deepak-karkala

import random
import numpy as np
import StanfordSentiment
import matplotlib
import matplotlib.pyplot as plt
import time

deepak-karkala

# A/B Testing: Conversion Rate: Distance between chi-squared distributions
from scipy.stats import chi2

T = np.array([165, 165, 9835, 9835])
O = np.array([150, 180, 9850, 9800])

D = np.sum(np.square(T-O)/T)

pvalue = chi2.sf(D, df=1)

deepak-karkala

# A/B testing: Z-test for Booking time (Normal Distribution)
from scipy.stats import norm

mu_A, std_A, n_A = 300, 105, 10000
mu_B, std_B, n_B = 296, 120, 10000

Z = (mu_A - mu_B)/np.sqrt(std_B**2/n_B + std_A**2/n_A)
pvalue = norm.sf(Z)

deepak-karkala

# REST API Service to get Price Predictions for Airbnb listings
@app.route("/predict", methods=["POST"])
def predict():
    
    # initialize the data dictionary that will be returned from the view
    data = {"success": False}

    # ensure an image was properly uploaded to our endpoint
    if flask.request.method == "POST":
        data["predictions"] = []

deepak-karkala

# FLASK Webapp for serving price Predictions for Airbnb listings 
import os, sys
sys.path.append(".")
import webapp_predict_price

import pickle
from flask import Flask
import flask
import sklearn
import joblib

deepak-karkala

# FLASK Webapp for Image Segmentation Model

import os, sys, io
sys.path.append(".")
import webapp

from flask import Flask
import flask
import numpy as np
import pandas as pd

deepak-karkala

# Serve Model predictions using REST API

# Predict and return JSON data
@app.route("/predict", methods=["POST"])
def predict():
    # initialize the data dictionary that will be returned from the view
    data = {"success": False}

    # ensure an image was properly uploaded to our endpoint
    if flask.request.method == "POST":

deepak-karkala

# Saving Tensorflow models for serving

# Option 1: SavedModel format
# Save model
model.save('saved_model/model')
# Load model
loaded_model = tf.keras.models.load_model('saved_model/model')

# Option 2: .h5 format
# Save model

deepak-karkala

# Model quantisation using TFLite
save_model_path = "/tmp/"

# Save original model in tflite format
tflite_models_dir = pathlib.Path(save_model_path)
tflite_models_dir.mkdir(exist_ok=True, parents=True)
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
tflite_model_file = tflite_models_dir/"model.tflite"
print(tflite_model_file.write_bytes(tflite_model))

deepak-karkala

### Tensorboard callbacks
# Log IoU at end of epoch
def log_epoch_metrics(epoch, logs):
  # Log directory
  logdir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
  file_writer = tf.summary.create_file_writer(logdir + "/fit")
  file_writer.set_as_default()

  # Intersection Over Union metric
  m = tf.keras.metrics.MeanIoU(num_classes=len(LABEL_NAMES))