Introduction

When deploying machine learning models on the web, it’s often essential to keep model weights private, especially when dealing with proprietary or large models like diffusion generators or commercial segmentation tools. This post explores how I built a Gradio frontend that interacts with a secure backend API, while hiding all model logic and weights from the user.

Why Hide the Weights?

In typical Gradio apps, models are often hosted together with the interface, which means:

• Users can inspect model files if hosted publicly.
• Heavier models slow down the UI.
• It’s harder to scale compute across machines.

To solve this, I decoupled the frontend interface and the inference backend.

How It Works

I implemented a client-server architecture:

• Frontend (Gradio UI):

  • Users upload an image and select options.
  • Requests are added to a queue.
  • UI checks the status periodically until results are ready.

• Backend (API Server):

  • Receives requests via Hugging Face’s gradio_client.
  • Processes one request at a time (ideal for GPU-heavy tasks).
  • Returns base64-encoded results to the frontend.

Queue-Based Request System

Since the GPU backend can only handle one request at a time, I used a queue system to:

• Handle multiple concurrent users.
• Provide estimated wait times.
• Prevent overload and timeouts.

Every request receives:

• A request_id
• Queue position
• Estimated wait time

Once processed, the UI displays the output.

Sample Workflow

1. User uploads a photo and selects preferences.
2. The app encodes the image in base64 and submits it to the backend.
3. The backend simulates inference and returns two output images:
   • An overlay (e.g., object mask or enhancement).
   • A final rendered background.
4. The frontend updates the UI and shows results.

Here’s a simplified version of the API function on the backend:

def predict_api(image_b64, category, gender):
    time.sleep(random.randint(5, 15))  # simulate delay
    overlay_img = get_random_image()
    bg_img = get_random_image()
    return image_b64, to_b64(overlay_img), to_b64(bg_img), "✅ Done"

The frontend periodically checks the request_id status every 2 seconds until it’s completed.

Benefits of This Setup

🔐 Security – Your model and logic stay hidden on the server.

⚙️ Scalability – You can deploy the backend on a stronger machine or GPU instance.

🧠 Modularity – Swap or upgrade models without changing the frontend.

🚦 Queue Control – Manage compute usage, prioritize jobs, and prevent server crashes.

Conclusion

By separating the Gradio interface from the model backend and implementing a queue, I was able to build a secure, scalable, and user-friendly AI tool without ever exposing the model weights. This method is ideal for use cases like:

• Commercial AI tools
• Diffusion or segmentation models
• Protected ML workflows

If you’re looking to deploy AI tools without leaking your IP, this pattern is worth considering.

Hashtags

#Gradio #SecureAI #QueueSystem #API #MLDeployment