rest-api-ml-model's Introduction

REST-API-ML-Model

Deploy simple machine learning model as Rest API in Python. The case the we used is Sentiment Analysis on Movie Reviews in Kaggle competition to predict whether sentimen of a comment in Rotten Tomatoes review is good or not. Dataset can be found here.

Use

We can test the API using request function in Jupyter Notebook.

url = 'http://127.0.0.1:5000/'
params ={'query': 'that movie was boring'}
response = requests.get(url, params)
response.json()
Output: {'confidence': 0.128, 'prediction': 'Negative'}

Or using HTTPie in terminal.

$ http http://127.0.0.1:5000/ query=='that movie was boring'
HTTP/1.0 200 OK
Content-Length: 58
Content-Type: application/json
Date: Fri, 31 Aug 2018 18:49:25 GMT
Server: Werkzeug/0.14.1 Python/3.6.3
{
    "confidence": 0.128,
    "prediction": "Negative"
}

File Structure

User use this file structure for API.

.
├── README.md
	├── app.py  # Flask REST API script
	├── build_model.py  # script to build and pickle the classifier
	├── model.py  # script for the classifier class object
	├── util.py  # helper functions
	├── requirements.txt
	└── lib/
		├── data/  # data from Kaggle
		│   ├── sampleSubmission.csv
		│   ├── test.tsv
		│   └── train.tsv
		└── models/  # pickled models for import into API script
    		├── SentimentClassifier.pkl
    		└── TFIDFVectorizer.pkl

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