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#Develop an API server on python using Fast API for the model created in the previous step.
from string import punctuation
from nltk.tokenize import word_tokenize
import nltk
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from os.path import dirname, join, realpath
import joblib
import uvicorn
from fastapi import FastAPI
import requests as r
#from pyramid_swagger import add_swagger_view
app = FastAPI(
title="Sentiment Analysis API",
description="A simple API that use NLP model to predict the sentiment of the airline reviews",
version="0.1",
)
# Load the model
model = joblib.load('sentiment_classifier.pkl')
vectorizer = joblib.load('vectorizer.pkl')
class Inference:
def __init__(self, model, vectorizer):
self.model = model
self.vectorizer = vectorizer
def get_sentiment(self, review):
new_review = [review]
new_review = self.vectorizer.transform(new_review)
pred = self.model.predict(new_review)
if pred == 1:
return 'Positive'
else:
return 'Negative'
inference = Inference(model, vectorizer)
@app.get("/")
def home():
return {"message": "Welcome to Sentiment Analysis API"}
@app.get("/predict/{review}")
def predict_sentiment(review: str):
return {"sentiment": inference.get_sentiment(review)}
#app.include_router(swagger_ui_bundle, tags=["Swagger UI"])
#app.include_router(swagger_ui_expose, tags=["Swagger UI"])
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