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FastAPI Model Inference Server

This is a FastAPI application that provides an HTTP endpoint for running predictions using several machine-learning models.

Overview

This application supports four types of pipelines: text generation, zero-shot-classification, object detection, and token classification. It converts input data into the v2 format and sends HTTP POST requests to the model servers to run the inference.

Setup and Installation

To get started with the application, follow the steps below:

  1. Clone this repository to your local machine:
git clone https://github.com/<mohsina-bilal>/<tf-intern>.git
  1. Navigate into the folder:
cd <tf-intern>
  1. Make sure you have Python 3.6+ installed on your machine. Install the required dependencies using:
pip install -r requirements.txt

Usage

To start the server, run the following command:

uvicorn app:app

Once the server is running, you can send HTTP POST requests to the /predict endpoint to run inference on the models. The request body should be a JSON object with a single inputs key, whose value can be anything.

Here is an example using curl:

curl -X POST "http://localhost:8000/predict" -H "accept: application/json" -H "Content-Type: application/json" -d "{\"inputs\":{\"sequence\":\"This is a test.\",\"candidate_labels\":[\"test\",\"exam\",\"check\"]}}"

The server will respond with the prediction results from the model server.

Note

How to make predictions

Use the /predict endpoint to make POST requests with the appropriate inputs for the chosen model. The input data formats for each model are as follows:

  • Zero-shot Classification:
{
  "inputs": {
    "sequence": "text_sequence",
    "candidate_labels": ["label1", "label2", ..., "labelN"]
  }
}
  • Object Detection:
{
  "inputs": "image.jpg"
}
  • Token Classification:
{
  "inputs": "text_input"
}
  • Text Generation:
{
  "inputs": "text_input"
}

You can make these requests using a tool like curl, Postman, or any HTTP client in a programming language of your choice.

The application currently uses predefined model servers. If you want to use your own model servers, you need to modify the model_deployed_url and hf_pipeline variables in app.py.

If you encounter any issues, please open an issue on this GitHub repository.

Contributing

If you want to contribute to this project, please create a pull request. Any contributions, big or small, are welcomed and appreciated!