image-captioning 1.0.0

Image captioning using BLIP (Transformers-based)

🖼️ Image Captioning API

A high-performance, production-ready REST API for ML-based image captioning — built with FastAPI, async concurrency, and GPU-optimized inference.

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🚀 Features

• Zero-Disk I/O — In-memory BytesIO buffers eliminate file system overhead (~200–500ms saved per request)
• Singleton Model Loading — Model weights loaded once at startup via FastAPI lifespan events (sub-second latency vs. 8+ seconds with per-request loading)
• Managed GPU Concurrency — asyncio.Semaphore prevents CUDA OOM crashes; asyncio.to_thread keeps the API responsive during heavy inference
• FP16 + torch.compile — 50% VRAM reduction and ~25% faster execution via Tensor Core acceleration
• Security Validation — python-magic binary header inspection + MAX_FILE_SIZE enforcement at the gate, before GPU resources are touched

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📈 Performance

Metric	Original Script	Optimized API
Average Latency	~7.5s (model reloads)	~0.6s (warm model)
Concurrency	Sequential (1 at a time)	Concurrent (queue-based)
Disk Usage	Temp file per request	0 bytes (in-memory)
VRAM Stability	High OOM risk	Safe (semaphore-limited)

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🏗️ Architecture

Incoming Request
      │
      ▼
[Security Shield]  ← python-magic + MAX_FILE_SIZE check
      │
      ▼
[In-Memory Buffer] ← io.BytesIO (no disk I/O)
      │
      ▼
[Async Queue]      ← asyncio.Semaphore (concurrency gate)
      │
      ▼
[Background Thread] ← asyncio.to_thread (non-blocking)
      │
      ▼
[GPU Inference]    ← Singleton CaptioningModel (FP16 + torch.compile)
      │
      ▼
   Response

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⚙️ Installation

# Clone the repository
git clone https://github.com/your-org/image-captioning-api.git
cd image-captioning-api

# Install dependencies
pip install -r requirements.txt

Requirements: Python 3.10+, CUDA-capable GPU, PyTorch with CUDA support.

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🔧 Running the API

Development

uvicorn app:app --reload --host 0.0.0.0 --port 8000

Production (recommended)

Use Gunicorn with Uvicorn workers for maximum performance on multi-core systems:

gunicorn -w 4 -k uvicorn.workers.UvicornWorker app:app --bind 0.0.0.0:8000

Tip: Set -w (workers) to (2 × CPU cores) + 1 as a starting point.

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📡 API Reference

POST /caption

Submit an image and receive a generated caption.

Request

Content-Type: multipart/form-data
Body: file=<image>

Response

{
  "caption": "A dog running through a field of sunflowers."
}

Error Responses

Code	Reason
400	Invalid file type or binary header mismatch
413	File exceeds MAX_FILE_SIZE limit
503	GPU queue at capacity

GET /health

Returns 200 OK when the model is loaded and the API is ready.

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🔐 Configuration

Set the following environment variables:

Variable	Default	Description
MAX_FILE_SIZE	10485760	Max upload size in bytes (default: 10MB)
GPU_CONCURRENCY	4	Max simultaneous GPU inference tasks
MODEL_PATH	./models/captioner	Path to model weights

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🛡️ Security

All uploads are validated before reaching GPU resources:

1. Binary header inspection via python-magic — rejects files that aren't valid images regardless of extension
2. Size enforcement — files exceeding MAX_FILE_SIZE are rejected at ingestion
3. No disk writes — uploaded data never touches the filesystem

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🧠 Key Implementation Details

Singleton Model (Lifespan)

@asynccontextmanager
async def lifespan(app: FastAPI):
    app.state.model = CaptioningModel()   # loaded once at startup
    yield
    del app.state.model                   # cleanup on shutdown

Managed GPU Concurrency

semaphore = asyncio.Semaphore(GPU_CONCURRENCY)

async def caption_image(file_bytes: bytes) -> str:
    async with semaphore:
        return await asyncio.to_thread(_run_inference, file_bytes)

FP16 Inference

model = model.half()              # FP16: 50% VRAM reduction
model = torch.compile(model)     # JIT-optimized kernels

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📄 License

MIT License — see LICENSE for details.