In vain learned to resist the excitement, but it was too late to understand the real coldness; Write a chat robot to heal yourself!

Write in front

• Previously, I used Huawei's ModelArts platform to do similar small projects such as image recognition, zero coding, but I need to do my own data sets, labeling, selection algorithms, training models, etc. if you use it, you can directly tune the API.
• A former internship company in NLP is useful to check the data of some similar Prospectus Documents. I will do some data cleaning as a java developer, call the NLP interface to identify some tables, then write some logic in Java, sort and upload the data according to the requirements.
• I haven't contacted or studied in my later work, but I'm very interested in this aspect. I'm basically Xiaobai. I don't understand algorithms such as NLP. I bought a related book and got gray. Seeing this activity, I wanted to learn, so I participated.
• I don't understand anything, but the overall feeling is more cumbersome than Huawei's ModelArts. I don't know why I don't understand.
• Still, attach the activity link: https://bbs.csdn.net/topics/601636817

I learned to resist the excitement in vain, but I didn't have time to understand the real coldness-------- Da Chun Zhang

Before using, we need to understand the relevant concepts

What is Azure cognitive service?:

Cognitive services: services that provide cognitive understanding (seeing, listening, speaking, understanding, and even decision-making).

Cognitive services are mainly divided into four categories:

• image
• voice
• language
• policy decision

Azure cognitive service is a cloud based service with REST API and client library SDK, which can be used to help you build cognitive intelligence into applications. Even if you don't have artificial intelligence (AI) or data science skills, you can add cognitive functions to your applications. Azure cognitive services include various AI services, enabling you to build cognitive solutions that can see, listen, speak, understand, and even make decisions.

We want to be a robot that can chat with ourselves, so we need language understanding

What is language understanding (LUIS)?

Language understanding (LUIS) is a cloud based conversational AI service, which can apply custom machine learning intelligence in the natural language text of user dialogue, so as to predict the overall meaning and extract relevant detailed information. LUIS provides access through its custom portal, API, and SDK client libraries.

1. First, we need to create a resource

2. Service application construction

Publish the application service test and call the interface with python script

┌──[[email protected]]-[/liruilong] └─$ docker pull centos/python-36-centos7 ┌──[[email protected]]-[/liruilong] └─$ ls input luis_run.sh output predict.py ┌──[[email protected]]-[/liruilong] └─$ docker run --rm -it --name=chatbot -v $PWD/predict.py:/predict.py centos/python-36-centos7 /bin/bash (app-root) cd / (app-root) ls anaconda-post.log bin boot dev etc help.1 home lib lib64 media mnt opt predict.py proc root run sbin srv sys tmp usr var (app-root) python predict.py Traceback (most recent call last): File "predict.py", line 6, in <module> import requests ModuleNotFoundError: No module named 'requests' (app-root) pip install requests ......................... Collecting requests Downloading https:/ ..... (app-root) python predict.py {'query': 'How does one live?', 'prediction': {'topIntent': 'Life refueling', 'intents': {'Life refueling': {'score': 0.8969882}, 'Calendar.ShowNext': {'score': 0.58274937}, 'Calendar.FindCalendarWhen': {'score': 0.25383785}, 'Places.GetReviews': {'score': 0.24764298}, 'Utilities.ReadAloud': {'score': 0.20971665}, 'HomeAutomation.QueryState': {'score': 0.15509635}, 'Calendar.CheckAvailability': {'score': 0.12212229}, 'Places.GetPriceRange': {'score': 0.122063436},........

Test success: predict.py script

I feel that there are too few corpora, so basically speaking, the ox head is not right and the horse tail needs a lot of training

########### Python 3.6 ############# # # This quickstart shows how to predict the intent of an utterance by using the LUIS REST APIs. # Import module import requests try: ########## # Values to modify. # YOUR-APP-ID: The App ID GUID found on the www.luis.ai Application Settings page. # Replace with your own APP-ID appId = '949d3538-07df-4149-bee8-83dc7f4e11bd' # YOUR-PREDICTION-KEY: Your LUIS prediction key, 32 character value. prediction_key = '24440ef2829c45f0a061599ee00b496a' # YOUR-PREDICTION-ENDPOINT: Replace with your prediction endpoint. # For example, "https://westus.api.cognitive.microsoft.com/" prediction_endpoint = 'https://chatbot0.cognitiveservices.azure.cn/' # The utterance you want to use. # What you want to say to him utterance = 'How does one live?' ########## # The headers to use in this REST call. headers = { } # The URL parameters to use in this REST call. params ={ 'query': utterance, 'timezoneOffset': '0', 'verbose': 'true', 'show-all-intents': 'true', 'spellCheck': 'false', 'staging': 'false', 'subscription-key': prediction_key } # Make the REST call. response = requests.get(f'luis/prediction/v3.0/apps//slots/production/predict', headers=headers, params=params) # Display the results on the console. print(response.json()) except Exception as e: # Display the error string. print(f'')

3. Deploy services, start and run LUIS environment preparation

Pull image

┌──[[email protected]]-[/etc/docker] └─$ docker pull mcr.microsoft.com/azure-cognitive-services/luis Using default tag: latest latest: Pulling from azure-cognitive-services/luis b248fa9f6d2a: Pull complete 406741bedf7a: Pull complete 0767c6f1d20a: Pull complete 3687afaf861f: Pull complete 095d858c817b: Pull complete 45c05f18f223: Pull complete ce065679c887: Pull complete Digest: sha256:3583c9034a6e8f4ec78b2cf8d880d7eab7d960deebfc5ebd03853cffcdb879f0 Status: Downloaded newer image for mcr.microsoft.com/azure-cognitive-services/luis:latest mcr.microsoft.com/azure-cognitive-services/luis:latest ┌──[[email protected]]-[/etc/docker] └─$ # Well, this image seems to be useless. It's not used in the back.

Install and run the Docker container for LUIS

┌──[[email protected]]-[/liruilong] └─$ mkdir input output ┌──[[email protected]]-[/liruilong] └─$ ls input luis_run.sh output ┌──[[email protected]]-[/liruilong] └─$ cat luis_run.sh docker run --rm -it -p 5000:5000 --memory 4g --cpus 2 -v $PWD/input:/input -v $PWD/output:/output mcr.microsoft.com/azure-cognitive-services/language/luis Eula=accept Billing=https://chatbot0.cognitiveservices.azure.cn/ ApiKey=24440ef2829c45f0a061599ee00b496a ## fill in your own parameters here ┌──[[email protected]]-[/liruilong] └─$ docker run --rm -it -p 5000:5000 --memory 4g --cpus 2 -v $PWD/input:/input -v $PWD/output:/output mcr.microsoft.com/azure-cognitive-services/language/luis Eula=accept Billing=https://Chatbot0. Cognitiveservices. Azure. CN / apikey = 24440ef2829c45f0a061569ee00b496a ## start container EULA Notice: Copyright © Microsoft Corporation 2020. This Cognitive Services Container image is made available to you under the terms [https://go.microsoft.com/fwlink/?linkid=2018657] governing your subscription to Microsoft Azure Services (including the Online Services Terms [https://go.microsoft.com/fwlink/?linkid=2018760]). If you do not have a valid Azure subscription, then you may not use this container. Using '/input' for reading models and other read-only data. Using '/output/luis/ff2a18ee78a1' for writing logs and other output data. Logging to console. Submitting metering to 'https://chatbot0.cognitiveservices.azure.cn/'. warn: Microsoft.AspNetCore.DataProtection.KeyManagement.XmlKeyManager[35] No XML encryptor configured. Key may be persisted to storage in unencrypted form. warn: Microsoft.AspNetCore.Server.Kestrel[0] Overriding address(es) 'http://+:80'. Binding to endpoints defined in UseKestrel() instead. Hosting environment: Production Content root path: /app Now listening on: http://0.0.0.0:5000 Application started. Press Ctrl+C to shut down.

Well, it's too late. The rest will be written tomorrow

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