Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
Fastapi Studio Template
Bootstrap a dark-themed FastAPI+HTMX studio app with SSE real-time progress, blind test mode, SQLite ratings, and Langfuse tracing. Based on the image-gen-st...
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Bootstrap a dark-themed FastAPI+HTMX studio app with SSE real-time progress, blind test mode, SQLite ratings, and Langfuse tracing. Based on the image-gen-st...
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- SKILL.md
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Release facts
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- Version
- 1.2.1
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Documentation
FastAPI Studio Template
Bootstrap a dark-themed FastAPI + HTMX studio app for generative AI comparison, A/B testing, and human evaluation with real-time progress streaming.
When to Use
Any "studio" app: image generation comparison, text model A/B testing, human evaluation UI Apps needing real-time progress updates (generation can take 30sโ15min) Blind test / evaluation interfaces where raters shouldn't know which model produced which output Rapid prototyping of gen AI comparison tools
When NOT to Use
Simple CRUD apps (use standard FastAPI + Jinja2) Apps that don't need real-time progress (SSE adds complexity) Production-scale apps with 100+ concurrent users (use WebSockets instead of SSE)
SSE Async Pattern (Critical)
MUST use threading.SimpleQueue + asyncio polling. Do NOT use run_in_executor with blocking reads โ it deadlocks the event loop. import asyncio import threading from queue import SimpleQueue from fastapi import FastAPI from fastapi.responses import StreamingResponse app = FastAPI() async def event_stream(queue: SimpleQueue): """Yield SSE events from a thread-safe queue.""" while True: try: msg = queue.get_nowait() except Exception: await asyncio.sleep(0.1) continue if msg is None: # sentinel yield f"data: {{\"done\": true}}\n\n" break yield f"data: {msg}\n\n" @app.get("/generate/stream") async def generate_stream(prompt: str, model: str): queue = SimpleQueue() def _run(): # Heavy generation work in background thread for step in range(10): import time; time.sleep(1) queue.put(f'{{"step": {step}, "total": 10}}') queue.put(None) # done sentinel threading.Thread(target=_run, daemon=True).start() return StreamingResponse(event_stream(queue), media_type="text/event-stream") Why not run_in_executor? FastAPI's executor runs on a thread pool, but SSE needs to yield events incrementally. Blocking in the executor means you can't stream partial progress โ you'd have to wait for the entire generation to finish. The queue pattern decouples generation from streaming.
Blind Test Mode
Generate N variants (one per model), randomise display order, reveal model identity only after the user rates all variants. import random import uuid def create_blind_test(prompt: str, models: list[str]) -> dict: test_id = str(uuid.uuid4()) variants = [] for model in models: variants.append({ "variant_id": str(uuid.uuid4()), "model": model, # hidden from UI until reveal "prompt": prompt, }) random.shuffle(variants) return { "test_id": test_id, "variants": variants, "display_order": [v["variant_id"] for v in variants], } In the HTMX frontend, render variants as "Option A", "Option B", etc. On rating submission, return the mapping from option letters to model names.
Hot-Loaded Model Singleton (ModelRegistry)
Cold-loading SDXL or similar models takes 6โ14 minutes. Cache loaded models in a registry singleton. class ModelRegistry: _instance = None _models: dict = {} _lock = threading.Lock() @classmethod def get(cls, model_name: str): with cls._lock: if model_name not in cls._models: cls._models[model_name] = cls._load_model(model_name) return cls._models[model_name] @classmethod def _load_model(cls, name: str): # Import and load the model if name == "sdxl": from mflux import Flux1 return Flux1.from_alias("schnell", quantize=8) raise ValueError(f"Unknown model: {name}") Preload at startup via the FastAPI lifespan hook for models you know you'll need.
float32 Requirement for SDXL on MPS
torch 2.10 on Apple Silicon (MPS) produces NaN outputs with float16 for SDXL. Force float32: import torch torch.set_default_dtype(torch.float32) # or per-model: model = model.to(dtype=torch.float32) This doubles VRAM usage but is the only reliable option until the MPS float16 bug is fixed.
SQLite Schema for Ratings
CREATE TABLE ratings ( id INTEGER PRIMARY KEY AUTOINCREMENT, test_id TEXT NOT NULL, variant_id TEXT NOT NULL, model TEXT NOT NULL, rater TEXT DEFAULT 'anonymous', score INTEGER CHECK(score BETWEEN 1 AND 5), preferred BOOLEAN DEFAULT FALSE, -- winner of pairwise comparison notes TEXT, created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ); CREATE INDEX idx_ratings_test ON ratings(test_id); CREATE INDEX idx_ratings_model ON ratings(model);
Langfuse Tracing
Wrap generation calls with Langfuse traces for cost tracking and latency monitoring: from langfuse import Langfuse langfuse = Langfuse() def generate_with_trace(prompt, model_name): trace = langfuse.trace(name="studio-generation", metadata={"model": model_name}) span = trace.span(name="generate", input={"prompt": prompt}) result = ModelRegistry.get(model_name).generate(prompt) span.end(output={"length": len(result)}) return result
Worked Example: Minimal Studio App
"""Minimal FastAPI+HTMX studio with SSE progress.""" import asyncio import json import threading from queue import SimpleQueue from pathlib import Path from fastapi import FastAPI, Request from fastapi.responses import HTMLResponse, StreamingResponse from fastapi.staticfiles import StaticFiles app = FastAPI() HTML = """ <!DOCTYPE html> <html> <head> <title>Studio</title> <script src="https://unpkg.com/htmx.org@1.9.12"></script> <script src="https://unpkg.com/htmx.org@1.9.12/dist/ext/sse.js"></script> <style> body { background: #1a1a2e; color: #e0e0e0; font-family: system-ui; padding: 2rem; } .card { background: #16213e; border-radius: 8px; padding: 1.5rem; margin: 1rem 0; } button { background: #0f3460; color: white; border: none; padding: 0.75rem 1.5rem; border-radius: 4px; cursor: pointer; } button:hover { background: #533483; } input, textarea { background: #0f3460; color: white; border: 1px solid #333; padding: 0.5rem; border-radius: 4px; width: 100%; } #progress { color: #e94560; } </style> </head> <body> <h1>๐จ Studio</h1> <div class="card"> <textarea id="prompt" placeholder="Enter prompt..." rows="3"></textarea> <br><br> <button onclick="startGeneration()">Generate</button> </div> <div id="progress" class="card" style="display:none"></div> <div id="results" class="card" style="display:none"></div> <script> function startGeneration() { const prompt = document.getElementById('prompt').value; const progress = document.getElementById('progress'); progress.style.display = 'block'; progress.textContent = 'Starting...'; const source = new EventSource('/generate/stream?prompt=' + encodeURIComponent(prompt)); source.onmessage = (e) => { const data = JSON.parse(e.data); if (data.done) { source.close(); progress.textContent = 'Done!'; } else { progress.textContent = `Step ${data.step}/${data.total}`; } }; } </script> </body> </html> """ @app.get("/", response_class=HTMLResponse) async def index(): return HTML async def event_stream(queue: SimpleQueue): while True: try: msg = queue.get_nowait() except Exception: await asyncio.sleep(0.1) continue if msg is None: yield f"data: {json.dumps({'done': True})}\n\n" break yield f"data: {msg}\n\n" @app.get("/generate/stream") async def generate_stream(prompt: str): queue = SimpleQueue() def _run(): import time for i in range(10): time.sleep(0.5) queue.put(json.dumps({"step": i + 1, "total": 10})) queue.put(None) threading.Thread(target=_run, daemon=True).start() return StreamingResponse(event_stream(queue), media_type="text/event-stream") Run with: uvicorn app:app --reload --port 8000
Tips
Dark theme first โ gen AI studios are used in long sessions; light themes cause eye strain Always show progress โ users will close the tab if they think it's frozen Log every generation โ Langfuse traces are invaluable for debugging quality issues Rate-limit generation โ SDXL on MPS can only do one image at a time; queue requests Export ratings as CSV โ researchers need data in portable formats
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
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Package contents
Included in package
1 Docs
- SKILL.md