Category: azure

Step 1. clone the repository
https://github.com/MicrosoftLearning/AI-102-AIEngineer/21-custom-form

Step 2. create a Form Recognizer resource in Azure Portal

Step 3. setup python env
edit C:\Hans\AI-102-AIEngineer\21-custom-form\setup.cmd with your values:

rem Set variable values 
set subscription_id=YOUR_SUBSCRIPTION_ID
set resource_group=YOUR_RESOURCE_GROUP
set location=YOUR_LOCATION_NAME

Then run the command to create a SAS URI:
(base) C:\Users\Student\miniconda3\AI-102-AIEngineer\21-custom-form>az login
(base) C:\Users\Student\miniconda3\AI-102-AIEngineer\21-custom-form>setup.cmd
Creating storage...
Uploading files...
Finished[#############################################################]  100.0000%
-------------------------------------
SAS URI: https://ai102form7685119.blob.core.windows.net/sampleforms?se=2022-01-01T00%3A00%3A00Z&sp=rwl&sv=2018-11-09&sr=c&sig=Wopn1A5klioFouoyYKV57hrFIO7SbkGJmjZV%2BIe7R6I%3D

Step 4. Train a model
pip install azure-ai-formrecognizer==3.0.0
edit train-model.py with your endpoint, key and SAS URI:

import os 

from azure.core.exceptions import ResourceNotFoundError
from azure.ai.formrecognizer import FormRecognizerClient
from azure.ai.formrecognizer import FormTrainingClient
from azure.core.credentials import AzureKeyCredential

def main(): 
     
    try: 
    
        # Get configuration settings 
        ENDPOINT='https://hansformrecognizer.cognitiveservices.azure.com/'
        KEY='f20ca70a5497484c9f239d3431df2757'
        trainingDataUrl = 'https://ai102form2397530048.blob.core.windows.net/sampleforms?se=2022-01-01T00%3A00%3A00Z&sp=rwl&sv=2018-11-09&sr=c&sig=3LQtq9KfelRXPSf6aqVN/Z3UcIN7KE1Net76W6alTGg%3D'

        # Authenticate Form Training Client
        form_recognizer_client = FormRecognizerClient(ENDPOINT, AzureKeyCredential(KEY))
        form_training_client = FormTrainingClient(ENDPOINT, AzureKeyCredential(KEY))

        # Train model 
        poller = form_training_client.begin_training(trainingDataUrl, use_training_labels=False)
        model = poller.result()

        print("Model ID: {}".format(model.model_id))
        print("Status: {}".format(model.status))
        print("Training started on: {}".format(model.training_started_on))
        print("Training completed on: {}".format(model.training_completed_on))

    except Exception as ex:
        print(ex)

if __name__ == '__main__': 
    main()

PS C:\Hans\AI-102-AIEngineer\21-custom-form\Python\train-model> python .\train-model.py
Model ID: 37951e13-645e-4364-a93e-96bb1bccdb78
Status: ready
Training started on: 2021-05-06 15:48:40+00:00
Training completed on: 2021-05-06 15:48:51+00:00

Step 5. Test the model
edit test-model.py with your Model ID generated in previous step:

import os 

from azure.core.exceptions import ResourceNotFoundError
from azure.ai.formrecognizer import FormRecognizerClient
from azure.ai.formrecognizer import FormTrainingClient
from azure.core.credentials import AzureKeyCredential

def main(): 
       
    try: 
    
        # Get configuration settings 
        ENDPOINT='https://hansformrecognizer.cognitiveservices.azure.com/'
        KEY='f20ca70a5497484c9f239d3431df2757'
         
        # Create client using endpoint and key
        form_recognizer_client = FormRecognizerClient(ENDPOINT, AzureKeyCredential(KEY))
        form_training_client = FormTrainingClient(ENDPOINT, AzureKeyCredential(KEY))

        # Model ID from when you trained your model.
        model_id = '37951e13-645e-4364-a93e-96bb1bccdb78'

        # Test trained model with a new form 
        with open('test1.jpg', "rb") as f: 
            poller = form_recognizer_client.begin_recognize_custom_forms(
                model_id=model_id, form=f)

        result = poller.result()

        for recognized_form in result:
            print("Form type: {}".format(recognized_form.form_type))
            for name, field in recognized_form.fields.items():
                print("Field '{}' has label '{}' with value '{}' and a confidence score of {}".format(
                    name,
                    field.label_data.text if field.label_data else name,
                    field.value,
                    field.confidence
                ))

    except Exception as ex:
        print(ex)

if __name__ == '__main__': 
    main()

verify the app:

C:\Hans\AI-102-AIEngineer\21-custom-form\Python\test-model> python .\test-model.py
Form type: form-0
Field 'field-0' has label 'Hero Limited' with value 'accounts@herolimited.com' and a confidence score of 0.53
Field 'field-1' has label 'Company Phone:' with value '555-348-6512' and a confidence score of 1.0
Field 'field-2' has label 'Website:' with value 'www.herolimited.com' and a confidence score of 1.0
Field 'field-3' has label 'Email:' with value '49823 Major Ave Cheer, MS, 38601' and a confidence score of 0.53
Field 'field-4' has label 'Dated As:' with value '04/04/2020' and a confidence score of 1.0
Field 'field-5' has label 'Purchase Order #:' with value '3929423' and a confidence score of 1.0
Field 'field-6' has label 'Vendor Name:' with value 'Seth Stanley' and a confidence score of 0.53
Field 'field-7' has label 'Company Name:' with value 'Yoga for You' and a confidence score of 1.0
Field 'field-8' has label 'Address:' with value '343 E Winter Road' and a confidence score of 1.0
Field 'field-9' has label 'Seattle, WA 93849 Phone:' with value '234-986-6454' and a confidence score of 0.53
Field 'field-10' has label 'Name:' with value 'Josh Granger' and a confidence score of 0.86
Field 'field-11' has label 'Company Name:' with value 'Granger Supply' and a confidence score of 0.53
Field 'field-12' has label 'Address:' with value '922 N Ebby Lane' and a confidence score of 0.53
Field 'field-13' has label 'Phone:' with value '932-294-2958' and a confidence score of 1.0
Field 'field-14' has label 'SUBTOTAL' with value '$6750.00' and a confidence score of 1.0
Field 'field-15' has label 'TAX' with value '$600.00' and a confidence score of 1.0
Field 'field-16' has label 'TOTAL' with value '$7350.00' and a confidence score of 1.0
Field 'field-17' has label 'Additional Notes:' with value 'Enjoy. Namaste. If you have any issues with your Yoga supplies please contact us directly via email or at 250-209-1294 during business hours.' and a confidence score 
of 0.53

Step 1. clone the repository
https://github.com/MicrosoftLearning/AI-102-AIEngineer/20-ocr

Step 2. edit read-text.py as below:

import os
import time
from PIL import Image, ImageDraw
from matplotlib import pyplot as plt

# import namespaces
from azure.cognitiveservices.vision.computervision import ComputerVisionClient
from azure.cognitiveservices.vision.computervision.models import OperationStatusCodes
from msrest.authentication import CognitiveServicesCredentials

def main():
    global cv_client
    try:
        # Get Configuration Settings
        KEY='c36f33fd15784c3984c5a88dcde4c31c'
        ENDPOINT='https://ai102cg.cognitiveservices.azure.com/'

        # Authenticate Computer Vision client
        credential = CognitiveServicesCredentials(KEY) 
        cv_client = ComputerVisionClient(ENDPOINT, credential)
     
        # Menu for text reading functions
        print('1: Use OCR API\n2: Use Read API\n3: Read handwriting\nAny other key to quit')
        command = input('Enter a number:')
        if command == '1':
            image_file = os.path.join('images','Lincoln.jpg')
            GetTextOcr(image_file)
        elif command =='2':
            image_file = os.path.join('images','Rome.pdf')
            GetTextRead(image_file)
        elif command =='3':
            image_file = os.path.join('images','Note.jpg')
            GetTextRead(image_file)
                
    except Exception as ex:
        print(ex)

def GetTextOcr(image_file):
    print('Reading text in {}\n'.format(image_file))
    # Use OCR API to read text in image
    with open(image_file, mode="rb") as image_data:
        ocr_results = cv_client.recognize_printed_text_in_stream(image_data)

    # Prepare image for drawing
    fig = plt.figure(figsize=(7, 7))
    img = Image.open(image_file)
    draw = ImageDraw.Draw(img)

    # Process the text line by line
    for region in ocr_results.regions:
        for line in region.lines:

            # Show the position of the line of text
            l,t,w,h = list(map(int, line.bounding_box.split(',')))
            draw.rectangle(((l,t), (l+w, t+h)), outline='magenta', width=5)

            # Read the words in the line of text
            line_text = ''
            for word in line.words:
                line_text += word.text + ' '
            print(line_text.rstrip())

    # Save the image with the text locations highlighted
    plt.axis('off')
    plt.imshow(img)
    outputfile = 'ocr_results.jpg'
    fig.savefig(outputfile)
    print('Results saved in', outputfile)

def GetTextRead(image_file):
    print('Reading text in {}\n'.format(image_file))
    # Use Read API to read text in image
    with open(image_file, mode="rb") as image_data:
        read_op = cv_client.read_in_stream(image_data, raw=True)

        # Get the async operation ID so we can check for the results
        operation_location = read_op.headers["Operation-Location"]
        operation_id = operation_location.split("/")[-1]

        # Wait for the asynchronous operation to complete
        while True:
            read_results = cv_client.get_read_result(operation_id)
            if read_results.status not in [OperationStatusCodes.running, OperationStatusCodes.not_started]:
                break
            time.sleep(1)

        # If the operation was successfuly, process the text line by line
        if read_results.status == OperationStatusCodes.succeeded:
            for page in read_results.analyze_result.read_results:
                for line in page.lines:
                    print(line.text)

if __name__ == "__main__":
    main()

Step 3. verify the app

C:\Hans\AI-102-AIEngineer\20-ocr\Python\read-text> python .\read-text.py
1: Use OCR API
2: Use Read API
3: Read handwriting
Any other key to quit
Enter a number:3
Reading text in images\Note.jpg

Shopping List
Non-Fat milk
Bread
Eggs

Step 1. install lib:
pip install azure-cognitiveservices-vision-computervision==0.7.0

Step 2. create image-analysis.py and prepare a picture images/street.jpg

from dotenv import load_dotenv
import os
from array import array
from PIL import Image, ImageDraw
import sys
import time
from matplotlib import pyplot as plt
import numpy as np

# Import namespaces
from azure.cognitiveservices.vision.computervision import ComputerVisionClient
from azure.cognitiveservices.vision.computervision.models import VisualFeatureTypes
from msrest.authentication import CognitiveServicesCredentials

def main():
    global cv_client

    try:
        # Get Configuration Settings
        KEY='c36f33fd15784c3984c5a88dcde4c31c'
        ENDPOINT='https://ai102cg.cognitiveservices.azure.com/'
        # Get image
        image_file = 'images/street.jpg'
        if len(sys.argv) > 1:
            image_file = sys.argv[1]

        # Authenticate Computer Vision client
        credential = CognitiveServicesCredentials(KEY) 
        cv_client = ComputerVisionClient(ENDPOINT, credential)

        # Analyze image
        AnalyzeImage(image_file)

        # Generate thumbnail
        GetThumbnail(image_file)

    except Exception as ex:
        print(ex)

def AnalyzeImage(image_file):
    print('Analyzing', image_file)

    # Specify features to be retrieved
    features = [VisualFeatureTypes.description,
                VisualFeatureTypes.tags,
                VisualFeatureTypes.categories,
                VisualFeatureTypes.brands,
                VisualFeatureTypes.objects,
                VisualFeatureTypes.adult]
    
    # Get image analysis
    with open(image_file, mode="rb") as image_data:
        analysis = cv_client.analyze_image_in_stream(image_data , features)

    # Get image description
    for caption in analysis.description.captions:
        print("Description: '{}' (confidence: {:.2f}%)".format(caption.text, caption.confidence * 100))

    # Get image tags
    if (len(analysis.tags) > 0):
        print("Tags: ")
        for tag in analysis.tags:
            print(" -'{}' (confidence: {:.2f}%)".format(tag.name, tag.confidence * 100))

    # Get image categories (including celebrities and landmarks)
    if (len(analysis.categories) > 0):
        print("Categories:")
        landmarks = []
        celebrities = []
        for category in analysis.categories:
            # Print the category
            print(" -'{}' (confidence: {:.2f}%)".format(category.name, category.score * 100))
            if category.detail:
                # Get landmarks in this category
                if category.detail.landmarks:
                    for landmark in category.detail.landmarks:
                        if landmark not in landmarks:
                            landmarks.append(landmark)

                # Get celebrities in this category
                if category.detail.celebrities:
                    for celebrity in category.detail.celebrities:
                        if celebrity not in celebrities:
                            celebrities.append(celebrity)

        # If there were landmarks, list them
        if len(landmarks) > 0:
            print("Landmarks:")
            for landmark in landmarks:
                print(" -'{}' (confidence: {:.2f}%)".format(landmark.name, landmark.confidence * 100))

        # If there were celebrities, list them
        if len(celebrities) > 0:
            print("Celebrities:")
            for celebrity in celebrities:
                print(" -'{}' (confidence: {:.2f}%)".format(celebrity.name, celebrity.confidence * 100))

    # Get brands in the image

    if (len(analysis.brands) > 0):
        print("Brands: ")
        for brand in analysis.brands:
            print(" -'{}' (confidence: {:.2f}%)".format(brand.name, brand.confidence * 100))

    # Get objects in the image
    if len(analysis.objects) > 0:
        print("Objects in image:")

        # Prepare image for drawing
        fig = plt.figure(figsize=(8, 8))
        plt.axis('off')
        image = Image.open(image_file)
        draw = ImageDraw.Draw(image)
        color = 'cyan'
        for detected_object in analysis.objects:
            # Print object name
            print(" -{} (confidence: {:.2f}%)".format(detected_object.object_property, detected_object.confidence * 100))

            # Draw object bounding box
            r = detected_object.rectangle
            bounding_box = ((r.x, r.y), (r.x + r.w, r.y + r.h))
            draw.rectangle(bounding_box, outline=color, width=3)
            plt.annotate(detected_object.object_property,(r.x, r.y), backgroundcolor=color)
        # Save annotated image
        plt.imshow(image)
        outputfile = 'objects.jpg'
        fig.savefig(outputfile)
        print('  Results saved in', outputfile)

    # Get moderation ratings
    ratings = 'Ratings:\n -Adult: {}\n -Racy: {}\n -Gore: {}'.format(analysis.adult.is_adult_content,
                                                                        analysis.adult.is_racy_content,
                                                                        analysis.adult.is_gory_content)
    print(ratings)        

def GetThumbnail(image_file):
    print('Generating thumbnail')

    # Generate a thumbnail
    with open(image_file, mode="rb") as image_data:
        # Get thumbnail data
        thumbnail_stream = cv_client.generate_thumbnail_in_stream(100, 100, image_data, True)

    # Save thumbnail image
    thumbnail_file_name = 'thumbnail.png'
    with open(thumbnail_file_name, "wb") as thumbnail_file:
        for chunk in thumbnail_stream:
            thumbnail_file.write(chunk)

    print('Thumbnail saved in.', thumbnail_file_name)

if __name__ == "__main__":
    main()

Step 3. run it with:
python image-analysis.py images/street.jpg
you will get objects.jpg and thumbnail.png generated in same folder:

ref: https://docs.microsoft.com/en-us/azure/cognitive-services/Face/Quickstarts/client-libraries?pivots=programming-language-python&tabs=visual-studio

Step 1. Clone the repository
https://github.com/MicrosoftLearning/AI-102-AIEngineer
We will use 13-bot-framework\Python

Step 2. Create a bot based on the EchoBot template
pip install botbuilder-core
pip install asyncio
pip install aiohttp
pip install cookiecutter==1.7.0
cookiecutter https://github.com/microsoft/botbuilder-python/releases/download/Templates/echo.zip
when prompted by cookiecutter, enter the following details:
bot_name: TimeBot
bot_description: A bot for our times

Step 3. Test the bot in the Bot Framework Emulator

(base) C:\Users\Student\hans\AI-102-AIEngineer\13-bot-framework\Python\TimeBot>python app.py
======== Running on http://localhost:3978 ========
(Press CTRL+C to quit)

Start the Bot Framework Emulator, and open your bot by specifying the endpoint with the /api/messages path appended, like this: http://localhost:3978/api/messages

Step 4. Update bot.py

# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.

from botbuilder.core import ActivityHandler, TurnContext
from botbuilder.schema import ChannelAccount
from datetime import datetime

class MyBot(ActivityHandler):
    # See https://aka.ms/about-bot-activity-message to learn more about the message and other activity types.

    async def on_message_activity(self, turn_context: TurnContext):
        input_message = turn_context.activity.text
        response_message = 'Ask me what the time is.'
        if (input_message.lower().startswith('what') and 'time' in input_message.lower()):
            now = datetime.now()
            response_message = 'The time is {}:{:02d}.'.format(now.hour,now.minute)
        await turn_context.send_activity(response_message)
    
    async def on_members_added_activity(
        self,
        members_added: ChannelAccount,
        turn_context: TurnContext
    ):
        for member_added in members_added:
            if member_added.id != turn_context.activity.recipient.id:
                await turn_context.send_activity("Hello and welcome!")

Step 5. Create an Azure application registration
az login
az ad app create –display-name “TimeBot” –password “Super$ecretPassw0rd” –available-to-other-tenants

Step 6. Create Azure resources
az deployment group create –resource-group “YOUR_RESOURCE_GROUP” –template-file “deploymenttemplates/template-with-preexisting-rg.json” –parameters appId=”YOUR_APP_ID” appSecret=”Super$ecretPassw0rd” botId=”A_UNIQUE_BOT_ID” newWebAppName=”A_UNIQUE_WEB_APP_NAME” newAppServicePlanName=”A_UNIQUE_PLAN_NAME” appServicePlanLocation=”REGION” –name “A_UNIQUE_SERVICE_NAME”

Step 7. Create a zip archive for deployment
select all of the files in the TimeBot folder to TimeBot.zip.

Step 8. Deploy and test the bot in Azure Portal
az webapp deployment source config-zip –resource-group “YOUR_RESOURCE_GROUP” –name “YOUR_WEB_APP_NAME” –src “TimeBot.zip”
In the Azure portal, in the resource group containing your resources, open the Bot Channels Registration resource (which will have the name you assigned to the BotId parameter when creating Azure resources).
In the Bot management section, select Test in Web Chat. Then wait for your bot to initialize.
Enter a message such as Hello and view the response from the bot, which should be Ask me what the time is.
Enter What is the time? and view the response.

Step 9. Get the Web Chat channel info from Azure Portal

Step 10. Update 13-bot-framework/web-client/default.html with Embed code and Secret Key

<head>
    <title>Time for Bots</title>
</head>
<Body>
    <h1>Time for Bots</h1>
    <p>Use this website to interact with the Time Bot</p>
    <!-- add the iframe for the bot here-->
    <iframe src='https://webchat.botframework.com/embed/hansBOTID?s=SoHXCaIxiSY.MBi3_Vc5Xl9oG7DX2TyqFR6UcOFX0dB9Nnyk-6lJ5jY'  style='min-width: 400px; width: 100%; min-height: 500px;'></iframe>
</Body>

then you can open the html page in Edge and test the Bots service:

Step 1. Clone the repository
Start Visual Studio Code.
Open the palette (SHIFT+CTRL+P) and run a Git: Clone command to clone the https://github.com/MicrosoftLearning/AI-102-AIEngineer repository to a local folder.

Step 2. Create a Cognitive Services resource with the following settings:
Subscription: Your Azure subscription
Resource group: Choose or create a resource group
Region: Choose any available region
Name: Enter a unique name
Pricing tier: Standard S0

When the resource has been deployed, go to it and view its Keys and Endpoint page. You will need the endpoint and one of the keys from this page in our API.

Step 3. In Visual Studio Code, in the Explorer pane, browse to the 05-analyze-text folder and expand the Python folder.
Right-click the text-analysis folder and open an integrated terminal. Then install the Text Analytics SDK package by running the appropriate command for your language preference:
pip install azure-ai-textanalytics==5.0.0, then edit .env with your Coginitive Services info:
COG_SERVICE_ENDPOINT=your_cognitive_services_endpoint
COG_SERVICE_KEY=your_cognitive_services_key

Step 4. Edit text-analysis.py as below:

from dotenv import load_dotenv
import os
# Import namespaces
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient

def main():
    try:
        # Get Configuration Settings
        load_dotenv()
        cog_endpoint = os.getenv('COG_SERVICE_ENDPOINT')
        cog_key = os.getenv('COG_SERVICE_KEY')

        # Create client using endpoint and key
        credential = AzureKeyCredential(cog_key)
        cog_client = TextAnalyticsClient(endpoint=cog_endpoint, credential=credential)

        # Analyze each text file in the reviews folder
        reviews_folder = 'reviews'
        for file_name in os.listdir(reviews_folder):
            # Read the file contents
            print('\n-------------\n' + file_name)
            text = open(os.path.join(reviews_folder, file_name), encoding='utf8').read()
            print('\n' + text)

            # Get language
            detectedLanguage = cog_client.detect_language(documents=[text])[0]
            print('\nLanguage: {}'.format(detectedLanguage.primary_language.name))

            # Get sentiment
            sentimentAnalysis = cog_client.analyze_sentiment(documents=[text])[0]
            print("\nSentiment: {}".format(sentimentAnalysis.sentiment))

            # Get key phrases
            phrases = cog_client.extract_key_phrases(documents=[text])[0].key_phrases
            if len(phrases) > 0:
                print("\nKey Phrases:")
                for phrase in phrases:
                    print('\t{}'.format(phrase))

            # Get entities
            entities = cog_client.recognize_entities(documents=[text])[0].entities
            if len(entities) > 0:
                print("\nEntities")
                for entity in entities:
                    print('\t{} ({})'.format(entity.text, entity.category))

            # Get linked entities
            entities = cog_client.recognize_linked_entities(documents=[text])[0].entities
            if len(entities) > 0:
                print("\nLinks")
                for linked_entity in entities:
                    print('\t{} ({})'.format(linked_entity.name, linked_entity.url))
    except Exception as ex:
        print(ex)
if __name__ == "__main__":
    main()

1. Create a machine learning workspace and open Azure Machine Learning Studio

2. Create a Compute instance in Azure Machine Learning Studio
3. Open instance Terminal to clone git repository
   git clone https://github.com/MicrosoftLearning/mslearn-dp100.git

4. Open instance Jupyter to run python code