zakirangwala

# Test Movie methods
query = "The Karate Kid"
moviesDB = imdb.IMDb()
movies = moviesDB.search_movie(find_imdb(query))
id = movies[0].getID()
score = rotten_tomatoes_score(find_imdb(query))
movie = moviesDB.get_movie(id)
title = movie['title']
year = movie['year']
rating = movie['rating']

zakirangwala

import sports
# Sports function
def scores(query):
  try:
    result = []
    all_matches = sports.all_matches()
    keys = list(all_matches.keys())
    for j in range(len(keys)):
        temp = all_matches[keys[j]]
        matches = []

zakirangwala

import smtplib
def send_mail(subject, body, reciever):
    server = smtplib.SMTP('smtp.gmail.com', 587)
    server.ehlo()
    server.starttls()
    server.ehlo()
    server.login(config.email, config.password)
    msg = f"Subject: {subject}\n\n{body}"
    server.sendmail(config.email, reciever, msg)
    server.quit()

zakirangwala

if __name__ == "__main__":
  greet()
  city, country, latitude, longitude = get_location()
  while True:
    query = listen().lower()
    if 'stock' in query:
      pass
    elif 'weather' in query or 'temperature' in query:
      pass
    elif 'movie' in query or 'documentary' in query:

zakirangwala

# Import Libraries
import os
import cv2
import glob
import pandas as pd
import xml.etree.ElementTree as ET
import numpy as np
import tensorflow as tf
from object_detection.utils import config_util
from object_detection.protos import pipeline_pb2

zakirangwala

# Setup Paths
WORKSPACE_PATH = 'Tensorflow/workspace'
SCRIPTS_PATH = 'Tensorflow/scripts'
APIMODEL_PATH = 'Tensorflow/tensorflow-models/models'
ANNOTATION_PATH = WORKSPACE_PATH+'/annotations'
IMAGE_PATH = WORKSPACE_PATH+'/images'
MODEL_PATH = WORKSPACE_PATH+'/models'
PRETRAINED_MODEL_PATH = WORKSPACE_PATH+'/pre-trained-models'
CONFIG_PATH = MODEL_PATH+'/my_ssd_mobnet/pipeline.config'
CHECKPOINT_PATH = MODEL_PATH+'/my_ssd_mobnet/'

zakirangwala

# Label Map
def construct_label_map():
    labels = [{'name': 'Mask', 'id': 1}, {'name': 'NoMask', 'id': 2}]

    with open(ANNOTATION_PATH + '\label_map.pbtxt', 'w') as f:
        for label in labels:
            f.write('item { \n')
            f.write('\tname:\'{}\'\n'.format(label['name']))
            f.write('\tid:{}\n'.format(label['id']))
            f.write('}\n')

zakirangwala

# XML TO CSV
def xml_to_csv(path):
    xml_list = []
    for xml_file in glob.glob(path + '/*.xml'):
        tree = ET.parse(xml_file)
        root = tree.getroot()
        for member in root.findall('object'):
            value = (root.find('filename').text+".jpg",
                     int(root.find('size')[0].text),
                     int(root.find('size')[1].text),

zakirangwala

# Configuration
def config():
    CONFIG_PATH = MODEL_PATH+'/'+CUSTOM_MODEL_NAME+'/pipeline.config'
    config = config_util.get_configs_from_pipeline_file(CONFIG_PATH)
    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    with tf.io.gfile.GFile(CONFIG_PATH, "r") as f:
        proto_str = f.read()
        text_format.Merge(proto_str, pipeline_config)
    # Modify pipeline
    pipeline_config.model.ssd.num_classes = 2

zakirangwala

# Load Model from checkpoints
def load_model():
    configs = config_util.get_configs_from_pipeline_file(CONFIG_PATH)
    detection_model = model_builder.build(
        model_config=configs['model'], is_training=False)
    ckpt = tf.compat.v2.train.Checkpoint(model=detection_model)
    ckpt.restore(os.path.join(CHECKPOINT_PATH, 'ckpt-9')).expect_partial()
    return detection_model