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Architecture

High-Level 

Browser (Next.js 14)  <--HTTP/SSE/WS-->  FastAPI (Python)
   | Recharts                           | Transformers + Torch
   | Shadcn/ui                          | pynvml/psutil
   | Tailwind                           | fpdf2

Frontend (frontend/src)

  • app/layout.tsx: RTL, Tajawal font, Sidebar + MobileNav
  • app/page.tsx: Dashboard (KPIs + ModelLoader + TelemetryCharts)
  • app/playground/page.tsx: Playground.tsx (SSE streaming, TPS/TTFT)
  • app/benchmark/page.tsx: BenchmarkPanel.tsx (preset + CustomDatasetPanel + progress SSE modal)
  • app/hardware/page.tsx: TelemetryCharts + Power vs VRAM scatter
  • app/models/page.tsx: Validate + scan GET /api/model/list
  • app/share/[token]/page.tsx: Public report view via GET /api/share/{token}
  • lib/api.ts: API_BASE = process.env.NEXT_PUBLIC_API_URL, apiFetch, streamGenerate, runBenchmarkStream
  • components/ui/*: Button, Card, Input, Badge, Tabs, Select, Progress (Shadcn)

Backend (backend/app)

  • main.py: FastAPI + CORSMiddleware(*) + 7 routers
  • model_manager.py: ModelManager singleton
    • load_model(path, dtype, quantization, device_map) → AutoModelForCausalLM.from_pretrained with BitsAndBytesConfig + torch_dtype + device_map
    • Demo fallback if !HAS_TRANSFORMERS or CUDA error
    • generate_stream(req) → TextIteratorStreamer + Thread or _demo_generate
  • telemetry.py: TelemetryService — psutil + pynvml.nvmlDeviceGetMemoryInfo + history[600]
  • benchmark.py: BENCHMARK_SUITES 15 tasks + evaluate_answer() (Exact/Regex/LLM) + run_benchmark()
  • dataset_parser.py: scan_folder() → parse_file() (CSV/JSON/JSONL/TXT/MD) + detect_language() + infer_category()
  • schemas.py: Pydantic models (ModelLoadRequest, GenerateRequest, TelemetrySnapshot, BenchmarkReport)
  • routers/:
    • model.py: /api/model/*
    • inference.py: /api/generate (SSE)
    • telemetry.py: /api/telemetry/ + GET /history + WS /ws/telemetry
    • benchmark.py: /api/benchmark/* including POST /run-stream (SSE)
    • custom_benchmark.py: /api/benchmark/custom/*
    • share.py: /api/share/* (in-memory share_store)
    • export.py: /api/export/{json,csv,pdf} (Unicode font via tahoma.ttf)

Data Flow

  1. Load: UI POST /api/model/load → model_manager.load_model() → info with num_parameters estimated from file size
  2. Chat: UI POST /api/generate stream=true → StreamingResponse data: {"type":"token",...} → UI updates tps
  3. Benchmark: UI POST /api/benchmark/run-stream → SSE progress per task → task_done → done with BenchmarkReport
  4. Telemetry: Poll GET /api/telemetry/ every 1.2s + history for charts
  5. Export: GET /api/export/pdf?report_id= → fpdf2 with Arabic font + reshaper

Persistence

  • reports_store: Dict[str, BenchmarkReport] (in-memory, lost on restart)
  • share_store: Dict[str, report_id] (in-memory)
  • For production, replace with sqlite/postgres.

Security Notes

  • No auth (local-first). For public, add proxy auth and restrict folder_path to allowlist.