: \"{routing.get('keyword', '')}\" → {routing['reason']}") + agent_log("tier", f"inference tier: {tier_name} | tool: analyze_csv (pyodide — in-browser Pandas)") + agent_log("info", "tool schema sent to LLM — waiting for tool_call decision") + + _current_tools = format_tools_for_llm(PYODIDE_TOOLS) + _history = [ + { + "role": "system", + "content": ( + "You are a data analyst assistant. You have access to an analyze_csv tool " + "that runs Pandas in the browser — no data leaves the device. " + "The tool returns: column names, row count, total revenue, top product, and QoQ growth. " + "Call analyze_csv first before answering any question about a CSV file. " + "For follow-up questions, answer directly from the tool result already in the conversation — " + "do not claim the tool lacks information that is already present in the tool result." + ), + }, + {"role": "user", "content": prompt}, + ] + await run_agent_loop(tier=tier, messages=_history, tools=_current_tools, route=tier_name) + + agent_log("agent_done", "agent loop complete") + + +# ============================================================================= +# DEMO 2 — LLM calls MCP tools over HTTP +# ============================================================================= + +async def run_demo_2(event=None): + global _history, _current_tools + clear_messages() + tier_name = get_selected_tier() + tier = get_tier(tier_name) + + prompt = "Find the top 3 climate stories from this week and save them to my notes" + add_message(prompt, role="user") + + routing = route_request(prompt) + add_message( + f"Routing → {tier_name.upper()} + MCP tools · {routing['reason']}", + role="system", + ) + from pyscript import window as _win + _win.animateRoute(tier_name, routing.get("keyword", "agent")) + + mcp_tools = await list_tools() + tool_names = [t["name"] for t in mcp_tools] if mcp_tools else ["(none found)"] + add_message(f"MCP tools available: {', '.join(tool_names)}", role="system") + + agent_log("route", f"keyword match: \"{routing.get('keyword', '')}\" → {routing['reason']}") + agent_log("tier", f"inference tier: {tier_name} | transport: MCP over HTTP (localhost:8765)") + agent_log("info", f"tools discovered: {', '.join(tool_names)}") + agent_log("info", "tool schemas sent to LLM — waiting for tool_call decision") + + _current_tools = format_tools_for_llm(mcp_tools) + _history = [ + { + "role": "system", + "content": ( + "You are a research assistant with access to tools. " + "When asked to find information, call web_search first. " + "When asked to save results, call save_to_file. " + "Always use the tools available — do not fabricate results." + ), + }, + {"role": "user", "content": prompt}, + ] + await run_agent_loop(tier=tier, messages=_history, tools=_current_tools, route=tier_name) + + agent_log("agent_done", "agent loop complete") + + +# ============================================================================= +# DEMO 3 — Load agent from local folder +# ============================================================================= + +async def run_demo_3(event=None): + """Prompt for a local agent folder, parse agent.yaml, chat with that agent.""" + global _history, _current_tools + clear_messages() + + add_message( + "📂 Folder Agent Demo — select a local agent folder to instantiate it.", + role="system", + ) + agent_log("info", "waiting for user to select agent folder…") + + config = await load_agent_from_folder() + if config is None: + add_message("No folder selected — demo cancelled.", role="system") + return + + tier_name = get_selected_tier() + tier = get_tier(tier_name) + + name = config.get("name", "Agent") + description = config.get("description", "") + system_prompt_raw = config.get("system_prompt", "You are a helpful assistant.") + documents = config.get("documents", {}) # {filename: text} + ai_cfg = config.get("ai", {}) + + # Build system message: system_prompt + injected documents + system_content = system_prompt_raw.strip() + if documents: + docs_block = "\n\n".join( + f"## Document: {fname}\n\n{text}" for fname, text in documents.items() + ) + system_content = f"{system_content}\n\n---\n\n# Reference Documents\n\n{docs_block}" + + # Display parsed agent card + model_info = ai_cfg.get("model", "unspecified") + doc_names = ", ".join(documents.keys()) if documents else "none" + add_message( + f"Agent loaded: {name}
" + f"{description}
" + f"Model hint: {model_info}  ·  Documents: {doc_names}
" + f"The selected tier overrides the model defined in agent.yaml.", + role="system", + ) + + agent_log("info", f"agent '{name}' — system prompt: {len(system_content)} chars") + agent_log("info", f"model hint from yaml: {model_info} | active tier: {tier_name}") + agent_log("info", f"documents injected: {', '.join(documents.keys()) if documents else 'none'}") + from pyscript import window as _win + _win.animateRoute(tier_name, "agent") + + # Seed the conversation with a greeting so the agent introduces itself + greeting_prompt = f"Hello! Please introduce yourself briefly." + add_message(greeting_prompt, role="user") + + _current_tools = [] # agent.yaml tools/mcp.json are informational; real MCP hookup is future work + _history = [ + {"role": "system", "content": system_content}, + {"role": "user", "content": greeting_prompt}, + ] + + agent_log("tier", f"inference tier: {tier_name}") + agent_log("info", "sending greeting — agent will introduce itself") + + await run_agent_loop(tier=tier, messages=_history, tools=_current_tools, route=tier_name) + + agent_log("agent_done", f"agent '{name}' ready — use the input below to chat") + add_message( + f"Agent {name} is ready — type your message below to continue the conversation.", + role="system", + ) + + +# ============================================================================= +# FREE-FORM INPUT +# ============================================================================= + +async def send_message(event=None): + global _history, _current_tools + input_el = document.getElementById("user-input") + prompt = input_el.value.strip() + if not prompt: + return + input_el.value = "" + + add_message(prompt, role="user") + tier_name = get_selected_tier() + add_message(f"Tier: {tier_name}", role="system") + + routing = route_request(prompt) + from pyscript import window as _win + _win.animateRoute(tier_name, routing.get("keyword", "")) + + _history.append({"role": "user", "content": prompt}) + + await run_agent_loop( + tier=get_tier(tier_name), + messages=_history, + tools=_current_tools, + route=tier_name, + ) + + +async def handle_keydown(event): + if event.key == "Enter": + await send_message() + + +# ============================================================================= +# INIT +# ============================================================================= + +print("Router Demo — PyCon US 2026") +update_status("", "In-browser: Not loaded") +agent_log("info", "PyScript + Pyodide ready — Python running in the browser") + +# Expose step-mode resume to JS +from pyscript import window as _win +from pyodide.ffi import create_proxy +from agent import resume_step +_win.agentResumeStep = create_proxy(resume_step) + +async def _prewarm(): + """Background pre-warm of WebLLM so Demo 1 in-browser is fast on stage.""" + from pyscript import window + from tiers import _init_browser_engine + if not getattr(window.navigator, "gpu", None): + return + if str(getattr(window, "caches", None)) in ("None", "undefined", ""): + return + await _init_browser_engine() + +asyncio.ensure_future(_prewarm()) +print("Ready.") diff --git a/pyconUS-2026/demo/metrics.py b/pyconUS-2026/demo/metrics.py new file mode 100644 index 0000000..925b7c5 --- /dev/null +++ b/pyconUS-2026/demo/metrics.py @@ -0,0 +1,21 @@ +"""In-page metrics helpers. + +Records time-to-first-token (TTFT) for each demo run and pushes it to +the JS metrics strip via window.recordTtft(). +""" + +import time +from pyscript import window + + +def start_timer() -> float: + return time.monotonic() + + +def record_ttft(start: float): + """Call this when the first token arrives. Pushes ms to the JS strip.""" + ms = int((time.monotonic() - start) * 1000) + try: + window.recordTtft(ms) + except Exception: + pass diff --git a/pyconUS-2026/demo/metrics_snapshot.json b/pyconUS-2026/demo/metrics_snapshot.json new file mode 100644 index 0000000..cb214de --- /dev/null +++ b/pyconUS-2026/demo/metrics_snapshot.json @@ -0,0 +1,53 @@ +{ + "_note": "Captured on 2026-05-08 from a cold Chrome load on M2 MacBook Pro. Re-run spike/measure_metrics.py before the talk to refresh these numbers.", + "bundle": { + "total_mb": 11.4, + "resource_count": 42, + "pyscript_kb": 312, + "pyodide_kb": 9800, + "pandas_kb": 620, + "webllm_kb": 180 + }, + "start_times": { + "cold_s": 8.2, + "warm_s": 1.1 + }, + "ttft_ms": { + "in_browser_first_token": 210, + "local_first_token": 85, + "remote_first_token": 11 + }, + "tiers": { + "in_browser": { + "model": "Llama-3.2-1B-Instruct-q4f32_1-MLC", + "model_download_mb": 700, + "ttft_ms": 210, + "tokens_per_sec": 28, + "privacy": "full — on device", + "cost_per_1k_tokens": "$0", + "offline": true, + "requires_webgpu": true, + "tool_call_quality": "poor (1B model)" + }, + "local": { + "model": "llama3.2 (8B via Ollama)", + "ttft_ms": 85, + "tokens_per_sec": 52, + "privacy": "full — on device", + "cost_per_1k_tokens": "$0", + "offline": true, + "requires_webgpu": false, + "tool_call_quality": "good" + }, + "remote": { + "model": "claude-sonnet-4-6", + "ttft_ms": 11, + "tokens_per_sec": 120, + "privacy": "data leaves device", + "cost_per_1k_tokens": "$0.003", + "offline": false, + "requires_webgpu": false, + "tool_call_quality": "excellent" + } + } +} diff --git a/pyconUS-2026/demo/models.py b/pyconUS-2026/demo/models.py new file mode 100644 index 0000000..66d3faf --- /dev/null +++ b/pyconUS-2026/demo/models.py @@ -0,0 +1,26 @@ +"""Thin compatibility shim — main logic lives in tiers.py. + +Kept so send_message() in main.py can still call browser/local/remote +generate without knowing about tier objects. +""" + +from tiers import InBrowserTier, LocalTier, RemoteTier, _init_browser_engine + + +async def init_browser_model(): + return await _init_browser_engine() + + +async def browser_stream(messages: list): + async for delta in InBrowserTier().stream_chat(messages): + yield delta + + +async def local_stream(messages: list, tools=None): + async for delta in LocalTier().stream_chat(messages, tools): + yield delta + + +async def remote_stream(messages: list, tools=None): + async for delta in RemoteTier().stream_chat(messages, tools): + yield delta diff --git a/pyconUS-2026/demo/pyscript.toml b/pyconUS-2026/demo/pyscript.toml new file mode 100644 index 0000000..6f65ad4 --- /dev/null +++ b/pyconUS-2026/demo/pyscript.toml @@ -0,0 +1,18 @@ +# PyScript configuration for Router Demo + +packages = ["pandas", "pyyaml"] + +[files] +# Load order: dependencies before dependents +"./config.py" = "" +"./streaming.py" = "" +"./ui.py" = "" +"./router.py" = "" +"./fs_bridge.py" = "" +"./tools.py" = "" +"./tiers.py" = "" +"./models.py" = "" +"./agent.py" = "" +"./metrics.py" = "" +"./agent_loader.py" = "" +"./main.py" = "" diff --git a/pyconUS-2026/demo/router.py b/pyconUS-2026/demo/router.py new file mode 100644 index 0000000..8d43dbb --- /dev/null +++ b/pyconUS-2026/demo/router.py @@ -0,0 +1,90 @@ +""" +Router for the Demo. + +Decides which model backend to use based on the prompt. +""" + + +def route_request(prompt: str) -> dict: + """Determine the routing path for a prompt. + + Routing logic: + - BROWSER: Simple tasks that can run in-browser (fast, private) + - LOCAL: Moderate tasks for local server (if available) + - HYBRID: Data processing + reasoning (local data + remote model) + - REMOTE: Complex queries or tasks needing tools + + Args: + prompt: User's input prompt + + Returns: + dict with: + - path: "browser" | "local" | "hybrid" | "remote" + - reason: Human-readable explanation + - needs_tools: List of required tool names (if any) + """ + prompt_lower = prompt.lower() + + # Patterns for in-browser handling (simple, quick, private) + browser_patterns = [ + "convert", "format", "translate to", "what is", + "calculate", "count", "spell", "define" + ] + + # Patterns that need external tools + tool_patterns = { + "search": "web_search", + "find": "web_search", + "look up": "web_search", + "save": "save_to_file", + "store": "save_to_file", + "remember": "save_to_file", + "notes": "save_to_file", + } + + # Patterns for hybrid processing (local data + remote reasoning) + hybrid_patterns = ["summarize", "analyze", "csv", "data", "file"] + + # Check for tool needs → remote path + needed_tools = [] + for pattern, tool in tool_patterns.items(): + if pattern in prompt_lower: + if tool not in needed_tools: + needed_tools.append(tool) + + if needed_tools: + matched_kw = next((p for p in tool_patterns if p in prompt_lower), needed_tools[0]) + return { + "path": "remote", + "reason": f"Requires tools: {', '.join(needed_tools)}", + "needs_tools": needed_tools, + "keyword": matched_kw, + } + + # Check for hybrid patterns + for pattern in hybrid_patterns: + if pattern in prompt_lower: + return { + "path": "hybrid", + "reason": "Data processing + reasoning needed", + "needs_tools": [], + "keyword": pattern, + } + + # Check for browser-local patterns (simple, fast, private) + for pattern in browser_patterns: + if pattern in prompt_lower: + return { + "path": "browser", + "reason": "Simple task → in-browser (WebLLM)", + "needs_tools": [], + "keyword": pattern, + } + + # Default to remote for complex queries + return { + "path": "remote", + "reason": "Complex query → remote model", + "needs_tools": [], + "keyword": "", + } diff --git a/pyconUS-2026/demo/servers/mcp_server.py b/pyconUS-2026/demo/servers/mcp_server.py new file mode 100644 index 0000000..f9abbb4 --- /dev/null +++ b/pyconUS-2026/demo/servers/mcp_server.py @@ -0,0 +1,188 @@ +"""MCP HTTP server for the router demo. + +Tools exposed: + web_search(query, k=3) — returns k climate news story objects + save_to_file(filename, content) — writes to ./notes/ (server-side fallback; + browser uses File System Access API first) + +Run with: python server.py +Endpoints: GET /tools, POST /call-tool, GET /health +""" + +import json +import os +from datetime import date +from http.server import HTTPServer, BaseHTTPRequestHandler +from pathlib import Path + +NOTES_DIR = Path(__file__).parent / "notes" +NOTES_DIR.mkdir(exist_ok=True) + +# Plausible climate stories used by web_search mock +_CLIMATE_STORIES = [ + { + "title": "Global Carbon Emissions Decline for First Time in a Decade", + "url": "https://example-news.org/climate/carbon-decline-2026", + "summary": ( + "A new IEA report shows global CO₂ emissions fell 0.4% in 2025, " + "driven by record renewable capacity additions and EV adoption." + ), + }, + { + "title": "EU Green Deal Reaches 50% Renewable Energy Milestone", + "url": "https://example-news.org/climate/eu-renewables-milestone", + "summary": ( + "The European Union announced that 50.2% of its electricity came " + "from renewable sources in Q1 2026 — three years ahead of schedule." + ), + }, + { + "title": "Ocean Cleanup Project Reports 30% Reduction in Pacific Plastic Patch", + "url": "https://example-news.org/climate/ocean-cleanup-2026", + "summary": ( + "The Ocean Cleanup nonprofit confirmed its System 03 has removed " + "over 400,000 kg of plastic, shrinking the Great Pacific Garbage Patch " + "by an estimated 30% since 2021." + ), + }, + { + "title": "Arctic Sea Ice Extent Records Largest Recovery in 15 Years", + "url": "https://example-news.org/climate/arctic-ice-recovery", + "summary": ( + "NSIDC data shows Arctic summer sea ice extent in 2025 was 6% larger " + "than the previous year — the biggest single-year recovery since 2010." + ), + }, + { + "title": "India Surpasses 500 GW Renewable Capacity Target Two Years Early", + "url": "https://example-news.org/climate/india-renewables-500gw", + "summary": ( + "India reached 502 GW of installed renewable energy capacity in March 2026, " + "making it the third-largest renewable producer globally." + ), + }, +] + + +TOOLS = [ + { + "name": "web_search", + "description": "Search the web for recent news stories on a topic.", + "inputSchema": { + "type": "object", + "properties": { + "query": { + "type": "string", + "description": "Search query string", + }, + "k": { + "type": "integer", + "description": "Number of results to return (default 3)", + }, + }, + "required": ["query"], + }, + }, + { + "name": "save_to_file", + "description": "Save text content to a file.", + "inputSchema": { + "type": "object", + "properties": { + "filename": { + "type": "string", + "description": "Name of the file to create or overwrite", + }, + "content": { + "type": "string", + "description": "Text content to write", + }, + }, + "required": ["filename", "content"], + }, + }, +] + + +def _call_web_search(arguments: dict) -> dict: + k = min(int(arguments.get("k", 3)), len(_CLIMATE_STORIES)) + stories = _CLIMATE_STORIES[:k] + lines = [] + for i, s in enumerate(stories, 1): + lines.append(f"{i}. **{s['title']}**\n {s['url']}\n {s['summary']}") + text = "\n\n".join(lines) + return {"content": [{"type": "text", "text": text}]} + + +def _call_save_to_file(arguments: dict) -> dict: + filename = arguments.get("filename", f"notes_{date.today()}.md") + content = arguments.get("content", "") + # Sanitize filename + safe_name = Path(filename).name + path = NOTES_DIR / safe_name + path.write_text(content, encoding="utf-8") + return {"content": [{"type": "text", "text": f"Saved to {path}"}]} + + +def call_tool(name: str, arguments: dict) -> dict: + if name == "web_search": + return _call_web_search(arguments) + if name == "save_to_file": + return _call_save_to_file(arguments) + return { + "content": [{"type": "text", "text": f"Unknown tool: {name}"}], + "isError": True, + } + + +class MCPHandler(BaseHTTPRequestHandler): + def _send_json(self, data, status=200): + body = json.dumps(data).encode() + self.send_response(status) + self.send_header("Content-Type", "application/json") + self.send_header("Content-Length", str(len(body))) + self.send_header("Access-Control-Allow-Origin", "*") + self.send_header("Access-Control-Allow-Methods", "GET, POST, OPTIONS") + self.send_header("Access-Control-Allow-Headers", "Content-Type") + self.end_headers() + self.wfile.write(body) + + def do_OPTIONS(self): + self.send_response(204) + self.send_header("Access-Control-Allow-Origin", "*") + self.send_header("Access-Control-Allow-Methods", "GET, POST, OPTIONS") + self.send_header("Access-Control-Allow-Headers", "Content-Type") + self.end_headers() + + def do_GET(self): + if self.path == "/tools": + self._send_json({"tools": TOOLS}) + elif self.path == "/health": + self._send_json({"status": "ok"}) + else: + self._send_json({"error": "Not found"}, 404) + + def do_POST(self): + if self.path == "/call-tool": + length = int(self.headers.get("Content-Length", 0)) + body = self.rfile.read(length) + try: + data = json.loads(body) + result = call_tool(data.get("name"), data.get("arguments", {})) + self._send_json(result) + except json.JSONDecodeError: + self._send_json({"error": "Invalid JSON"}, 400) + else: + self._send_json({"error": "Not found"}, 404) + + def log_message(self, fmt, *args): + print(f"[MCP] {args[0]}") + + +if __name__ == "__main__": + port = 8765 + server = HTTPServer(("localhost", port), MCPHandler) + print(f"MCP server on http://localhost:{port}") + print(f"Notes directory: {NOTES_DIR}") + print("Endpoints: GET /tools POST /call-tool GET /health") + server.serve_forever() diff --git a/pyconUS-2026/demo/servers/run_all.sh b/pyconUS-2026/demo/servers/run_all.sh new file mode 100755 index 0000000..cd128e2 --- /dev/null +++ b/pyconUS-2026/demo/servers/run_all.sh @@ -0,0 +1,41 @@ +#!/usr/bin/env bash +# Start all servers needed for the router demo. +# Run from the repo root: ./demo/servers/run_all.sh + +SERVERS_DIR="$(cd "$(dirname "$0")" && pwd)" +DEMO_DIR="$(cd "$SERVERS_DIR/.." && pwd)" + +# Kill any leftover processes on our ports before starting +for port in 8000 8765 8766; do + lsof -ti ":$port" | xargs kill -9 2>/dev/null || true +done + +cleanup() { + echo "" + echo "Stopping all servers..." + # Kill the entire process group so subshells and their children all die + kill -9 -- -$$ 2>/dev/null || true + # Belt-and-suspenders: also kill by port in case PGIDs differ + for port in 8000 8765 8766; do + lsof -ti ":$port" | xargs kill -9 2>/dev/null || true + done + echo "Done." + exit 0 +} + +trap cleanup INT TERM + +# Run all three in the same process group (setsid not used — $$ is the group leader) +python "$SERVERS_DIR/mcp_server.py" & +python "$SERVERS_DIR/sse_server.py" & +python -m http.server 8000 --directory "$DEMO_DIR" & + +echo "" +echo "All servers running:" +echo " Demo: http://localhost:8000" +echo " MCP: http://localhost:8765" +echo " Remote: http://localhost:8766 (mock SSE)" +echo "" +echo "Press Ctrl+C to stop all." + +wait diff --git a/pyconUS-2026/demo/servers/sse_server.py b/pyconUS-2026/demo/servers/sse_server.py new file mode 100644 index 0000000..5ff291f --- /dev/null +++ b/pyconUS-2026/demo/servers/sse_server.py @@ -0,0 +1,324 @@ +"""Mock SSE server that simulates LLM token streaming. + +Run with: python sse_server.py +Exposes: POST /v1/chat/completions (OpenAI-compatible streaming) + +Prompt-aware: matches the last user message against known demo prompts and +returns a contextually correct streamed response. Falls back to a generic +reply for free-form input. +""" + +from http.server import HTTPServer, BaseHTTPRequestHandler +import json +import time + + +def _has_tool_result(messages: list) -> bool: + """Return True if the history already contains a tool result message.""" + return any(m.get("role") == "tool" for m in messages) + + +def _wants_tools(messages: list, tools: list) -> bool: + """Return True if this is the first turn of a tool-calling demo.""" + if not tools: + return False + last = "" + for m in reversed(messages): + if m.get("role") == "user": + last = m.get("content", "").lower() + break + return ( + "climate" in last or "stories" in last or "notes" in last or "search" in last + or "csv" in last or "analyze" in last or "sales" in last + ) + + +def _pick_tool_calls(messages: list) -> list[dict]: + """Return the tool_calls to emit on the first agent turn.""" + last = "" + for m in reversed(messages): + if m.get("role") == "user": + last = m.get("content", "").lower() + break + # Demo 2 — CSV analysis via Pyodide tool + if "csv" in last or "analyze" in last or "sales" in last: + return [{"name": "analyze_csv", "args": {"path": "./data/sales.csv"}}] + # Demo 3 — climate search + save + calls = [{"name": "web_search", "args": {"query": "top climate stories this week", "k": 3}}] + if "save" in last or "notes" in last: + calls.append({"name": "save_to_file", "args": {"filename": "climate_notes.md", "content": "placeholder"}}) + return calls + + +def _pick_response(messages: list) -> list[str]: + """Return token list for the final (after tool results) assistant turn.""" + last = "" + for m in reversed(messages): + if m.get("role") == "user": + last = m.get("content", "").lower() + break + + # Demo 1 — ISO date conversion + if "iso" in last or "may 5" in last or "2026" in last: + return list("2026-05-05") + + # Demo 2 — narrative after analyze_csv tool result + if "csv" in last or "analyze" in last or "sales" in last or "board slide" in last or "executive summary" in last: + return ( + "Revenue reached $2.4 M in the first half of 2026, driven by strong " + "Analytics Suite performance at 43 % of total sales. " + "Quarter-over-quarter growth of +11.2 % signals healthy demand momentum heading into Q3. " + "Management recommends accelerating Analytics Suite inventory ahead of the summer peak season." + ).split(" ") + + # Demo 3 — final summary after tool results arrive + if "climate" in last or "stories" in last or "notes" in last: + return ( + "I've searched for the top climate stories this week and saved them " + "to your notes. The headlines cover record renewable capacity, ocean " + "cleanup milestones, and Arctic ice recovery — all strong signals of " + "progress on the climate agenda." + ).split(" ") + + # Generic fallback + return ( + "I'm a mock remote LLM. Your message was received and processed " + "server-side. Switch to a real API endpoint by setting REMOTE_API_KEY." + ).split(" ") + + +class SSEHandler(BaseHTTPRequestHandler): + def _send_cors_headers(self): + self.send_header("Access-Control-Allow-Origin", "*") + self.send_header("Access-Control-Allow-Methods", "GET, POST, OPTIONS") + self.send_header("Access-Control-Allow-Headers", "Content-Type, Authorization") + + def do_OPTIONS(self): + self.send_response(204) + self._send_cors_headers() + self.end_headers() + + def do_GET(self): + if self.path == "/health": + self.send_response(200) + self.send_header("Content-Type", "application/json") + self._send_cors_headers() + self.end_headers() + self.wfile.write(b'{"status": "ok"}') + else: + self.send_response(404) + self._send_cors_headers() + self.end_headers() + + def do_POST(self): + if self.path == "/v1/chat/completions": + self._handle_completions() + else: + self.send_response(404) + self._send_cors_headers() + self.end_headers() + self.wfile.write(b'{"error": "Not found"}') + + def _handle_completions(self): + content_length = int(self.headers.get("Content-Length", 0)) + body = self.rfile.read(content_length) + + try: + data = json.loads(body) + except Exception: + data = {} + + messages = data.get("messages", []) + tools = data.get("tools", []) + stream = data.get("stream", False) + + # Decide: emit tool_calls (first agent turn) or text (final/text turn) + if _wants_tools(messages, tools) and not _has_tool_result(messages): + self._tool_calls_to_emit = _pick_tool_calls(messages) + if stream: + self._stream_tool_calls() + else: + self._send_tool_calls() + else: + self._response_tokens = _pick_response(messages) + if stream: + self._stream_response() + else: + self._send_full_response() + + def _stream_tool_calls(self): + """Emit an SSE stream that ends with finish_reason=tool_calls.""" + self.send_response(200) + self.send_header("Content-Type", "text/event-stream") + self.send_header("Cache-Control", "no-cache") + self.send_header("Connection", "keep-alive") + self._send_cors_headers() + self.end_headers() + + tool_calls = getattr(self, "_tool_calls_to_emit", []) + for idx, tc in enumerate(tool_calls): + # First chunk: id + name + chunk = { + "id": f"chatcmpl-tc-{idx}", + "object": "chat.completion.chunk", + "created": int(time.time()), + "model": "mock-remote", + "choices": [{ + "index": 0, + "delta": { + "tool_calls": [{ + "index": idx, + "id": f"call_{idx}", + "type": "function", + "function": {"name": tc["name"], "arguments": ""}, + }] + }, + "finish_reason": None, + }] + } + self.wfile.write(f"data: {json.dumps(chunk)}\n\n".encode()) + self.wfile.flush() + time.sleep(0.03) + + # Second chunk: arguments + args_str = json.dumps(tc["args"]) + chunk2 = { + "id": f"chatcmpl-tc-{idx}-args", + "object": "chat.completion.chunk", + "created": int(time.time()), + "model": "mock-remote", + "choices": [{ + "index": 0, + "delta": { + "tool_calls": [{ + "index": idx, + "function": {"arguments": args_str}, + }] + }, + "finish_reason": None, + }] + } + self.wfile.write(f"data: {json.dumps(chunk2)}\n\n".encode()) + self.wfile.flush() + time.sleep(0.03) + + # Final chunk with finish_reason=tool_calls + final = { + "id": "chatcmpl-tc-final", + "object": "chat.completion.chunk", + "created": int(time.time()), + "model": "mock-remote", + "choices": [{"index": 0, "delta": {}, "finish_reason": "tool_calls"}] + } + self.wfile.write(f"data: {json.dumps(final)}\n\n".encode()) + self.wfile.write(b"data: [DONE]\n\n") + self.wfile.flush() + + def _send_tool_calls(self): + """Non-streaming tool_calls response.""" + self.send_response(200) + self.send_header("Content-Type", "application/json") + self._send_cors_headers() + self.end_headers() + + tool_calls = getattr(self, "_tool_calls_to_emit", []) + response = { + "id": "chatcmpl-tc", + "object": "chat.completion", + "created": int(time.time()), + "model": "mock-remote", + "choices": [{ + "index": 0, + "message": { + "role": "assistant", + "content": None, + "tool_calls": [ + { + "id": f"call_{i}", + "type": "function", + "function": {"name": tc["name"], "arguments": json.dumps(tc["args"])}, + } + for i, tc in enumerate(tool_calls) + ], + }, + "finish_reason": "tool_calls", + }] + } + self.wfile.write(json.dumps(response).encode()) + + def _stream_response(self): + """Send SSE streaming response like OpenAI.""" + self.send_response(200) + self.send_header("Content-Type", "text/event-stream") + self.send_header("Cache-Control", "no-cache") + self.send_header("Connection", "keep-alive") + self._send_cors_headers() + self.end_headers() + + tokens = getattr(self, "_response_tokens", ["…"]) + for i, token in enumerate(tokens): + # Add space prefix for word-split tokens (not for char-split like ISO date) + tok = token if i == 0 else (" " + token if len(token) > 1 else token) + event = { + "id": f"chatcmpl-{i}", + "object": "chat.completion.chunk", + "created": int(time.time()), + "model": "mock-remote", + "choices": [{ + "index": 0, + "delta": {"content": tok} if i > 0 else {"role": "assistant", "content": tok}, + "finish_reason": None, + }] + } + self.wfile.write(f"data: {json.dumps(event)}\n\n".encode()) + self.wfile.flush() + time.sleep(0.04) + + # Send final event + final_event = { + "id": f"chatcmpl-final", + "object": "chat.completion.chunk", + "created": int(time.time()), + "model": "mock-gpt-4", + "choices": [{ + "index": 0, + "delta": {}, + "finish_reason": "stop" + }] + } + self.wfile.write(f"data: {json.dumps(final_event)}\n\n".encode()) + self.wfile.write(b"data: [DONE]\n\n") + self.wfile.flush() + + def _send_full_response(self): + """Send non-streaming response.""" + self.send_response(200) + self.send_header("Content-Type", "application/json") + self._send_cors_headers() + self.end_headers() + + tokens = getattr(self, "_response_tokens", ["…"]) + content = " ".join(tokens) if len(tokens[0]) > 1 else "".join(tokens) + response = { + "id": "chatcmpl-123", + "object": "chat.completion", + "created": int(time.time()), + "model": "mock-remote", + "choices": [{ + "index": 0, + "message": {"role": "assistant", "content": content}, + "finish_reason": "stop", + }] + } + self.wfile.write(json.dumps(response).encode()) + + def log_message(self, format, *args): + print(f"[SSE Server] {args[0]}") + + +if __name__ == "__main__": + port = 8766 + server = HTTPServer(("localhost", port), SSEHandler) + print(f"SSE test server running on http://localhost:{port}") + print("Endpoints: POST /v1/chat/completions (stream=true for SSE)") + server.serve_forever() diff --git a/pyconUS-2026/demo/streaming.py b/pyconUS-2026/demo/streaming.py new file mode 100644 index 0000000..8909655 --- /dev/null +++ b/pyconUS-2026/demo/streaming.py @@ -0,0 +1,108 @@ +"""SSE stream reader and Delta normalization. + +Reads response.body via JS getReader(), decodes tokens, and yields +normalized Delta objects for any OpenAI-compatible SSE endpoint. +""" + +import json +from dataclasses import dataclass, field +from pyscript import window + + +@dataclass +class ToolCall: + id: str + name: str + args: dict = field(default_factory=dict) + + +@dataclass +class Delta: + text: str | None = None + tool_call: ToolCall | None = None + finish_reason: str | None = None + + +async def read_sse_stream(response): + """Read an SSE response stream and yield raw line strings.""" + decoder = window.TextDecoder.new() + reader = response.body.getReader() + buffer = "" + + while True: + result = await reader.read() + if result.done: + break + try: + text = decoder.decode(result.value) + buffer += text + while "\n" in buffer: + line, buffer = buffer.split("\n", 1) + line = line.strip() + if line: + yield line + except Exception: + pass + + if buffer.strip(): + yield buffer.strip() + + +async def parse_openai_sse(response): + """Parse an OpenAI-compatible SSE stream into Delta objects. + + Handles both text tokens and tool_calls (accumulated across chunks). + """ + # Accumulate tool call fragments keyed by index + pending_tool_calls: dict[int, dict] = {} + + async for line in read_sse_stream(response): + if not line.startswith("data:"): + continue + + data = line[5:].strip() + if data == "[DONE]": + break + + try: + chunk = json.loads(data) + except json.JSONDecodeError: + continue + + choice = chunk.get("choices", [{}])[0] + finish_reason = choice.get("finish_reason") + delta = choice.get("delta", {}) + + # Text token + content = delta.get("content") + if content: + yield Delta(text=content) + + # Tool call fragments + for tc_chunk in delta.get("tool_calls", []): + idx = tc_chunk.get("index", 0) + if idx not in pending_tool_calls: + pending_tool_calls[idx] = { + "id": tc_chunk.get("id", f"call_{idx}"), + "name": "", + "arguments": "", + } + fn = tc_chunk.get("function", {}) + if fn.get("name"): + pending_tool_calls[idx]["name"] += fn["name"] + if fn.get("arguments"): + pending_tool_calls[idx]["arguments"] += fn["arguments"] + + if finish_reason == "tool_calls": + for tc in pending_tool_calls.values(): + try: + args = json.loads(tc["arguments"]) if tc["arguments"] else {} + except json.JSONDecodeError: + args = {"raw": tc["arguments"]} + yield Delta(tool_call=ToolCall(id=tc["id"], name=tc["name"], args=args)) + yield Delta(finish_reason="tool_calls") + return + + if finish_reason == "stop": + yield Delta(finish_reason="stop") + return diff --git a/pyconUS-2026/demo/tiers.py b/pyconUS-2026/demo/tiers.py new file mode 100644 index 0000000..68c6dec --- /dev/null +++ b/pyconUS-2026/demo/tiers.py @@ -0,0 +1,255 @@ +"""Tier implementations: InBrowserTier, LocalTier, RemoteTier. + +Each tier implements: + async def stream_chat(messages, tools=None) -> AsyncIterator[Delta] + +Callers (main.py, agent.py) are tier-agnostic; they only see Delta objects. +""" + +import json +import asyncio +from pyscript import fetch, window +from pyodide.ffi import create_proxy, to_js +import js + +from config import ( + BROWSER_MODEL_ID, + LOCAL_API_URL, LOCAL_API_MODEL, + REMOTE_API_URL, REMOTE_API_KEY, REMOTE_API_MODEL, +) +from streaming import Delta, ToolCall, parse_openai_sse +from ui import update_status, update_progress, agent_log + + +# ============================================================================= +# IN-BROWSER TIER (WebLLM via WebGPU) +# ============================================================================= + +_browser_engine = None + + +async def _init_browser_engine(): + global _browser_engine + if _browser_engine is not None: + return True + + update_status("loading", "In-browser: Checking prerequisites…") + try: + # 1. Must be served over HTTP (not file://) for Cache Storage API to work + protocol = str(window.location.protocol) + if protocol == "file:": + raise Exception("Must be served over http:// — run: python -m http.server 8000") + + # 2. Cache Storage API must be available (secure context check) + if not getattr(window, "caches", None): + raise Exception("Cache Storage API unavailable — try Chrome with hardware acceleration enabled (chrome://settings/system)") + + # 3. WebGPU must be available for local inference + gpu = getattr(window.navigator, "gpu", None) + if not gpu: + raise Exception("WebGPU unavailable — enable GPU acceleration in Chrome settings (chrome://settings/system)") + + # 4. Wait for the WebLLM ES module to finish loading + update_status("loading", "In-browser: Loading model…") + for _ in range(20): + if getattr(window, "webllmLoaded", False): + break + await asyncio.sleep(0.25) + else: + raise Exception("WebLLM module did not load — check browser console") + + webllm = window.webllm + + def on_progress(progress): + # Extract immediately — progress is a borrowed JS proxy + try: + pct = int(float(str(progress.progress)) * 100) + except Exception: + pct = 0 + try: + text = str(progress.text) + except Exception: + text = "" + update_status("loading", f"In-browser: {pct}%") + update_progress(pct, text) + + on_progress_proxy = create_proxy(on_progress) + _browser_engine = await webllm.CreateMLCEngine( + BROWSER_MODEL_ID, + {"initProgressCallback": on_progress_proxy}, + ) + on_progress_proxy.destroy() + update_status("ready", "In-browser: Ready") + update_progress(100, "Model ready") + return True + except Exception as e: + update_status("", f"In-browser: Failed ({e})") + return False + + +class InBrowserTier: + name = "in-browser" + + async def stream_chat(self, messages, tools=None): + agent_log("tier", f"in-browser: initializing WebLLM engine ({BROWSER_MODEL_ID})") + ok = await _init_browser_engine() + if not ok: + agent_log("error", "in-browser: engine init failed — model unavailable") + yield Delta(text="[In-browser model unavailable]", finish_reason="stop") + return + + agent_log("tier", f"in-browser: sending {len(messages)} message(s) to {BROWSER_MODEL_ID}") + _push_call("in-browser", "request", { + "model": BROWSER_MODEL_ID, + "messages": messages, + "max_tokens": 512, + "temperature": 0.1, + "stream": True, + "note": "WebLLM — runs entirely in the browser, no network call", + }) + try: + # Pass options as a JS object — Python dict kwargs don't survive + # the async boundary correctly with some WebLLM builds + options = to_js({ + "messages": messages, + "max_tokens": 512, + "temperature": 0.1, + "stream": True, + }, dict_converter=js.Object.fromEntries) + + stream = await _browser_engine.chat.completions.create(options) + agent_log("tier", "in-browser: stream opened — reading tokens") + _push_call("in-browser", "response", {"stream": "WebLLM token stream (in-browser, no HTTP)"}) + + async for chunk in stream: + # Extract values immediately before yielding — proxies are + # destroyed by Pyodide at yield/await boundaries + try: + content = str(chunk.choices[0].delta.content or "") + finish = str(chunk.choices[0].finish_reason or "") + except Exception: + content = "" + finish = "" + + if content: + yield Delta(text=content) + # WebLLM may send finish_reason as "stop", "None", or "null" + if finish in ("stop", "length"): + agent_log("tier", f"in-browser: stream finished ({finish})") + yield Delta(finish_reason="stop") + return + if finish not in ("", "None", "null", "none"): + # unexpected finish reason — treat as stop + agent_log("tier", f"in-browser: stream finished (finish_reason={finish!r})") + yield Delta(finish_reason="stop") + return + + # Stream exhausted without an explicit finish_reason + agent_log("tier", "in-browser: stream ended (no explicit finish_reason — implying stop)") + yield Delta(finish_reason="stop") + except Exception as e: + agent_log("error", f"in-browser: exception during stream — {e}") + yield Delta(text=f"\n[Error: {e}]", finish_reason="stop") + + +# ============================================================================= +# LOCAL TIER (Ollama / LM Studio / llama.cpp — OpenAI-compatible) +# ============================================================================= + +def _push_call(tier_name: str, direction: str, payload): + try: + window.archAddCall(tier_name, direction, json.dumps(payload, indent=2)) + except Exception: + pass + + +class LocalTier: + name = "local" + + async def stream_chat(self, messages, tools=None): + url = f"{LOCAL_API_URL}/v1/chat/completions" + agent_log("tier", f"local: POST {url} — model={LOCAL_API_MODEL}, {len(messages)} msg(s), tools={len(tools or [])}") + payload = { + "model": LOCAL_API_MODEL, + "messages": messages, + "stream": True, + } + if tools: + payload["tools"] = tools + + _push_call("local", "request", payload) + try: + response = await fetch( + url, + method="POST", + headers={"Content-Type": "application/json"}, + body=json.dumps(payload), + ) + agent_log("tier", f"local: HTTP {response.status} — reading SSE stream") + if response.status != 200: + raise Exception(f"HTTP {response.status}") + _push_call("local", "response", {"status": response.status, "stream": "SSE (streamed tokens)"}) + async for delta in parse_openai_sse(response): + yield delta + agent_log("tier", "local: stream complete") + except Exception as e: + agent_log("error", f"local: {e}") + _push_call("local", "response", {"error": str(e)}) + yield Delta(text=f"[Local server error: {e}]", finish_reason="stop") + + +# ============================================================================= +# REMOTE TIER (OpenAI-compatible — real API or mock SSE server) +# ============================================================================= + +class RemoteTier: + name = "remote" + + async def stream_chat(self, messages, tools=None): + url = f"{REMOTE_API_URL}/v1/chat/completions" + auth = "key set" if REMOTE_API_KEY else "no key (mock)" + agent_log("tier", f"remote: POST {url} — model={REMOTE_API_MODEL}, {len(messages)} msg(s), tools={len(tools or [])}, auth={auth}") + payload = { + "model": REMOTE_API_MODEL, + "messages": messages, + "stream": True, + } + if tools: + payload["tools"] = tools + + headers = {"Content-Type": "application/json"} + if REMOTE_API_KEY: + headers["Authorization"] = f"Bearer {REMOTE_API_KEY}" + + _push_call("remote", "request", payload) + try: + response = await fetch( + url, + method="POST", + headers=headers, + body=json.dumps(payload), + ) + agent_log("tier", f"remote: HTTP {response.status} — reading SSE stream") + if response.status != 200: + raise Exception(f"HTTP {response.status}") + _push_call("remote", "response", {"status": response.status, "stream": "SSE (streamed tokens)"}) + async for delta in parse_openai_sse(response): + yield delta + agent_log("tier", "remote: stream complete") + except Exception as e: + agent_log("error", f"remote: {e}") + _push_call("remote", "response", {"error": str(e)}) + yield Delta(text=f"[Remote API error: {e}]", finish_reason="stop") + + +# ============================================================================= +# FACTORY +# ============================================================================= + +def get_tier(name: str): + """Return a tier instance by name: 'in-browser', 'local', 'remote'.""" + return { + "in-browser": InBrowserTier, + "local": LocalTier, + "remote": RemoteTier, + }[name]() diff --git a/pyconUS-2026/demo/tools.py b/pyconUS-2026/demo/tools.py new file mode 100644 index 0000000..9802079 --- /dev/null +++ b/pyconUS-2026/demo/tools.py @@ -0,0 +1,184 @@ +"""MCP tool client, Pyodide-backed tools, and tool-call normalization helpers.""" + +import json +from pyscript import fetch + +from config import MCP_SERVER_URL +from fs_bridge import save_file +from ui import agent_log + + +# ============================================================================= +# PYODIDE-BACKED TOOLS +# These run entirely in the browser — no network call, no backend. +# The LLM decides to call them just like any other tool. +# ============================================================================= + +PYODIDE_TOOLS = [ + { + "name": "analyze_csv", + "description": ( + "Analyze a CSV file using Pandas running in the browser (Pyodide). " + "Returns column names, row count, total revenue, top product by revenue, and QoQ growth. " + "Call this tool whenever the user asks about the file structure, column names, or data analysis. " + "No data leaves the browser." + ), + "inputSchema": { + "type": "object", + "properties": { + "path": { + "type": "string", + "description": "Path to the CSV file, relative to the demo root (e.g. './data/sales.csv')", + } + }, + "required": ["path"], + }, + } +] + + +async def _call_analyze_csv(arguments: dict) -> dict: + """Run Pandas analysis in Pyodide. Returns a structured summary string.""" + import io as _io + try: + import pandas as pd + except ImportError: + agent_log("error", "analyze_csv: pandas not available in this environment") + return {"content": [{"type": "text", "text": "Pandas not available in this environment."}]} + + path = arguments.get("path", "./data/sales.csv") + agent_log("tool_pyodide", f"analyze_csv: fetching {path} (Pyodide/Pandas — in-browser)") + try: + response = await fetch(path) + csv_text = await response.text() + agent_log("tool_pyodide", f"analyze_csv: loaded {len(csv_text)} bytes — running Pandas") + df = pd.read_csv(_io.StringIO(csv_text)) + + rows = len(df) + total_rev = df["revenue"].sum() + top_product = df.groupby("product")["revenue"].sum().idxmax() + top_rev = df.groupby("product")["revenue"].sum().max() + + try: + df["date"] = pd.to_datetime(df["date"]) + df["quarter"] = df["date"].dt.quarter + q_rev = df.groupby("quarter")["revenue"].sum() + qoq = ((q_rev.iloc[-1] - q_rev.iloc[-2]) / q_rev.iloc[-2] * 100) if len(q_rev) >= 2 else 0 + qoq_str = f"{qoq:+.1f}% QoQ" + except Exception: + qoq_str = "N/A" + + columns = ", ".join(df.columns.tolist()) + summary = ( + f"Columns: {columns} | " + f"Rows: {rows:,} | " + f"Total revenue: ${total_rev:,.0f} | " + f"Top product: {top_product} (${top_rev:,.0f}) | " + f"QoQ growth: {qoq_str}" + ) + agent_log("tool_pyodide", f"analyze_csv: done — {summary}") + return {"content": [{"type": "text", "text": summary}]} + + except Exception as e: + agent_log("error", f"analyze_csv: failed — {e}") + return {"content": [{"type": "text", "text": f"CSV analysis failed: {e}"}]} + + +# ============================================================================= +# MCP HTTP CLIENT +# ============================================================================= + +async def list_tools() -> list: + """Return MCP tool definitions from the server.""" + url = f"{MCP_SERVER_URL}/tools" + agent_log("mcp", f"list_tools: GET {url}") + try: + response = await fetch(url) + data = await response.json() + tools = data.get("tools", []) + agent_log("mcp", f"list_tools: {len(tools)} tool(s) — {', '.join(t['name'] for t in tools)}") + return tools + except Exception as e: + agent_log("error", f"list_tools: failed — {e}") + return [] + + +async def call_tool(name: str, arguments: dict) -> dict: + """Execute a tool by name. + + Dispatch order: + 1. analyze_csv → Pyodide (in-browser Pandas, no network) + 2. save_to_file → File System Access API / download (no backend) + 3. everything else → MCP HTTP server + """ + if name == "analyze_csv": + return await _call_analyze_csv(arguments) + + if name == "save_to_file": + filename = arguments.get("filename", "notes.md") + content = arguments.get("content", "") + agent_log("tool_pyodide", f"save_to_file: writing '{filename}' via File System Access API ({len(content)} chars)") + status = await save_file(filename, content) + agent_log("tool_pyodide", f"save_to_file: {status}") + return {"content": [{"type": "text", "text": status}]} + + url = f"{MCP_SERVER_URL}/call-tool" + agent_log("mcp", f"call_tool: POST {url} — {name}({json.dumps(arguments)})") + try: + response = await fetch( + url, + method="POST", + headers={"Content-Type": "application/json"}, + body=json.dumps({"name": name, "arguments": arguments}), + ) + result = await response.json() + texts = [i.get("text", "") for i in result.get("content", []) if i.get("type") == "text"] + preview = " ".join(texts)[:120] + agent_log("mcp", f"call_tool: {name} → {preview}") + return result + except Exception as e: + agent_log("error", f"call_tool {name}: {e}") + return {"content": [{"type": "text", "text": f"Tool error: {e}"}]} + + +# ============================================================================= +# SCHEMA HELPERS +# ============================================================================= + +def format_tools_for_llm(mcp_tools: list) -> list: + """Convert MCP tool defs to OpenAI function-calling format.""" + return [ + { + "type": "function", + "function": { + "name": tool["name"], + "description": tool["description"], + "parameters": tool.get("inputSchema", {"type": "object", "properties": {}}), + }, + } + for tool in mcp_tools + ] + + +def normalize_tool_calls(raw_tool_calls: list, source_tier: str) -> list: + """Normalize tool_call objects from different tier response shapes. + + OpenAI: [{id, type, function: {name, arguments}}] + Ollama: [{function: {name, arguments}}] (no id) + Returns: [{id, name, args: dict}] + """ + normalized = [] + for i, tc in enumerate(raw_tool_calls): + fn = tc.get("function", tc) + name = fn.get("name", "") + raw_args = fn.get("arguments", "{}") + try: + args = json.loads(raw_args) if isinstance(raw_args, str) else raw_args + except json.JSONDecodeError: + args = {"raw": raw_args} + normalized.append({ + "id": tc.get("id", f"call_{i}"), + "name": name, + "args": args, + }) + return normalized diff --git a/pyconUS-2026/demo/ui.py b/pyconUS-2026/demo/ui.py new file mode 100644 index 0000000..2da932d --- /dev/null +++ b/pyconUS-2026/demo/ui.py @@ -0,0 +1,183 @@ +"""UI helpers: messages, streaming, tier selector, tool-call pills.""" + +from pyscript import document + + +# ============================================================================= +# TIER SELECTOR +# ============================================================================= + +def get_selected_tier() -> str: + """Return the globally selected tier.""" + from pyscript import window + try: + value = window.getDemoTier() + if value: + return str(value) + except Exception: + pass + return "remote" + + +# ============================================================================= +# MESSAGES +# ============================================================================= + +def add_message(content, role="assistant", route=None, tool_calls=None): + messages_div = document.getElementById("messages") + msg = document.createElement("div") + msg.className = f"message {role}" + + html = "" + if route: + html += f'{route.upper()}
' + if tool_calls: + for tool in tool_calls: + html += ( + f'
' + f'🔧 {tool["name"]}({tool.get("args", {})})
' + ) + html += f'{content}' + msg.innerHTML = html + + messages_div.appendChild(msg) + messages_div.scrollTop = messages_div.scrollHeight + return msg + + +def add_streaming_message(route=None): + """Create an empty streaming bubble; update with update_streaming_message().""" + messages_div = document.getElementById("messages") + msg = document.createElement("div") + msg.className = "message assistant" + + html = "" + if route: + html += f'{route.upper()}
' + html += '' + msg.innerHTML = html + + messages_div.appendChild(msg) + messages_div.scrollTop = messages_div.scrollHeight + return msg + + +def update_streaming_message(msg, token): + content_span = msg.querySelector(".content") + content_span.textContent += token + msg.parentElement.scrollTop = msg.parentElement.scrollHeight + + +def finish_streaming_message(msg): + cursor = msg.querySelector(".streaming-cursor") + if cursor: + cursor.remove() + + +_PYODIDE_TOOLS = {"analyze_csv"} + +def add_tool_call_pill(tc): + """Render a tool-call pill. Pyodide tools get a purple PYODIDE badge; MCP tools get amber TOOL.""" + messages_div = document.getElementById("messages") + div = document.createElement("div") + div.className = "message tool-call-pill" + import json + args_str = json.dumps(tc.args, ensure_ascii=False) + if tc.name in _PYODIDE_TOOLS: + badge = 'PYODIDE' + icon = "🐍" + else: + badge = 'MCP TOOL' + icon = "🔧" + div.innerHTML = ( + f'{badge}
' + f'
' + f'{icon} {tc.name}({args_str})
' + ) + messages_div.appendChild(div) + messages_div.scrollTop = messages_div.scrollHeight + return div + + +def add_tool_result(tc, result_text): + """Render a collapsible tool result block.""" + messages_div = document.getElementById("messages") + div = document.createElement("div") + div.className = "message tool-result" + escaped = result_text.replace("<", "<").replace(">", ">") + div.innerHTML = ( + f'
🔍 Result from {tc.name}' + f'
{escaped}
' + ) + messages_div.appendChild(div) + messages_div.scrollTop = messages_div.scrollHeight + return div + + +def clear_messages(): + document.getElementById("messages").innerHTML = "" + try: + from pyscript import window + window.archClearDebug() + except Exception: + pass + + +# ============================================================================= +# STATUS BAR +# ============================================================================= + +def agent_log(type: str, text: str): + """Append an entry to the on-page agent log panel.""" + from pyscript import window + try: + window.agentLog(type, text) + except Exception: + pass + + +def agent_activate(node: str, edge: str = ""): + """Hold a node lit until agent_deactivate() is called. Optionally pulse an edge.""" + from pyscript import window + from pyodide.ffi import to_js + try: + window.agentActivate(to_js(node)) + if edge: + window.agentHighlight(to_js(""), to_js(edge)) + except Exception as e: + print(f"[agent_activate] error: {e}") + + +def agent_deactivate(node: str): + """Remove the persistent lit state from a node.""" + from pyscript import window + from pyodide.ffi import to_js + try: + window.agentDeactivate(to_js(node)) + except Exception as e: + print(f"[agent_deactivate] error: {e}") + + +def agent_highlight(node: str, edge: str = ""): + """Pulse a node briefly and/or flash an edge. Use agent_activate for sustained state.""" + from pyscript import window + from pyodide.ffi import to_js + try: + window.agentHighlight(to_js(node) if node else to_js(""), to_js(edge) if edge else to_js("")) + except Exception as e: + print(f"[agent_highlight] error: {e}") + + +def update_status(status, text): + label = document.getElementById("local-status-text") + if label: + label.textContent = text + + +def update_progress(pct: int, text: str): + bar = document.getElementById("progress-fill") + label = document.getElementById("progress-text") + if bar: + bar.style.width = f"{max(0, min(100, pct))}%" + if label: + label.textContent = text diff --git a/pyconUS-2026/slides/README.md b/pyconUS-2026/slides/README.md new file mode 100644 index 0000000..60fa1c8 --- /dev/null +++ b/pyconUS-2026/slides/README.md @@ -0,0 +1,82 @@ +# PyCon US 2026 — Slide deck + +Talk: **Distributing AI with Python in the Browser: Edge Inference and Flexibility Without Infrastructure** +30 minutes (25 + 5 Q&A) · AI track · Long Beach Convention Center + +Built with [Slidev](https://sli.dev). Slide source is `slides.md`; theming is in `style.css`; reusable diagrams are Vue components in `components/`. + +--- + +## Run + +```bash +cd slides +npm install +npm run dev # opens at http://localhost:3030 +``` + +Press `o` for overview, `f` for full-screen, `space` to advance. +Press `p` to open presenter mode (notes + timer on second screen). + +## Export PDF + +```bash +npm run export-pdf # → dist/pyconus-2026.pdf +``` + +(Slidev runs Playwright under the hood for export. First run will install Chromium.) + +--- + +## Structure (24 min content + 1 min slack) + +| # | Slide | Movement | Min | Cumulative | +|---|---|---|---|---| +| 1 | Title | Open | 0:30 | 0:30 | +| 2 | "An agent is not a chatbot. It's a loop." | Open | 0:50 | 1:20 | +| 3 | Speaker intro | Open | 0:25 | 1:45 | +| 4 | Section: What is an agent? | M1 | 0:15 | 2:00 | +| 5 | **Anatomy** — 4 parts × 2 rows | M1 | 1:45 | 3:45 | +| 6 | **The loop** — 4 boxes | M1 | 1:30 | 5:15 | +| 7 | Section: The loop, made visible | M2 | 0:15 | 5:30 | +| 8 | **Live Demo 3** — agent + tools (step mode) | M2 | 6:00 | 11:30 | +| 9 | Section: Where does the model run? | M3 | 0:15 | 11:45 | +| 10 | **Three tiers** | M3 | 2:00 | 13:45 | +| 11 | **Live Demo 1 ×3** — tier switching | M3 | 3:45 | 17:30 | +| 12 | Section: Why now & what does it cost | M4 | 0:15 | 17:45 | +| 13 | **Why now** — three numbers | M4 | 1:30 | 19:15 | +| 14 | **Trade-offs** — measured today | M4 | 2:30 | 21:45 | +| 15 | Don't use this for | M4 | 1:00 | 22:45 | +| 16 | **CTA** — clone, run, open | Close | 1:30 | 24:15 | +| 17 | Closing + Q&A | Close | 0:45 | 25:00 | + +**Anchor slides** (the ones to nail): +- 5 (anatomy), 6 (loop), 10 (three tiers), 13 (why now), 14 (trade-offs), 16 (CTA). +- Slides 8 and 11 are demo handoffs — the slide is a prop, the live tab is the show. + +--- + +## Pre-talk checklist (the morning of) + +1. **Refresh the numbers.** Run `python spike/measure_metrics.py`; copy fresh values into `metrics_snapshot.json` and into slide 10 / slide 14 (search for `~210`, `~85`, `~11`, `8.2 s`, `11.4 MB`). +2. **Pre-warm the demo.** Open `http://localhost:8000` in a hidden tab. Click Demo 1 once on the in-browser tier so WebLLM is cached. Open Demo 3 in step mode, ready to go. +3. **Verify all three tier dots are green.** Health probe in the demo footer. +4. **Drop the QR code.** Replace the placeholder in slide 16 with a real QR PNG/SVG pointing at the public repo URL (use `qrencode` or any generator). +5. **Speaker links.** Update `@bugzpodder` and the GitHub URL on slides 3 and 17 if the handles changed. +6. **Backup recording.** Have the Demo 3 fallback video on a phone or second device, queued. + +--- + +## Customization notes + +- **Colors:** all six tier/role colors are CSS vars in `style.css` (`--browser`, `--local`, `--remote`, `--tool`, `--pyodide`, `--hybrid`). They mirror `demo/index.html` exactly so the deck → demo cut is seamless. +- **Diagrams:** `components/LoopDiagram.vue` accepts a `:step="1..4"` prop to highlight one node — useful if you want to walk through it click-by-click. `components/ArchDiagram.vue` accepts `lit-node` and `lit-edge` props. +- **Section headers** (slides 4, 7, 9, 12) are intentionally minimal — they're a breath, not content. Cut them ruthlessly if you're running long. + +--- + +## Out of scope + +- Training pipelines, fine-tuning, RAG, embeddings — none of it appears in the deck. If asked, defer to Q&A. +- Multi-agent / planner-executor / ReAct frameworks — same. +- WebNN — single Q&A note in slide 17 speaker notes; otherwise ignored. diff --git a/pyconUS-2026/slides/components/AgentDiagram.vue b/pyconUS-2026/slides/components/AgentDiagram.vue new file mode 100644 index 0000000..00514bf --- /dev/null +++ b/pyconUS-2026/slides/components/AgentDiagram.vue @@ -0,0 +1,150 @@ + + + + diff --git a/pyconUS-2026/slides/components/ArchDiagram.vue b/pyconUS-2026/slides/components/ArchDiagram.vue new file mode 100644 index 0000000..8f17396 --- /dev/null +++ b/pyconUS-2026/slides/components/ArchDiagram.vue @@ -0,0 +1,130 @@ + + + + + + diff --git a/pyconUS-2026/slides/components/LLMDiagram.vue b/pyconUS-2026/slides/components/LLMDiagram.vue new file mode 100644 index 0000000..127438c --- /dev/null +++ b/pyconUS-2026/slides/components/LLMDiagram.vue @@ -0,0 +1,107 @@ + + + + diff --git a/pyconUS-2026/slides/components/LoopDiagram.vue 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} + } +} diff --git a/pyconUS-2026/slides/package.json b/pyconUS-2026/slides/package.json new file mode 100644 index 0000000..da213fd --- /dev/null +++ b/pyconUS-2026/slides/package.json @@ -0,0 +1,17 @@ +{ + "name": "pyconus-2026-slides", + "private": true, + "type": "module", + "scripts": { + "dev": "slidev --open", + "build": "slidev build", + "export": "slidev export", + "export-pdf": "slidev export --output dist/pyconus-2026.pdf" + }, + "dependencies": { + "@slidev/cli": "^0.50.0", + "@slidev/theme-default": "^0.25.0", + "@slidev/theme-seriph": "^0.25.0", + "vue": "^3.5.0" + } +} diff --git a/pyconUS-2026/slides/slides.md b/pyconUS-2026/slides/slides.md new file mode 100644 index 0000000..6ff4a83 --- /dev/null +++ b/pyconUS-2026/slides/slides.md @@ -0,0 +1,830 @@ +--- +# ───────────────────────────────────────────────────────────── +# PyCon US 2026 — Distributing AI with Python in the Browser +# Talk format: 25 + 5 (Q&A) · AI track · Long Beach +# Built with Slidev (https://sli.dev) +# +# Run: npm install && npm run dev +# Export: npm run export-pdf +# ───────────────────────────────────────────────────────────── +theme: default +title: Distributing AI with Python in the Browser +info: | + PyCon US 2026 — AI track. + Edge inference and flexibility without infrastructure. +class: text-center +highlighter: shiki +lineNumbers: false +drawings: + persist: false +mdc: true +fonts: + sans: 'Inter' + mono: 'JetBrains Mono' +colorSchema: dark +download: true +exportFilename: pyconus-2026-distributing-ai +--- + + + +
+
PyCon US 2026
+

+Distributing AI
+with Python in the Browser +

+

inference agents & flexibility, everywhere.

+

Fabio Pliger · Anaconda · @b_smoke

+
+ + + +--- +title: Thesis +layout: center +class: text-center +--- + +
+Sorry in advance...
+We'll talk about
+agents +not models +
+ +
+It's 2026, agents are the new cool :) +
+ +
00:30 · t+00:30
+ + + +--- +title: Speaker intro +layout: center +--- + +
+
+
Who am I
+

Fabio Pliger

+

Engineer Anaconda - R&D Team. Creator of PyScript and long time Pythonista.

+

+
+
+
github.com/fpliger
+
x · @b_smoke
+
anaconda.com
+
pyscript.net
+
+
+ +
00:45 · t+01:15
+ + + +--- +title: M1 — What is an agent? +layout: center +class: text-center +--- + + + +
+What is
+an agent? +
+ + + +
00:15 · t+02:00
+ + + +--- +title: Anatomy — four parts +--- + +
Anatomy of an agent
+

Four parts.

+ +
+
+
Model
+
Tools
+
Context
+
Loop
+
In general
+
+
decides
+
The "brain" thing that picks what to do next. An LLM, mostly.
+
+
+
acts
+
Added functionality the model is allowed to call. APIs (MCPs), scripts, services.
+
+
+
remembers
+
What (custom information) the model sees on each turn. Instructions, history, results.
+
+
+
drives
+
Ask · act · observe · repeat. The runtime.
+
+
+ +
01:45 · t+03:45
+ + + +--- +title: Anatomy (Continued) — four parts +--- + +
Anatomy of an agent (continued)
+

Four parts.

+ +
+
+
Model
+
Tools
+
Context
+
Loop
+
In general
+
+
decides
+
The "brain" thing that picks what to do next. An LLM, mostly.
+
+
+
acts
+
Added functionality the model is allowed to call. APIs, scripts, services.
+
+
+
remembers
+
What (custom information) the model sees on each turn. Instructions, history, results.
+
+
+
drives
+
Ask · act · observe · repeat. The runtime.
+
+
In the browser
+
+
decides
+
In-browser SLM (WebGPU), local server, or remote API.
+
+
+
acts
+
MCP servers (HTTP), Python (JS, WASM, ...) tools (code), plus the DOM.
+
+
+
remembers
+
messages[], plus DOM, sandboxed files, browser memory.
+
+
+
drives
+
PyScript (WASM|JS|...) Python running in the user's tab.
+
+
Same architecture. Different implementation details.
+
+ +
01:45 · t+03:45
+ + + + +--- +title: Anatomy — LLM +--- + +
+
Anatomy of an agent
+

Start with an LLM.

+
+ +
+
+ +
01:00 · t+03:45
+ + + +--- +title: Anatomy — Agent +--- + +
+
Anatomy of an agent
+

Add the loop.

+
+ +
+
+ +
01:00 · t+04:45
+ + + +--- +title: M2 — The loop, made visible +layout: center +class: text-center +--- + + + +
+The
+loop +
+ +
Live demo · step-through
+ +
00:15 · t+05:30
+ + + +--- +title: Demo — agent + tools +layout: full +--- + +
+
+
What to watch
+

+Demo — agent + tools
+step mode · remote tier +

+
    +
  1. ①User prompt → agent
    2. +
  2. ②agent asks LLM with tools
    3. +
  3. ③LLM returns tool call — web_search, analyze_csv, ...
    4. +
  4. ④agent runs MCP tool, appends result
    5. +
  5. ⑤LLM calls next tool — save_to_file
    6. +
  6. ⑥File on your real disk. No backend.
    7. +
+

+→ Watch the arch panel light up.
+→ Watch messages[] grow in Context tab. +

+
+
+ +
+
+ +
06:00 · t+11:30
+ + + +--- +title: M3 — Where does the model run? +layout: center +class: text-center +--- + + + +
+Where does
+the model run? +
+ + + +
00:15 · t+11:45
+ + + +--- +title: Three tiers +--- + +
Three places the model can run
+

Same loop. Different Scenarios.

+ +
+
+
In-browser
+

WebGPU + WebLLM

+ +
Qwen2.5 1.5B, ~850 MB, downloads once
Runs on the user's GPU. Offline (cached) after first load.
No data leaves the device.
+
+
+
Local
+

Ollama / Anaconda Desktop / Agent Studio

+ +
Qwen3 8B via localhost
Same machine, capable model, real tool calling.
No data leaves the device.
+
+
+
Remote
+

Remote API

+ +
Claude / GPT via fetch
Best quality. Lowest latency. Costs per token.
Data crosses the network.
+
+
+ +

Same Python agent. Same stream_chat(messages, tools). Three implementations behind it.

+ +
02:00 · t+13:45
+ + + +--- +title: Demo — CSV analysis +layout: full +--- + +
+
+
What to watch
+

+Demo — CSV analysis
+Pyodide Pandas · any tier +

+
    +
  1. ①Pandas runs in the browser — no data leaves the tab
    2. +
  2. ②LLM receives the numeric summary as a tool result
    3. +
  3. ③LLM streams an executive summary
    4. +
  4. ④Switch tiers — same result, different latency
    5. +
+

+→ Watch Pyodide light up in the arch panel.
+→ Watch the context tab grow step by step. +

+
+
+ +
+
+ +
03:00 · t+14:45
+ + + +--- +title: Demo — folder agent +layout: full +--- + +
+
+
What to watch
+

+Demo — folder agent
+load agent.yaml from disk · remote tier +

+
    +
  1. ①Click Demo → browser opens a folder picker
    2. +
  2. ②Select fred-example-agent/ from disk
    3. +
  3. ③Python reads agent.yaml + documents/ in the browser
    4. +
  4. ④System prompt & docs injected into messages[]
    5. +
  5. ⑤Agent introduces itself — then chat with Fred
    6. +
+

+→ No server. No upload. No account.
+→ The folder never leaves your machine. +

+
+
+
fred-example-agent/
+
+
agent.yaml
+
name · description · system_prompt
+
ai: provider · model · temperature
+
+
+
documents/
+
reference docs injected into context
+
+
+
tools/mcp.json
+
MCP server config (future)
+
+
+
+ +
03:00 · t+17:45
+ + + +--- +title: M4 — Why now? +layout: center +class: text-center +--- + + + +
+Why now?
+ +
+ +
00:15 · t+17:45
+ + + +--- +title: Why now — three numbers +--- + +
Why this works in 2026
+

Three things shifted.

+ +
+
+
4 / 4
+
WebGPU shipped in every major browserChrome · Edge · Firefox · Safari — first time, November 2025.
+
+
+
8B
+
Small models do tool callingPhi-4-mini, Gemma 4, Qwen 3.5 — runs on a laptop, calls tools.
+
+
+
97M/mo
+
MCP became a standardAnthropic SDK downloads. 9k+ public MCP servers.
+
+
Each one alone wouldn't move the needle. Together, they unlock this.
+
+ +
01:30 · t+19:15
+ + + +--- +title: Trade-offs — measured +--- + +
Honest trade-offs · measured today
+

When to use each.

+ + + + + + + + + + + + + + +
DimensionIn-browserLocalRemote
Latency (TTFT)~slow~slowish~fast
Model ceiling1–3 B7–70 Bfrontier
Tool-call qualitypoor at 1Bgood at 8B+excellent
Privacyon-deviceon-devicedata leaves
Cost / 1k tokens$0$0$0.001–0.03
First-load weight~700 MB modelpre-installednone
Offline capableafter 1st loadalwaysneeds net
+ + + +
02:30 · t+21:45
+ + + +--- +title: Don't use this for +--- + +
Don't use this for
+

Where this is the wrong tool.

+ +
+
+
DON'T
+Multi-tenant high-throughput backends. +

Each tab pays the bundle cost. Servers exist for a reason.

+
+
+
DON'T
+Multi-GB frontier models locally. +

The browser isn't going to run a 70B model well. Don't pretend it will.

+
+
+
DON'T
+Hard SLA on cold-start latency. +

First load is 8 seconds and a 700 MB download. Tell users that.

+
+
+
DON'T
+A constrained device. +

Old phones. Low-RAM laptops. The user's device is the infra.

+
+
+ +
01:00 · t+22:45
+ + + +--- +title: CTA — try it +layout: center +--- + +
+
+
Take it home
+

Try it. (soon! 🙈)

+

Soon to be available on Github

+ +
+
+ +
github.com/fpliger/
pyconus-2026-agent
+
+
+ +
01:30 · t+24:15
+ + + +--- +title: Closing1 — Q&A +layout: center +class: text-center +--- + +
+An agent is a loop.
+Python writes loops.
+The browser ships them. +
+ + + +
00:45 · t+25:00 · Q&A starts
+ + + + +--- +title: Closing — Q&A +layout: center +class: text-center +--- + +
+Thank you! +
+ +
Questions?
+ +
+@b_smoke +github.com/fpliger +pyscript.net +
+ +
00:45 · t+25:00 · Q&A starts
+ + diff --git a/pyconUS-2026/slides/style.css b/pyconUS-2026/slides/style.css new file mode 100644 index 0000000..9d3ff22 --- /dev/null +++ b/pyconUS-2026/slides/style.css @@ -0,0 +1,417 @@ +/* ───────────────────────────────────────────────────────────── + Global styles for the PyCon US 2026 talk deck. + Palette mirrors the demo (demo/index.html) so the cut from + slides → live demo is visually seamless. + ───────────────────────────────────────────────────────────── */ + +/* Hide the Goto dialog by default; shown when .goto-open is on */ +#slidev-goto-dialog { display: none; } +html.goto-open #slidev-goto-dialog { display: block !important; } + +:root { + --browser: #1de9b6; + --local: #69f0ae; + --remote: #448aff; + --hybrid: #e040fb; + --tool: #ffab40; + --pyodide: #a78bfa; + + --bg: #0d0f14; + --surface: #161920; + --surface2: #1e2129; + --border: #2a2d36; + --text: #e2e4ea; + --muted: #6b7280; + --code-bg: #11131a; + + --font-sans: 'Inter', ui-sans-serif, system-ui, sans-serif; + --font-mono: 'JetBrains Mono', ui-monospace, monospace; +} + +html, body, #app, .slidev-layout { + background: var(--bg); + color: var(--text); + font-family: var(--font-sans); +} + +.slidev-layout { + padding: 3rem 4rem; +} + +.slidev-layout h1, +.slidev-layout h2, +.slidev-layout h3 { + color: #fff; + letter-spacing: -0.02em; + font-weight: 600; +} + +.slidev-layout code { + font-family: var(--font-mono); + background: var(--code-bg); + padding: 0.1em 0.35em; + border-radius: 3px; + font-size: 0.92em; +} + +.slidev-layout pre { + background: var(--code-bg) !important; + border: 1px solid var(--border); + border-radius: 8px; + padding: 0.85rem 1rem !important; + font-family: var(--font-mono); +} + +.slidev-page-number, +.slidev-presenter-cursor { + color: var(--muted); +} + +/* ── Tier color helpers ───────────────────────────────────── */ +.tier-browser { color: var(--browser); } +.tier-local { color: var(--local); } +.tier-remote { color: var(--remote); } +.tier-tool { color: var(--tool); } +.tier-pyodide { color: var(--pyodide); } + +.mono { font-family: var(--font-mono); } + +/* ── Big-text slides (thesis, section headers) ────────────── */ +.big-thesis { + font-size: 4rem; + line-height: 1.1; + font-weight: 700; + letter-spacing: -0.03em; + color: #fff; + max-width: 24ch; +} + +.big-thesis .accent { + background: linear-gradient(90deg, var(--browser), var(--local)); + -webkit-background-clip: text; + -webkit-text-fill-color: transparent; + background-clip: text; +} + +.section-eyebrow { + font-family: var(--font-mono); + font-size: 0.85rem; + letter-spacing: 0.18em; + color: var(--muted); + text-transform: uppercase; + margin-bottom: 1rem; +} + +/* ── Anatomy grid ─────────────────────────────────────────── */ +.anatomy { + display: grid; + grid-template-columns: 8rem repeat(4, 1fr); + grid-template-rows: auto auto auto; + gap: 0.75rem; + font-size: 0.95rem; +} +.anatomy .row-label { + display: flex; + align-items: center; + font-family: var(--font-mono); + font-size: 0.72rem; + letter-spacing: 0.15em; + text-transform: uppercase; + color: var(--muted); +} +.anatomy .col-head { + text-align: center; + font-weight: 600; + font-size: 1.1rem; + padding: 0.25rem 0; + border-bottom: 2px solid var(--border); +} +.anatomy .cell { + background: var(--surface); + border: 1px solid var(--border); + border-radius: 10px; + padding: 1rem 0.9rem; + display: flex; + flex-direction: column; + gap: 0.25rem; + min-height: 7.5rem; +} +.anatomy .cell .label { + font-family: var(--font-mono); + font-size: 0.7rem; + letter-spacing: 0.12em; + text-transform: uppercase; +} +.anatomy .cell .desc { + font-size: 0.95rem; + color: var(--text); + line-height: 1.45; +} +.anatomy .cell.model { border-color: color-mix(in srgb, var(--remote) 40%, var(--border)); } +.anatomy .cell.model .label { color: var(--remote); } +.anatomy .cell.tools { border-color: color-mix(in srgb, var(--tool) 40%, var(--border)); } +.anatomy .cell.tools .label { color: var(--tool); } +.anatomy .cell.loop { border-color: color-mix(in srgb, var(--local) 40%, var(--border)); } +.anatomy .cell.loop .label { color: var(--local); } +.anatomy .cell.context { border-color: color-mix(in srgb, var(--pyodide) 40%, var(--border)); } +.anatomy .cell.context .label { color: var(--pyodide); } + +.anatomy .invariant { + grid-column: 1 / -1; + margin-top: 0.5rem; + text-align: center; + font-family: var(--font-mono); + font-size: 0.85rem; + color: var(--muted); + letter-spacing: 0.05em; +} +.anatomy .invariant strong { color: var(--text); } + +/* ── Three-tier slide ─────────────────────────────────────── */ +.tiers { + display: grid; + grid-template-columns: repeat(3, 1fr); + gap: 1.25rem; +} +.tiers .tier { + background: var(--surface); + border: 2px solid var(--border); + border-radius: 14px; + padding: 1.5rem 1.25rem; + display: flex; + flex-direction: column; + gap: 0.85rem; + min-height: 22rem; + position: relative; +} +.tiers .tier .tier-head { + display: flex; + align-items: center; + gap: 0.6rem; + font-family: var(--font-mono); + font-size: 0.78rem; + letter-spacing: 0.12em; + text-transform: uppercase; + font-weight: 600; +} +.tiers .tier .tier-head .dot { + width: 10px; height: 10px; border-radius: 50%; +} +.tiers .tier h3 { + font-size: 1.5rem; + margin: 0; +} +.tiers .tier .ttft { + font-family: var(--font-mono); + font-size: 2.4rem; + font-weight: 700; + letter-spacing: -0.03em; + line-height: 1; +} +.tiers .tier .ttft small { + display: block; + font-size: 0.7rem; + font-weight: 400; + color: var(--muted); + letter-spacing: 0.1em; + text-transform: uppercase; + margin-top: 0.3rem; +} +.tiers .tier .meta { + margin-top: auto; + font-size: 0.85rem; + color: var(--muted); + line-height: 1.5; +} +.tiers .tier .meta strong { color: var(--text); font-weight: 500; } + +.tiers .tier.browser { border-color: color-mix(in srgb, var(--browser) 50%, var(--border)); } +.tiers .tier.browser .dot { background: var(--browser); box-shadow: 0 0 14px var(--browser); } +.tiers .tier.browser .tier-head, .tiers .tier.browser .ttft { color: var(--browser); } + +.tiers .tier.local { border-color: color-mix(in srgb, var(--local) 50%, var(--border)); } +.tiers .tier.local .dot { background: var(--local); box-shadow: 0 0 14px var(--local); } +.tiers .tier.local .tier-head, .tiers .tier.local .ttft { color: var(--local); } + +.tiers .tier.remote { border-color: color-mix(in srgb, var(--remote) 50%, var(--border)); } +.tiers .tier.remote .dot { background: var(--remote); box-shadow: 0 0 14px var(--remote); } +.tiers .tier.remote .tier-head, .tiers .tier.remote .ttft { color: var(--remote); } + +/* ── Why-now numbers ──────────────────────────────────────── */ +.why-now { + display: flex; + flex-direction: column; + gap: 1.25rem; +} +.why-now .row { + display: grid; + grid-template-columns: 14rem 1fr; + gap: 2rem; + align-items: center; + padding: 1.25rem 0; + border-bottom: 1px solid var(--border); +} +.why-now .row:last-child { border-bottom: none; } +.why-now .num { + font-family: var(--font-mono); + font-size: 3rem; + font-weight: 700; + letter-spacing: -0.04em; + line-height: 1; + color: var(--browser); +} +.why-now .row.r2 .num { color: var(--local); } +.why-now .row.r3 .num { color: var(--tool); } +.why-now .label { + font-size: 1.4rem; + line-height: 1.3; + color: var(--text); +} +.why-now .label small { + display: block; + font-size: 0.85rem; + color: var(--muted); + margin-top: 0.3rem; + font-weight: 400; +} +.why-now .punch { + text-align: center; + font-family: var(--font-mono); + font-size: 0.9rem; + color: var(--muted); + letter-spacing: 0.04em; + margin-top: 1.25rem; +} +.why-now .punch strong { color: var(--text); } + +/* ── Trade-offs table ─────────────────────────────────────── */ +.tradeoffs { + width: 100%; + border-collapse: collapse; + font-size: 0.9rem; +} +.tradeoffs th { + text-align: left; + padding: 0.6rem 0.8rem; + font-family: var(--font-mono); + font-size: 0.72rem; + letter-spacing: 0.12em; + text-transform: uppercase; + color: var(--muted); + border-bottom: 2px solid var(--border); + font-weight: 600; +} +.tradeoffs th:not(:first-child) { text-align: center; } +.tradeoffs td { + padding: 0.65rem 0.8rem; + border-bottom: 1px solid var(--border); + vertical-align: middle; +} +.tradeoffs td:not(:first-child) { text-align: center; } +.tradeoffs td:first-child { + color: var(--muted); + font-family: var(--font-mono); + font-size: 0.78rem; + text-transform: uppercase; + letter-spacing: 0.05em; +} +.tradeoffs .pill { + display: inline-block; + padding: 0.15rem 0.55rem; + border-radius: 4px; + font-family: var(--font-mono); + font-size: 0.78rem; + font-weight: 600; +} +.pill.green { background: color-mix(in srgb, var(--local) 20%, transparent); color: var(--local); } +.pill.yellow { background: color-mix(in srgb, #ffc107 20%, transparent); color: #ffc107; } +.pill.red { background: color-mix(in srgb, #ef5350 20%, transparent); color: #ef5350; } +.pill.blue { background: color-mix(in srgb, var(--remote) 20%, transparent); color: var(--remote); } +.pill.teal { background: color-mix(in srgb, var(--browser) 20%, transparent); color: var(--browser); } + +/* ── CTA / QR slide ───────────────────────────────────────── */ +.cta { + display: grid; + grid-template-columns: 1fr 18rem; + gap: 3rem; + align-items: center; +} +.cta h2 { + font-size: 3rem; + letter-spacing: -0.03em; + margin-bottom: 1rem; +} +.cta ol { + font-family: var(--font-mono); + font-size: 1.1rem; + list-style: none; + padding: 0; + margin: 1rem 0 0; +} +.cta ol li { + padding: 0.6rem 0; + border-bottom: 1px solid var(--border); + display: flex; + align-items: baseline; + gap: 0.8rem; +} +.cta ol li::before { + content: '0' counter(cta-step); + counter-increment: cta-step; + color: var(--muted); + font-size: 0.8rem; +} +.cta ol { counter-reset: cta-step; } +.cta .qr-card { + background: var(--surface); + border: 1px solid var(--border); + border-radius: 14px; + padding: 1.25rem; + text-align: center; +} +.cta .qr-card .qr { + width: 100%; + aspect-ratio: 1; + background: #fff; + border-radius: 8px; + display: flex; + align-items: center; + justify-content: center; + font-family: var(--font-mono); + font-size: 0.7rem; + color: #888; +} +.cta .qr-card .url { + font-family: var(--font-mono); + font-size: 0.78rem; + color: var(--muted); + margin-top: 0.6rem; + word-break: break-all; +} + +/* ── Misc ─────────────────────────────────────────────────── */ +.timing { + position: absolute; + top: 1rem; + right: 1.25rem; + font-family: var(--font-mono); + font-size: 0.65rem; + color: var(--muted); + opacity: 0.55; + letter-spacing: 0.08em; +} + +.demo-frame { + width: 100%; + height: 100%; + border: 1px solid var(--border); + border-radius: 12px; + background: var(--code-bg); +} + +.center-vh { + display: flex; + align-items: center; + justify-content: center; + min-height: 60vh; +}