Hi Guys,
Today, I’ll be discussing the following topic – “How to analyze text using IBM Watson implementing through Python.”
IBM has significantly improved in the field of Visual Image Analysis or Text language analysis using its IBM Watson cloud platform. In this particular topic, we’ll be exploring the natural languages only.
To access IBM API, we need to first create an IBM Cloud account from this site.
Let us quickly go through the steps to create the IBM Language Understanding service. Click the Catalog on top of your browser menu as shown in the below picture –
After that, click the AI option on your left-hand side of the panel marked in RED.
Click the Watson-Studio & later choose the plan. In our case, We’ll select the “Lite” option as IBM provided this platform for all the developers to explore their cloud for free.
Clicking the create option will lead to a blank page of Watson Studio as shown below –
And, now, we need to click the Get Started button to launch it. This will lead to Create Project page, which can be done using the following steps –
Now, clicking the create a project will lead you to the next screen –
You can choose either an empty project, or you can create it from a sample file. In this case, we’ll be selecting the first option & this will lead us to the below page –
And, then you will click the “Create” option, which will lead you to the next screen –
Now, you need to click “Add to Project.” This will give you a variety of services that you want to explore/use from the list. If you want to create your own natural language classifier, which you can do that as follows –
Once, you click it – you need to select the associate service –
Here, you need to click the hyperlink, which prompts to the next screen –
You need to check the price for both the Visual & Natural Language Classifier. They are pretty expensive. The visual classifier has the Lite plan. However, it has limitations of output.
Clicking the “Create” will prompt to the next screen –
After successful creation, you will be redirected to the following page –
Now, We’ll be adding our “Natural Language Understand” for our test –
This will prompt the next screen –
Once, it is successful. You will see the service registered as shown below –
If you click the service marked in RED, it will lead you to another page, where you will get the API Key & Url. You need both of this information in Python application to access this API as shown below –
Now, we’re ready with the necessary cloud set-up. After this, we need to install the Python package for IBM Cloud as shown below –
We’ve noticed that, recently, IBM has launched one upgraded package. Hence, we installed that one as well. I would recommend you to install this second package directly instead of the first one shown above –
Now, we’re done with our set-up.
Let’s see the directory structure –
We’ll be discussing only the main calling script & class script. However, we’ll be posting the parameters without discussing it. And, we won’t discuss clsL.py as we’ve already discussed that in our previous post.
1. clsConfig.py (This script contains all the parameter details.)
############################################## #### Written By: SATYAKI DE #### #### Written On: 04-Apr-2020 #### #### #### #### Objective: This script is a config #### #### file, contains all the keys for #### #### IBM Cloud API. Application will #### #### process these information & perform #### #### various analysis on IBM Watson cloud.#### ############################################## import os import platform as pl class clsConfig(object): Curr_Path = os.path.dirname(os.path.realpath(__file__)) os_det = pl.system() if os_det == "Windows": sep = '\\' else: sep = '/' config = { 'APP_ID': 1, 'SERVICE_URL': "https://api.eu-gb.natural-language-understanding.watson.cloud.ibm.com/instances/xxxxxxxxxxxxxxXXXXXXXXXXxxxxxxxxxxxxxxxx", 'API_KEY': "Xxxxxxxxxxxxxkdkdfifd984djddkkdkdkdsSSdkdkdd", 'API_TYPE': "application/json", 'CACHE': "no-cache", 'CON': "keep-alive", 'ARCH_DIR': Curr_Path + sep + 'arch' + sep, 'PROFILE_PATH': Curr_Path + sep + 'profile' + sep, 'LOG_PATH': Curr_Path + sep + 'log' + sep, 'REPORT_PATH': Curr_Path + sep + 'report', 'SRC_PATH': Curr_Path + sep + 'Src_File' + sep, 'APP_DESC_1': 'IBM Watson Language Understand!', 'DEBUG_IND': 'N', 'INIT_PATH': Curr_Path }
Note that you will be placing your API_KEY & URL here, as shown in the configuration file.
2. clsIBMWatson.py (This is the main script, which will invoke the IBM Watson API based on the input from the user & return 0 if successful.)
############################################## #### Written By: SATYAKI DE #### #### Written On: 04-Apr-2020 #### #### Modified On 04-Apr-2020 #### #### #### #### Objective: Main scripts to invoke #### #### IBM Watson Language Understand API. #### ############################################## import logging from clsConfig import clsConfig as cf import clsL as cl import json from ibm_watson import NaturalLanguageUnderstandingV1 from ibm_cloud_sdk_core.authenticators import IAMAuthenticator from ibm_watson.natural_language_understanding_v1 import Features, EntitiesOptions, KeywordsOptions, SentimentOptions, CategoriesOptions, ConceptsOptions from ibm_watson import ApiException class clsIBMWatson: def __init__(self): self.api_key = cf.config['API_KEY'] self.service_url = cf.config['SERVICE_URL'] def calculateExpressionFromUrl(self, inputUrl, inputVersion): try: api_key = self.api_key service_url = self.service_url print('-' * 60) print('Beginning of the IBM Watson for Input Url.') print('-' * 60) authenticator = IAMAuthenticator(api_key) # Authentication via service credentials provided in our config files service = NaturalLanguageUnderstandingV1(version=inputVersion, authenticator=authenticator) service.set_service_url(service_url) response = service.analyze( url=inputUrl, features=Features(entities=EntitiesOptions(), sentiment=SentimentOptions(), concepts=ConceptsOptions())).get_result() print(json.dumps(response, indent=2)) return 0 except ApiException as ex: print('-' * 60) print("Method failed for Url with status code " + str(ex.code) + ": " + ex.message) print('-' * 60) return 1 def calculateExpressionFromText(self, inputText, inputVersion): try: api_key = self.api_key service_url = self.service_url print('-' * 60) print('Beginning of the IBM Watson for Input Url.') print('-' * 60) authenticator = IAMAuthenticator(api_key) # Authentication via service credentials provided in our config files service = NaturalLanguageUnderstandingV1(version=inputVersion, authenticator=authenticator) service.set_service_url(service_url) response = service.analyze( text=inputText, features=Features(entities=EntitiesOptions(), sentiment=SentimentOptions(), concepts=ConceptsOptions())).get_result() print(json.dumps(response, indent=2)) return 0 except ApiException as ex: print('-' * 60) print("Method failed for Url with status code " + str(ex.code) + ": " + ex.message) print('-' * 60) return 1
Some of the key lines from the above snippet –
authenticator = IAMAuthenticator(api_key) # Authentication via service credentials provided in our config files service = NaturalLanguageUnderstandingV1(version=inputVersion, authenticator=authenticator) service.set_service_url(service_url)
By providing the API Key & Url, the application is initiating the service for Watson.
response = service.analyze( url=inputUrl, features=Features(entities=EntitiesOptions(), sentiment=SentimentOptions(), concepts=ConceptsOptions())).get_result()
Based on your type of input, it will bring the features of entities, sentiment & concepts here. Apart from that, you can additionally check the following features as well – Keywords & Categories.
3. callIBMWatsonAPI.py (This is the first calling script. Based on user choice, it will receive input either as Url or as the plain text & then analyze it.)
############################################## #### Written By: SATYAKI DE #### #### Written On: 04-Apr-2020 #### #### Modified On 04-Apr-2020 #### #### #### #### Objective: Main calling scripts. #### ############################################## from clsConfig import clsConfig as cf import clsL as cl import logging import datetime import clsIBMWatson as cw # Disbling Warning def warn(*args, **kwargs): pass import warnings warnings.warn = warn # Lookup functions from # Azure cloud SQL DB var = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S") def main(): try: ret_1 = 0 general_log_path = str(cf.config['LOG_PATH']) # Enabling Logging Info logging.basicConfig(filename=general_log_path + 'IBMWatson_NaturalLanguageAnalysis.log', level=logging.INFO) # Initiating Log Class l = cl.clsL() # Moving previous day log files to archive directory log_dir = cf.config['LOG_PATH'] curr_ver =datetime.datetime.now().strftime("%Y-%m-%d") tmpR0 = "*" * 157 logging.info(tmpR0) tmpR9 = 'Start Time: ' + str(var) logging.info(tmpR9) logging.info(tmpR0) print("Log Directory::", log_dir) tmpR1 = 'Log Directory::' + log_dir logging.info(tmpR1) print('Welcome to IBM Wantson Language Understanding Calling Program: ') print('-' * 60) print('Please Press 1 for Understand the language from Url.') print('Please Press 2 for Understand the language from your input-text.') input_choice = int(input('Please provide your choice:')) # Create the instance of the IBM Watson Class x2 = cw.clsIBMWatson() # Let's pass this to our map section if input_choice == 1: textUrl = str(input('Please provide the complete input url:')) ret_1 = x2.calculateExpressionFromUrl(textUrl, curr_ver) elif input_choice == 2: inputText = str(input('Please provide the input text:')) ret_1 = x2.calculateExpressionFromText(inputText, curr_ver) else: print('Invalid options!') if ret_1 == 0: print('Successful IBM Watson Language Understanding Generated!') else: print('Failed to generate IBM Watson Language Understanding!') print("-" * 60) print() print('Finding Analysis points..') print("*" * 157) logging.info('Finding Analysis points..') logging.info(tmpR0) tmpR10 = 'End Time: ' + str(var) logging.info(tmpR10) logging.info(tmpR0) except ValueError as e: print(str(e)) print("Invalid option!") logging.info("Invalid option!") except Exception as e: print("Top level Error: args:{0}, message{1}".format(e.args, e.message)) if __name__ == "__main__": main()
This script is pretty straight forward as it is first creating an instance of the main class & then based on the user input, it is calling the respective functions here.
As of now, IBM Watson can work on a list of languages, which are available here.
If you want to start from scratch, please refer to the following link.
Please find the screenshot of our application run –
Case 1 (With Url):
Case 2 (With Plain text):
Now, Don’t forget to delete all the services from your IBM Cloud.
As you can see, from the service, you need to delete all the services one-by-one as shown in the figure.
So, we’ve done it.
To explore my photography, you can visit the following link.
I’ll be posting another new post in the coming days. Till then, Happy Avenging! 😀
Note: All the data posted here are representational data & available over the internet & for educational purpose only.
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