* Rebuild Studio branch on top of main * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix security and code quality issues for Studio PR #4237 - Validate models_dir query param against allowed directory roots to prevent path traversal in /api/models/local endpoint - Replace string startswith() with Path.is_relative_to() for frontend path traversal check in serve_frontend - Sanitize SSE error messages to not leak exception details to clients (4 locations in inference.py) - Bind port-discovery socket to 127.0.0.1 instead of all interfaces in llama_cpp backend - Import datasets_root and resolve_output_dir in embedding training function to fix NameError and use managed output directory - Remove stale .gitignore entries for package-lock.json and test directories so tests can be tracked in version control - Add venv-reexecution logic to ui CLI command matching the studio command behavior * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Move models_dir path validation before try/except block The HTTPException(403) was inside the try/except Exception handler, so it would be caught and re-raised as a 500. Moving the validation before the try block ensures the 403 is returned directly and also makes the control flow clearer for static analysis (path is validated before any filesystem operations). * Use os.path.realpath + startswith for models_dir validation CodeQL py/path-injection does not recognize Path.is_relative_to() as a sanitizer. Switched to os.path.realpath + str.startswith which is a recognized sanitizer pattern in CodeQL's taint analysis. The startswith check uses root_str + os.sep to prevent prefix collisions (e.g. /app/models_evil matching /app/models). * Never pass user input to Path constructor in models_dir validation CodeQL traces taint through Path(resolved) even after a startswith barrier guard. Fix: the user-supplied models_dir is only used as a string for comparison against allowed roots. The Path object passed to _scan_models_dir comes from the trusted allowed_roots list, not from user input. This fully breaks the taint chain. --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
258 lines
8.1 KiB
Python
258 lines
8.1 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
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# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
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"""
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Main FastAPI application for Unsloth UI Backend
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"""
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import os
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# Suppress annoying C-level dependency warnings globally
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os.environ["PYTHONWARNINGS"] = "ignore"
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import secrets
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import shutil
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import warnings
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from contextlib import asynccontextmanager
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# Suppress annoying dependency warnings in production
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if os.getenv("ENVIRONMENT_TYPE", "production") == "production":
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warnings.filterwarnings("ignore")
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# Alternatively, you can be more specific:
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# warnings.filterwarnings("ignore", category=DeprecationWarning)
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# warnings.filterwarnings("ignore", module="triton.*")
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from fastapi import FastAPI
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.staticfiles import StaticFiles
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from fastapi.responses import FileResponse, HTMLResponse, Response
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from pathlib import Path
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from datetime import datetime
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# Import routers
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from routes import (
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auth_router,
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data_recipe_router,
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datasets_router,
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export_router,
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inference_router,
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models_router,
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training_router,
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)
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from auth import storage
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from utils.hardware import detect_hardware, get_device, DeviceType
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import utils.hardware.hardware as _hw_module
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from utils.cache_cleanup import clear_unsloth_compiled_cache
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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"""Startup: detect hardware, print setup token if needed. Shutdown: clean up compiled cache."""
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# Clean up any stale compiled cache from previous runs
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clear_unsloth_compiled_cache()
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# Remove stale .venv_overlay from previous versions — no longer used.
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# Version switching now uses .venv_t5/ (pre-installed by setup.sh).
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overlay_dir = Path(__file__).resolve().parent.parent.parent / ".venv_overlay"
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if overlay_dir.is_dir():
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shutil.rmtree(overlay_dir, ignore_errors = True)
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# Detect hardware first — sets DEVICE global used everywhere
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detect_hardware()
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# Disable flex attention on Blackwell+ GPUs (sm_120 and above)
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if get_device() == DeviceType.CUDA:
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import torch
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props = torch.cuda.get_device_properties(0)
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sm_version = props.major * 10 + props.minor
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if sm_version >= 120:
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os.environ["UNSLOTH_ENABLE_FLEX_ATTENTION"] = "0"
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import structlog
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from loggers import get_logger
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get_logger(__name__).info(
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f"GPU sm_{sm_version} detected — setting UNSLOTH_FLEX_ATTENTION=0"
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)
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# Pre-cache the helper GGUF model for LLM-assisted dataset detection.
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# Runs in a background thread so it doesn't block server startup.
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import threading
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def _precache():
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try:
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from utils.datasets.llm_assist import precache_helper_gguf
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precache_helper_gguf()
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except Exception:
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pass # non-critical
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threading.Thread(target = _precache, daemon = True).start()
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if not storage.is_initialized():
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setup_token = secrets.token_urlsafe(32)
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storage.save_setup_token(setup_token)
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print("\n" + "=" * 60)
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print("FIRST-TIME SETUP REQUIRED")
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print("Use this one-time setup token to create your admin account:\n")
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print(f" {setup_token}\n")
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print("This token can only be used once.")
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print("=" * 60 + "\n")
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yield
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# Cleanup
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_hw_module.DEVICE = None
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clear_unsloth_compiled_cache()
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# Create FastAPI app
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app = FastAPI(
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title = "Unsloth UI Backend",
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version = "1.0.0",
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description = "Backend API for Unsloth UI - Training and Model Management",
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lifespan = lifespan,
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)
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# Initialize structured logging
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from loggers.config import LogConfig
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from loggers.handlers import LoggingMiddleware
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logger = LogConfig.setup_logging(
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service_name = "unsloth-studio-backend",
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env = os.getenv("ENVIRONMENT_TYPE", "production"),
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)
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app.add_middleware(LoggingMiddleware)
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# CORS middleware
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app.add_middleware(
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CORSMiddleware,
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allow_origins = ["*"], # In production, specify allowed origins
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allow_credentials = True,
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allow_methods = ["*"],
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allow_headers = ["*"],
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)
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# ============ Register API Routes ============
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# Register routers
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app.include_router(auth_router, prefix = "/api/auth", tags = ["auth"])
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app.include_router(training_router, prefix = "/api/train", tags = ["training"])
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app.include_router(models_router, prefix = "/api/models", tags = ["models"])
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app.include_router(inference_router, prefix = "/api/inference", tags = ["inference"])
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# OpenAI-compatible endpoints: mount the same inference router at /v1
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# so external tools (Open WebUI, SillyTavern, etc.) can use the
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# standard /v1/chat/completions path.
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app.include_router(inference_router, prefix = "/v1", tags = ["openai-compat"])
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app.include_router(datasets_router, prefix = "/api/datasets", tags = ["datasets"])
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app.include_router(data_recipe_router, prefix = "/api/data-recipe", tags = ["data-recipe"])
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app.include_router(export_router, prefix = "/api/export", tags = ["export"])
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# ============ Health and System Endpoints ============
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@app.get("/api/health")
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async def health_check():
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"""Health check endpoint"""
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return {
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"status": "healthy",
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"timestamp": datetime.now().isoformat(),
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"service": "Unsloth UI Backend",
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}
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@app.get("/api/system")
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async def get_system_info():
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"""Get system information"""
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import platform
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import psutil
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from utils.hardware import get_device, get_gpu_memory_info, DeviceType
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# GPU Info — uses the hardware module (works on CUDA, MPS, CPU)
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mem_info = get_gpu_memory_info()
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gpu_info = {"available": mem_info.get("available", False), "devices": []}
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if mem_info.get("available"):
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gpu_info["devices"].append(
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{
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"index": mem_info.get("device", 0),
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"name": mem_info.get("device_name", "Unknown"),
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"memory_total_gb": round(mem_info.get("total_gb", 0), 2),
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}
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)
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# CPU & Memory
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memory = psutil.virtual_memory()
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return {
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"platform": platform.platform(),
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"python_version": platform.python_version(),
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"device_backend": get_device().value,
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"cpu_count": psutil.cpu_count(),
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"memory": {
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"total_gb": round(memory.total / 1e9, 2),
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"available_gb": round(memory.available / 1e9, 2),
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"percent_used": memory.percent,
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},
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"gpu": gpu_info,
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}
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@app.get("/api/system/hardware")
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async def get_hardware_info():
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"""Return GPU name, total VRAM, and key ML package versions."""
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from utils.hardware import get_gpu_summary, get_package_versions
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return {
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"gpu": get_gpu_summary(),
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"versions": get_package_versions(),
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}
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# ============ Serve Frontend (Optional) ============
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def setup_frontend(app: FastAPI, build_path: Path):
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"""Mount frontend static files (optional)"""
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if not build_path.exists():
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return False
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# Mount assets
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assets_dir = build_path / "assets"
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if assets_dir.exists():
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app.mount("/assets", StaticFiles(directory = assets_dir), name = "assets")
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@app.get("/")
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async def serve_root():
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content = (build_path / "index.html").read_bytes()
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return Response(
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content = content,
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media_type = "text/html",
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headers = {"Cache-Control": "no-cache, no-store, must-revalidate"},
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)
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@app.get("/{full_path:path}")
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async def serve_frontend(full_path: str):
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if full_path.startswith("api"):
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return {"error": "API endpoint not found"}
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file_path = (build_path / full_path).resolve()
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# Block path traversal — ensure resolved path stays inside build_path
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if not file_path.is_relative_to(build_path.resolve()):
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return Response(status_code = 403)
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if file_path.is_file():
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return FileResponse(file_path)
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# Serve index.html as bytes — avoids Content-Length mismatch
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content = (build_path / "index.html").read_bytes()
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return Response(
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content = content,
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media_type = "text/html",
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headers = {"Cache-Control": "no-cache, no-store, must-revalidate"},
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)
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return True
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