Ajinkya Chavan AjinkyaChavan9

## download_coursera_jupyter_data.md

      
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                AjinkyaChavan9
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              October 5, 2020 21:21
                — forked from msis/download_coursera_jupyter_data.md
            
              
                [How to download coursera classes?] #coursera #jupyter #notebook
              
          
File > Open ...
Launch new terminal instance: New > Terminal
tarball the folder:

tar czvhf coursera.tar.gz *

a. split it (usually this file might be too large for your instance to allow download)

split -b 90M -d coursera.tar.gz coursera.

  
## Procfile
web: sh setup.sh && streamlit run RGB_web_app.py

## setup.sh
mkdir -p ~/.streamlit

echo "[server]
headless = true
port = $PORT
enableCORS = false
" > ~/.streamlit/config.toml

## RGB_web_app.py
# -*- coding: utf-8 -*-
import streamlit as st
import PIL
from PIL import Image, ImageOps
from color_classifier import predict_color #importing predicting color function

# display image with the size and rgb color
def display_image():
    img = Image.new("RGB", (200, 200), color=(Red,Green,Blue))
    img = ImageOps.expand(img, border=1, fill='black')  # border to the img

## color_classifier.py
# -*- coding: utf-8 -*-

## Importing Libraries
import numpy as np

# Importing Tensorflow
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

## 1_import_library.py
!pip install pyyaml h5py  # Required to save models in HDF5 format

## requirements.txt
absl-py==0.10.0
altair==4.1.0
argon2-cffi==20.1.0
astor==0.8.1
astunparse==1.6.3
attrs==20.1.0
backcall==0.2.0
base58==2.0.1
bleach==3.1.5
blinker==1.4

## Classification Report.py
#Classification Report
target_names = ['Red', 'Green', 'Blue', 'Yellow', 'Orange', 'Pink', 'Purple', 'Brown', 'Grey', 'Black', 'White']
print(classification_report(actual_encoded_test_labels, predicted_encoded_test_labels, target_names=target_names))

## Confusion Matrix.py
from sklearn.metrics import confusion_matrix, classification_report
confusion_matrix_test = confusion_matrix(actual_encoded_test_labels, predicted_encoded_test_labels)
f,ax = plt.subplots(figsize=(16,12))
categories = ['Red', 'Green', 'Blue', 'Yellow', 'Orange', 'Pink', 'Purple', 'Brown', 'Grey', 'Black', 'White']
sns.heatmap(confusion_matrix_test, annot=True, cmap='Blues', fmt='d',
            xticklabels = categories,
            yticklabels = categories)
plt.show()

## Evaluate Model.py
model.evaluate(x=test_dataset, y=test_labels)
	mkdir -p ~/.streamlit

	echo "[server]
	headless = true
	port = $PORT
	enableCORS = false
	" > ~/.streamlit/config.toml
	# -- coding: utf-8 --
	import streamlit as st
	import PIL
	from PIL import Image, ImageOps
	from color_classifier import predict_color #importing predicting color function

	# display image with the size and rgb color
	def display_image():
	img = Image.new("RGB", (200, 200), color=(Red,Green,Blue))
	img = ImageOps.expand(img, border=1, fill='black') # border to the img
	# -- coding: utf-8 --

	## Importing Libraries
	import numpy as np

	# Importing Tensorflow
	import tensorflow as tf
	from tensorflow import keras
	from tensorflow.keras import layers
	absl-py==0.10.0
	altair==4.1.0
	argon2-cffi==20.1.0
	astor==0.8.1
	astunparse==1.6.3
	attrs==20.1.0
	backcall==0.2.0
	base58==2.0.1
	bleach==3.1.5
	blinker==1.4
	#Classification Report
	target_names = ['Red', 'Green', 'Blue', 'Yellow', 'Orange', 'Pink', 'Purple', 'Brown', 'Grey', 'Black', 'White']
	print(classification_report(actual_encoded_test_labels, predicted_encoded_test_labels, target_names=target_names))
	from sklearn.metrics import confusion_matrix, classification_report
	confusion_matrix_test = confusion_matrix(actual_encoded_test_labels, predicted_encoded_test_labels)
	f,ax = plt.subplots(figsize=(16,12))
	categories = ['Red', 'Green', 'Blue', 'Yellow', 'Orange', 'Pink', 'Purple', 'Brown', 'Grey', 'Black', 'White']
	sns.heatmap(confusion_matrix_test, annot=True, cmap='Blues', fmt='d',
	xticklabels = categories,
	yticklabels = categories)
	plt.show()