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Serving ML Models with API or UI app

Create Rest API or Interactive UI app for Any Learning Model - ML, DL, Image Classification, NLP, Tensorflow, PyTorch or SKLearn.

What can it do?

  • Create Rest API endpoint for Model Serving
  • Create Interactive UI for Model Prototype Demo
  • Share UI Demo with everyone by generating public url
  • Predefined processing functions for image classification (NLP processing functions coming soon)
  • Override custom preprocessing and Postprocessing function with your own.
  • Request Response Schema (JSON body) will be changed based on the api_type.

install: pip install -U "chitra[serve]"

Default available API types are:

  1. Image Classification
  2. Object Detection
  3. Text Classification
  4. Question Answering

To get a full list of available API types you can call chitra.serve.API.get_available_api_types().

Create Rest API

Text Classification API

You can easily create Sentiment Analysis API. In this example, I will use HuggingFace to load the Sentiment Analysis Model but feel free to use other models as well.

from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline

from chitra.serve import create_api

tokenizer = AutoTokenizer.from_pretrained("finiteautomata/beto-sentiment-analysis")
model = AutoModelForSequenceClassification.from_pretrained(
classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)

create_api(classifier, run=True, api_type="text-classification")

You can open Swagger UI in your browser to test the API 🔥

Image Classification API

from chitra.serve import create_api
from chitra.trainer import create_cnn

model = create_cnn('mobilenetv2', num_classes=2)

create_api(model, run=True, api_type='image-classification')

Open in your browser and try out the API. You can upload any image to try.



Create Interactive UI with Gradio

To get a full list of available api_types for GradioApp you can call chitra.serve.GradioApp.get_available_api_types().

Image Classification Demo

Instantiate ImageNet pretrained Model with Tensorflow

import tensorflow as tf

from chitra.core import load_imagenet_labels

image_shape = (224, 224)
model = tf.keras.applications.MobileNetV2(weights="imagenet")
IMAGENET_LABELS = load_imagenet_labels()

Chitra will automatically create a preprocessing function based on api_type. But if you want to override and define your own then you can just pass any callable function.

def postprocess(preds):
    preds = tf.argmax(preds, 1).numpy()
    label = IMAGENET_LABELS[preds[0]]
    return label

Create GradioApp with Chitra

from import GradioApp

app = GradioApp(

If you want to share the live internet url then set share=True, it will create a public url that you can share with anyone over the internet.



Last update: July 24, 2021