Obsei is a low code AI powered automation tool.

Last update: Dec 31, 2022

Overview

Note: Obsei is still in alpha stage hence carefully use it in Production. Also as it is constantly undergoing development hence master branch may contains many breaking changes. Please use released version.

Obsei is an open-source low-code AI powered automation tool. Obsei consist of -

Observer, observes platform like Twitter, Facebook, App Stores, Google reviews, Amazon reviews, News, Website etc and feed that information to,
Analyzer, which perform text analysis like classification, sentiment, translation, PII etc and feed that information to,
Informer, which send it to ticketing system, data store, dataframe etc for further action and analysis.

Future thoughts -

Text, Image, Audio, Documents and Video oriented workflows
Collect data from every possible private and public channels
Add each possible to AI piece which can automate manual cognitive workflows

Introductory demo video

Use cases

Obsei use cases are following, but not limited to -

Social listening
Alerting/Notification when user complaints on social media
Automatic customer issue creation based on sentiment analysis (reduction of MTTD)
Proper tagging of ticket based for example login issue, signup issue, delivery issue etc (reduction of MTTR)
Checking effectiveness of social media marketing campaign
Extraction of deeper insight from feedbacks on various platforms
Research purpose
Many more based on creativity 💡

Companies/Projects using Obsei

Here are some companies/projects (alphabetical order) using Obsei. To add your company/project to the list, please raise a PR or contact us via email.

1Page: Giving a better context in meetings and calls
Spacepulse: The operating system for spaces
Superblog: A blazing fast alternative to WordPress and Medium
Zolve: Creating a financial world beyond borders

Tutorials

Sr. No.	Workflow	Colab	Binder
1	Observe app reviews from Google play store, Analyze them via performing text classification and then Inform them on console via logger
1	PlayStore Reviews → Classification → Logger
2	Observe app reviews from Google play store, PreProcess text via various text cleaning function, Analyze them via performing text classification, Inform them to Pandas DataFrame and store resultant CSV to Google Drive
2	PlayStore Reviews → PreProcessing → Classification → Pandas DataFrame → CSV in Google Drive
3	Observe app reviews from Apple app store, PreProcess text via various text cleaning function, Analyze them via performing text classification, Inform them to Pandas DataFrame and store resultant CSV to Google Drive
3	AppStore Reviews → PreProcessing → Classification → Pandas DataFrame → CSV in Google Drive
4	Observe news article from Google news, PreProcess text via various text cleaning function, Analyze them via performing text classification while splitting text in small chunks and later computing final inference using given formula
4	Google News → Text Cleaner → Text Splitter → Classification → Inference Aggregator

Demo

We have a minimal streamlit based UI that you can use to test Obsei.

Watch UI demo video

To test remotely, just open: Obsei Demo Link (Note: Due to rate limit sometime Streamlit demo might not work, hence please use docker image locally.)

To test locally, just run

docker run -d --name obesi-ui -p 8501:8501 obsei/obsei-ui-demo

# You can find the UI at http://localhost:8501

To run Obsei workflow easily using GitHub Actions (no signups and cloud hosting require), refer repo for more information.

Documentation

For detailed installation instructions, usages and example refer documentation.

Support and Release Matrix

	Linux	Mac	Windows¹	Remark
Tests	✅	✅	✅	Low Coverage as difficult to test 3rd party libs
PIP	✅	✅	✅	Fully Supported
Conda²	✅	✅	✅	Partially Supported

1 On Windows you have to install pytorch manually. Refer Pytorch official instruction. ↩

2 Conda channel missing few dependencies, hence install missing dependencies manually - ↩

Missing Conda dependencies -

pip install presidio-analyzer
pip install presidio-anonymizer
pip install zenpy
pip install searchtweets-v2
pip install google-play-scraper
pip install tweet-preprocessor
pip install gnews
pip install trafilatura
pip install python-facebook-api

How to use

Expend following steps and create your workflow -

Step 1: Prerequisite

Install following if system do not have -

Install Python 3.7+
Install PIP (Optional if you prefer Conda)
Install Conda (Optional if you prefer PIP)

Step 2: Install Obsei

You can install Obsei either via PIP or Conda based on your preference.

NOTE: On Windows you have to install pytorch manually. Refer https://pytorch.org/get-started/locally/

Install via PIP:

To install latest released version -

pip install obsei

Install from master branch (if you want to try the latest features):

git clone https://github.com/obsei/obsei.git
cd obsei
pip install --editable .

Install via Conda:

To install latest released version -

conda install -c lalitpagaria obsei

Install from master branch (if you want to try the latest features):

git clone https://github.com/obsei/obsei.git
cd obsei
conda env create -f conda/environment.yml

For GPU based local environment -

git clone https://github.com/obsei/obsei.git
cd obsei
conda env create -f conda/gpu-environment.yml

Step 3: Configure Source/Observer

Twitter

` (day|hour|minute) cred_info=TwitterCredentials( # Enter your twitter consumer key and secret. Get it from https://developer.twitter.com/en/apply-for-access consumer_key="

", consumer_secret="

", bearer_token='

', ) ) # initialize tweets retriever source = TwitterSource() ">
from obsei.source.twitter_source import TwitterCredentials, TwitterSource, TwitterSourceConfig

# initialize twitter source config
source_config = TwitterSourceConfig(
   keywords=["issue"], # Keywords, @user or #hashtags
   lookup_period="1h", # Lookup period from current time, format: `
             
              
               ` (day|hour|minute)
              
             
   cred_info=TwitterCredentials(
       # Enter your twitter consumer key and secret. Get it from https://developer.twitter.com/en/apply-for-access
       consumer_key="
             
              "
             ,
       consumer_secret="
             
              "
             ,
       bearer_token='
             
              '
             ,
   )
)

# initialize tweets retriever
source = TwitterSource()

Facebook

` (day|hour|minute) cred_info=FacebookCredentials( # Enter your facebook app_id, app_secret and long_term_token. Get it from https://developers.facebook.com/apps/ app_id="

", app_secret="

", long_term_token="

", ) ) # initialize facebook post comments retriever source = FacebookSource() ">
from obsei.source.facebook_source import FacebookCredentials, FacebookSource, FacebookSourceConfig

# initialize facebook source config
source_config = FacebookSourceConfig(
   page_id="110844591144719", # Facebook page id, for example this one for Obsei
   lookup_period="1h", # Lookup period from current time, format: `
             
              
               ` (day|hour|minute)
              
             
   cred_info=FacebookCredentials(
       # Enter your facebook app_id, app_secret and long_term_token. Get it from https://developers.facebook.com/apps/
       app_id="
             
              "
             ,
       app_secret="
             
              "
             ,
       long_term_token="
             
              "
             ,
   )
)

# initialize facebook post comments retriever
source = FacebookSource()

Email

` (day|hour|minute) ) # initialize email retriever source = EmailSource() ">
from obsei.source.email_source import EmailConfig, EmailCredInfo, EmailSource

# initialize email source config
source_config = EmailConfig(
   # List of IMAP servers for most commonly used email providers
   # https://www.systoolsgroup.com/imap/
   # Also, if you're using a Gmail account then make sure you allow less secure apps on your account -
   # https://myaccount.google.com/lesssecureapps?pli=1
   # Also enable IMAP access -
   # https://mail.google.com/mail/u/0/#settings/fwdandpop
   imap_server="imap.gmail.com", # Enter IMAP server
   cred_info=EmailCredInfo(
       # Enter your email account username and password
       username="
          
           "
          ,
       password="
          
           "
          
   ),
   lookup_period="1h" # Lookup period from current time, format: `
          
           
            ` (day|hour|minute)
           
          
)

# initialize email retriever
source = EmailSource()

Google Maps Reviews Scrapper

", # Enter Google Maps link or place id # For example below is for the "Taj Mahal" queries=["https://www.google.co.in/maps/place/Taj+Mahal/@27.1751496,78.0399535,17z/data=!4m5!3m4!1s0x39747121d702ff6d:0xdd2ae4803f767dde!8m2!3d27.1751448!4d78.0421422"], number_of_reviews=10, ) # initialize Outscrapper Maps review retriever source = OSGoogleMapsReviewsSource() ">
from obsei.source import OSGoogleMapsReviewsSource, OSGoogleMapsReviewsConfig

# initialize Outscrapper Maps review source config
source_config = OSGoogleMapsReviewsConfig(
   # Collect API key from https://outscraper.com/
   api_key="
          
           "
          ,
   # Enter Google Maps link or place id
   # For example below is for the "Taj Mahal"
   queries=["https://www.google.co.in/maps/place/Taj+Mahal/@27.1751496,78.0399535,17z/data=!4m5!3m4!1s0x39747121d702ff6d:0xdd2ae4803f767dde!8m2!3d27.1751448!4d78.0421422"],
   number_of_reviews=10,
)


# initialize Outscrapper Maps review retriever
source = OSGoogleMapsReviewsSource()

AppStore Reviews Scrapper

` (day|hour|minute) ) # initialize app store reviews retriever source = AppStoreScrapperSource() ">
from obsei.source.appstore_scrapper import AppStoreScrapperConfig, AppStoreScrapperSource

# initialize app store source config
source_config = AppStoreScrapperConfig(
   # Need two parameters app_id and country.
   # `app_id` can be found at the end of the url of app in app store.
   # For example - https://apps.apple.com/us/app/xcode/id497799835
   # `310633997` is the app_id for xcode and `us` is country.
   countries=["us"],
   app_id="310633997",
   lookup_period="1h" # Lookup period from current time, format: `
          
           
            ` (day|hour|minute)
           
          
)


# initialize app store reviews retriever
source = AppStoreScrapperSource()

Play Store Reviews Scrapper

` (day|hour|minute) ) # initialize play store reviews retriever source = PlayStoreScrapperSource() ">
from obsei.source.playstore_scrapper import PlayStoreScrapperConfig, PlayStoreScrapperSource

# initialize play store source config
source_config = PlayStoreScrapperConfig(
   # Need two parameters package_name and country.
   # `package_name` can be found at the end of the url of app in play store.
   # For example - https://play.google.com/store/apps/details?id=com.google.android.gm&hl=en&gl=US
   # `com.google.android.gm` is the package_name for xcode and `us` is country.
   countries=["us"],
   package_name="com.google.android.gm",
   lookup_period="1h" # Lookup period from current time, format: `
          
           
            ` (day|hour|minute)
           
          
)

# initialize play store reviews retriever
source = PlayStoreScrapperSource()

Reddit

` (day|hour|minute) ) # initialize reddit retriever source = RedditSource() ">
from obsei.source.reddit_source import RedditConfig, RedditSource, RedditCredInfo

# initialize reddit source config
source_config = RedditConfig(
   subreddits=["wallstreetbets"], # List of subreddits
   # Reddit account username and password
   # You can also enter reddit client_id and client_secret or refresh_token
   # Create credential at https://www.reddit.com/prefs/apps
   # Also refer https://praw.readthedocs.io/en/latest/getting_started/authentication.html
   # Currently Password Flow, Read Only Mode and Saved Refresh Token Mode are supported
   cred_info=RedditCredInfo(
       username="
          
           "
          ,
       password="
          
           "
          
   ),
   lookup_period="1h" # Lookup period from current time, format: `
          
           
            ` (day|hour|minute)
           
          
)

# initialize reddit retriever
source = RedditSource()

Reddit Scrapper

Note: Reddit heavily rate limit scrappers, hence use it to fetch small data during long period

` (day|hour|minute) ) # initialize reddit retriever source = RedditScrapperSource() ">
from obsei.source.reddit_scrapper import RedditScrapperConfig, RedditScrapperSource

# initialize reddit scrapper source config
source_config = RedditScrapperConfig(
   # Reddit subreddit, search etc rss url. For proper url refer following link -
   # Refer https://www.reddit.com/r/pathogendavid/comments/tv8m9/pathogendavids_guide_to_rss_and_reddit/
   url="https://www.reddit.com/r/wallstreetbets/comments/.rss?sort=new",
   lookup_period="1h" # Lookup period from current time, format: `
          
           
            ` (day|hour|minute)
           
          
)

# initialize reddit retriever
source = RedditScrapperSource()

Google News

from obsei.source.google_news_source import GoogleNewsConfig, GoogleNewsSource

# initialize Google News source config
source_config = GoogleNewsConfig(
   query='bitcoin',
   max_results=5,
   # To fetch full article text enable `fetch_article` flag
   # By default google news gives title and highlight
   fetch_article=True,
)

# initialize Google News retriever
source = GoogleNewsSource()

Web Crawler

from obsei.source.website_crawler_source import TrafilaturaCrawlerConfig, TrafilaturaCrawlerSource

# initialize website crawler source config
source_config = TrafilaturaCrawlerConfig(
   urls=['https://obsei.github.io/obsei/']
)

# initialize website text retriever
source = TrafilaturaCrawlerSource()

Pandas DataFrame

import pandas as pd
from obsei.source.pandas_source import PandasSource, PandasSourceConfig

# Initialize your Pandas DataFrame from your sources like csv, excel, sql etc
# In following example we are reading csv which have two columns title and text
csv_file = "https://raw.githubusercontent.com/deepset-ai/haystack/master/tutorials/small_generator_dataset.csv"
dataframe = pd.read_csv(csv_file)

# initialize pandas sink config
sink_config = PandasSourceConfig(
   dataframe=dataframe,
   include_columns=["score"],
   text_columns=["name", "degree"],
)

# initialize pandas sink
sink = PandasSource()

Step 4: Configure Analyzer

Note: To run transformers in an offline mode, check transformers offline mode.

Some analyzer support GPU and to utilize pass device parameter. List of possible values of device parameter (default value auto):

auto: GPU (cuda:0) will be used if available otherwise CPU will be used
cpu: CPU will be used
cuda:{id} - GPU will be used with provided CUDA device id

Text Classification

Text classification, classify text into user provided categories.

from obsei.analyzer.classification_analyzer import ClassificationAnalyzerConfig, ZeroShotClassificationAnalyzer

# initialize classification analyzer config
# It can also detect sentiments if "positive" and "negative" labels are added.
analyzer_config=ClassificationAnalyzerConfig(
   labels=["service", "delay", "performance"],
)

# initialize classification analyzer
# For supported models refer https://huggingface.co/models?filter=zero-shot-classification
text_analyzer = ZeroShotClassificationAnalyzer(
   model_name_or_path="typeform/mobilebert-uncased-mnli",
   device="auto"
)

Sentiment Analyzer

Sentiment Analyzer, detect the sentiment of the text. Text classification can also perform sentiment analysis but if you don't want to use heavy-duty NLP model then use less resource hungry dictionary based Vader Sentiment detector.

from obsei.analyzer.sentiment_analyzer import VaderSentimentAnalyzer

# Vader does not need any configuration settings
analyzer_config=None

# initialize vader sentiment analyzer
text_analyzer = VaderSentimentAnalyzer()

NER Analyzer

NER (Named-Entity Recognition) Analyzer, extract information and classify named entities mentioned in text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc

from obsei.analyzer.ner_analyzer import NERAnalyzer

# NER analyzer does not need configuration settings
analyzer_config=None

# initialize ner analyzer
# For supported models refer https://huggingface.co/models?filter=token-classification
text_analyzer = NERAnalyzer(
   model_name_or_path="elastic/distilbert-base-cased-finetuned-conll03-english",
   device = "auto"
)

Translator

from obsei.analyzer.translation_analyzer import TranslationAnalyzer

# Translator does not need analyzer config
analyzer_config = None

# initialize translator
# For supported models refer https://huggingface.co/models?pipeline_tag=translation
analyzer = TranslationAnalyzer(
   model_name_or_path="Helsinki-NLP/opus-mt-hi-en",
   device = "auto"
)

PII Anonymizer

from obsei.analyzer.pii_analyzer import PresidioEngineConfig, PresidioModelConfig, \
   PresidioPIIAnalyzer, PresidioPIIAnalyzerConfig

# initialize pii analyzer's config
analyzer_config = PresidioPIIAnalyzerConfig(
   # Whether to return only pii analysis or anonymize text
   analyze_only=False,
   # Whether to return detail information about anonymization decision
   return_decision_process=True
)

# initialize pii analyzer
analyzer = PresidioPIIAnalyzer(
   engine_config=PresidioEngineConfig(
       # spacy and stanza nlp engines are supported
       # For more info refer
       # https://microsoft.github.io/presidio/analyzer/developing_recognizers/#utilize-spacy-or-stanza
       nlp_engine_name="spacy",
       # Update desired spacy model and language
       models=[PresidioModelConfig(model_name="en_core_web_lg", lang_code="en")]
   )
)

Dummy Analyzer

Dummy Analyzer, do nothing it simply used for transforming input (TextPayload) to output (TextPayload) also adding user supplied dummy data.

from obsei.analyzer.dummy_analyzer import DummyAnalyzer, DummyAnalyzerConfig

# initialize dummy analyzer's configuration settings
analyzer_config = DummyAnalyzerConfig()

# initialize dummy analyzer
analyzer = DummyAnalyzer()

Step 5: Configure Sink/Informer

Slack

", # To get channel id refer https://stackoverflow.com/questions/40940327/what-is-the-simplest-way-to-find-a-slack-team-id-and-a-channel-id channel_id="C01LRS6CT9Q" ) # initialize slack sink sink = SlackSink() ">
from obsei.sink.slack_sink import SlackSink, SlackSinkConfig

# initialize slack sink config
sink_config = SlackSinkConfig(
   # Provide slack bot/app token
   # For more detail refer https://slack.com/intl/en-de/help/articles/215770388-Create-and-regenerate-API-tokens
   slack_token="
          
           "
          ,
   # To get channel id refer https://stackoverflow.com/questions/40940327/what-is-the-simplest-way-to-find-a-slack-team-id-and-a-channel-id
   channel_id="C01LRS6CT9Q"
)

# initialize slack sink
sink = SlackSink()

Zendesk

from obsei.sink.zendesk_sink import ZendeskSink, ZendeskSinkConfig, ZendeskCredInfo

# initialize zendesk sink config
sink_config = ZendeskSinkConfig(
   # For custom domain refer http://docs.facetoe.com.au/zenpy.html#custom-domains
   # Mainly you can do this by setting the environment variables:
   # ZENPY_FORCE_NETLOC
   # ZENPY_FORCE_SCHEME (default to https)
   # when set it will force request on:
   # {scheme}://{netloc}/endpoint
   # provide zendesk domain
   domain="zendesk.com",
   # provide subdomain if you have one
   subdomain=None,
   # Enter zendesk user details
   cred_info=ZendeskCredInfo(
       email="
         
          "
         ,
       password="
         
          "
         
   )
)

# initialize zendesk sink
sink = ZendeskSink()

Jira

from obsei.sink.jira_sink import JiraSink, JiraSinkConfig

# For testing purpose you can start jira server locally
# Refer https://developer.atlassian.com/server/framework/atlassian-sdk/atlas-run-standalone/

# initialize Jira sink config
sink_config = JiraSinkConfig(
   url="http://localhost:2990/jira", # Jira server url
    # Jira username & password for user who have permission to create issue
   username="
         
          "
         ,
   password="
         
          "
         ,
   # Which type of issue to be created
   # For more information refer https://support.atlassian.com/jira-cloud-administration/docs/what-are-issue-types/
   issue_type={"name": "Task"},
   # Under which project issue to be created
   # For more information refer https://support.atlassian.com/jira-software-cloud/docs/what-is-a-jira-software-project/
   project={"key": "CUS"},
)

# initialize Jira sink
sink = JiraSink()

ElasticSearch

from obsei.sink.elasticsearch_sink import ElasticSearchSink, ElasticSearchSinkConfig

# For testing purpose you can start Elasticsearch server locally via docker
# `docker run -d --name elasticsearch -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.9.2`

# initialize Elasticsearch sink config
sink_config = ElasticSearchSinkConfig(
   # Elasticsearch server hostname
   host="localhost",
   # Elasticsearch server port
   port=9200,
   # Index name, it will create if not exist
   index_name="test",
)

# initialize Elasticsearch sink
sink = ElasticSearchSink()

Http

from obsei.sink.http_sink import HttpSink, HttpSinkConfig

# For testing purpose you can create mock http server via postman
# For more details refer https://learning.postman.com/docs/designing-and-developing-your-api/mocking-data/setting-up-mock/

# initialize http sink config (Currently only POST call is supported)
sink_config = HttpSinkConfig(
   # provide http server url
   url="https://localhost:8080/api/path",
   # Here you can add headers you would like to pass with request
   headers={
       "Content-type": "application/json"
   }
)

# To modify or converting the payload, create convertor class
# Refer obsei.sink.dailyget_sink.PayloadConvertor for example

# initialize http sink
sink = HttpSink()

Pandas DataFrame

from pandas import DataFrame
from obsei.sink.pandas_sink import PandasSink, PandasSinkConfig

# initialize pandas sink config
sink_config = PandasSinkConfig(
   dataframe=DataFrame()
)

# initialize pandas sink
sink = PandasSink()

Logger

This is useful for testing and dry run checking of pipeline.

from obsei.sink.logger_sink import LoggerSink, LoggerSinkConfig
import logging
import sys

logger = logging.getLogger("Obsei")
logging.basicConfig(stream=sys.stdout, level=logging.INFO)

# initialize logger sink config
sink_config = LoggerSinkConfig(
   logger=logger,
   level=logging.INFO
)

# initialize logger sink
sink = LoggerSink()

Step 6: Join and create workflow

source will fetch data from selected the source, then feed that to analyzer for processing, whose output we feed into sink to get notified at that sink.

# Uncomment if you want logger
# import logging
# import sys
# logger = logging.getLogger(__name__)
# logging.basicConfig(stream=sys.stdout, level=logging.INFO)

# This will fetch information from configured source ie twitter, app store etc
source_response_list = source.lookup(source_config)

# Uncomment if you want to log source response
# for idx, source_response in enumerate(source_response_list):
#     logger.info(f"source_response#'{idx}'='{source_response.__dict__}'")

# This will execute analyzer (Sentiment, classification etc) on source data with provided analyzer_config
analyzer_response_list = text_analyzer.analyze_input(
    source_response_list=source_response_list,
    analyzer_config=analyzer_config
)

# Uncomment if you want to log analyzer response
# for idx, an_response in enumerate(analyzer_response_list):
#    logger.info(f"analyzer_response#'{idx}'='{an_response.__dict__}'")

# Analyzer output added to segmented_data
# Uncomment inorder to log it
# for idx, an_response in enumerate(analyzer_response_list):
#    logger.info(f"analyzed_data#'{idx}'='{an_response.segmented_data.__dict__}'")

# This will send analyzed output to configure sink ie Slack, Zendesk etc
sink_response_list = sink.send_data(analyzer_response_list, sink_config)

# Uncomment if you want to log sink response
# for sink_response in sink_response_list:
#     if sink_response is not None:
#         logger.info(f"sink_response='{sink_response}'")

Step 7: Execute workflow

Copy code snippets from Step 3 to Step 6 into python file for example example.py and execute following command -

python example.py

Articles

Sr. No.	Title	Author
1	AI based Comparative Customer Feedback Analysis Using Obsei	Reena Bapna
2	LinkedIn App - User Feedback Analysis	Himanshu Sharma

Tips

Handle large text classification

Upcoming Release

Upcoming release plan and progress can be tracked at link (Suggestions are welcome).

Discussion Forum

Discussion about Obsei can be done at community forum

Contribution

First off, thank you for even considering contributing to this package, every contribution big or small is greatly appreciated. Please refer our Contribution Guideline and Code of Conduct.

Changelog

Refer releases and projects.

Security Issue

For any security issue please contact us via email

Stargazers over time

Attribution

This could not have been possible without these open source software.

Acknowledgement

We would like to thank DailyGet for continuous support and encouragement. Please check DailyGet out. it is a platform which can easily be configured to solve any business process automation requirements.

Comments

Add analyzer batching

@lalitpagaria I have added the batching on ZeroShotAnalyzer to get your feedback on changes that I made and design in general, I used the following code to benchmark performance, seems like batch size of 1 are outperforming larger batches which is counter-intuitive this may change on GPU I have only tested on local CPU

if __name__ == "__main__":
    import timeit  ## for benchmarking execution

    start = timeit.default_timer()
    GOOD_TEXT = """If anyone is interested... these are our hosts. I can’t recommend them enough, Abc & Pbc.

    The unit is just lovely, you go to sleep & wake up to this incredible place, and you have use of a Weber grill and a ridiculously indulgent hot-tub under the stars"""

    BAD_TEXT = """I had the worst experience ever with XYZ in Egypt. Bad Cars, asking to pay in cash,  do not have enough fuel,  do not open AC,  wait far away from my location until the trip is cancelled,  call and ask about the destination then cancel, and more. Worst service."""

    MIXED_TEXT = """I am mixed"""

    TEXTS = [
        GOOD_TEXT,
        BAD_TEXT,
        MIXED_TEXT,
        GOOD_TEXT,
        BAD_TEXT,
        MIXED_TEXT,
        GOOD_TEXT,
        BAD_TEXT,
        MIXED_TEXT,
        GOOD_TEXT,
        BAD_TEXT,
        MIXED_TEXT,
        GOOD_TEXT,
        BAD_TEXT,
        MIXED_TEXT,
        GOOD_TEXT,
        BAD_TEXT,
        MIXED_TEXT,
        GOOD_TEXT,
        BAD_TEXT,
        MIXED_TEXT,
        GOOD_TEXT,
        BAD_TEXT,
        MIXED_TEXT,
        GOOD_TEXT,
        BAD_TEXT,
        MIXED_TEXT,
        GOOD_TEXT,
        BAD_TEXT,
        MIXED_TEXT,
        GOOD_TEXT,
        BAD_TEXT,
        MIXED_TEXT,
        GOOD_TEXT,
        BAD_TEXT,
        MIXED_TEXT,
    ] * 2
    zero_shot_analyzer = ZeroShotClassificationAnalyzer(
        model_name_or_path="typeform/mobilebert-uncased-mnli",
    )
    batch_size = 4
    labels = ["facility", "food", "comfortable", "positive", "negative"]

    source_responses = [
        AnalyzerRequest(processed_text=text, source_name="sample") for text in TEXTS
    ]
    analyzer_responses = zero_shot_analyzer.analyze_input(
        source_response_list=source_responses,
        analyzer_config=ClassificationAnalyzerConfig(
            labels=labels, batch_size=batch_size
        ),
    )
    stop = timeit.default_timer()

    print("Time: ", stop - start)
    print("Batch size: ", batch_size)
    print("Total Texts: ", len(TEXTS))
    """for res in analyzer_responses:
        print(res.segmented_data)
    """
    assert len(analyzer_responses) == len(TEXTS)

    for analyzer_response in analyzer_responses:
        assert len(analyzer_response.segmented_data) == len(labels)
        assert "positive" in analyzer_response.segmented_data
        assert "negative" in analyzer_response.segmented_data

enhancement

opened by shahrukhx01 16

add preprocessor/cleaner

Could you review the structure of the code, is it as per your expectation and style guide we have for this repo? I'll then start adding functions to this and keep updating this PR. thanks

issues: #75
enhancement

opened by shahrukhx01 12
google Colab getting "no module named 'dateparser'"

Describe the bug A clear and concise description of what the bug is.

installing !pip install obsei

and run twitter scrape

`/usr/local/lib/python3.7/dist-packages/obsei/misc/utils.py in () 3 import time 4 ----> 5 import dateparser 6 from datetime import datetime, timezone 7 from importlib import import_module

ModuleNotFoundError: No module named 'dateparser'`

from obsei.source.twitter_source import TwitterCredentials, TwitterSource, TwitterSourceConfig
bug

opened by dominicdev 11
ner analyzer changes , tokenizer initialization updated .
Apart from the tokenizer fix , I also observed one more thing, in the documentation of configure Ner Analyzer it is mentioned that "# NER analyzer does not need configuration settings" . But while predicting the NER tags using NERAnalyzer's analyze_input method, analyzer_config is required (below). Hence the NER tagging fails.

if analyzer_config is None: raise ValueError("analyzer_config can't be None")

So two things can be done, either we mention config setting for analyzer or fix the batch size 1 in code, I have done a quick fix here but I need your comment so I can make a cleaner fix. Either make the ner's analyze_input batch free or define a config and a batch size for ners .
enhancement
opened by akar5h 9
Topic Analyzer / Topic Modeling

Please suggeset the changes and refactoring Rn the two things I am undecisive of are the parameters or configurations to Umap , HDBSCAN .
Also I have Keras as an autoencoder, undecisive if I should make it to Pytorch completely. You will notice I have not included batchify , since we want all the text to process in one go .

opened by akar5h 7
Add TextSplitter as Preprocessor node

When this project was started initial intention was to handle only short text. But now we have added Google News and Crawlers, hence there is need to handle longer text as well. As we know that most of BERT based model support 512 max tokens (with few exceptions like BigBird). Currently Analyzer ignore (https://github.com/lalitpagaria/obsei/issues/113) excessive text.

Now Idea to introduce TextSplitter to split longer text and feed it to Analyzer. But it introduce another complexity with Analyzer predictions? How to combine inferences by multiple chunks for final prediction. Right now there not proper solution exist to handle this scenarios except try few like voting, averaging or like the one suggested here https://discuss.huggingface.co/t/new-pipeline-for-zero-shot-text-classification/681/84.

For sake of simplicity let's first implement TextSplitter. For this purpose let's take inspiration from Haystack splitter along with adding context like chunk_id, passage_id, etc into meta data.

For inference aggregation later we can add another node for Inference aggregation, we may call it InferenceAggregator. This will aggregate Analyzer result on text chunks to compute final inference.

enhancement

opened by lalitpagaria 7
Batch call to pipeline in Analyzers

Is your feature request related to a problem? Please describe. Currently analyzers are iterating over array and calling pipeline method with single argument. This can be improve upon by calling pipeline with array of data.

Describe the solution you'd like Divide input array into multiple batches and pass batch array to pipeline. Also, do performance analysis if this improves library latency.
low priority analyzer

opened by GirishPatel 6
[Analyzer] Integrate intent classifier

An intent classifier is useful to detect what customer goals like buy, sell, and purchase also useful in conversional flow.

This same things can also be done via zero shot classifier as well but it would better to add separate analyzer to separate it from generic text classification. It will help user to load their own models for this purpose.
low priority analyzer

opened by lalitpagaria 6
Add DAG support and fix inconsistent naming
[ ] Introduce DAG based workflow. Need to finalise between networkx or airflow

[ ] Replace use of Sink with Informer (packages, classes and variables)

[ ] Replace use of Source with Observer (packages, classes and variables)

[ ] Replace use of Analyzer with Segmenter (packages, classes and variables)

[ ] Add BaseSegmenter class

[ ] Fix state store

[ ] Fix circular dependency in Workflow classes

breaking changes low priority engineering excellence
opened by lalitpagaria 6

[BUG]TwitterSourceConfig - AttributeError: At least one non empty parameter required (query, keywords, hashtags, and usernames)

source_config = TwitterSourceConfig( usernames=["@Zappos"], lookup_period="24h", tweet_fields=[.....

error:

AttributeError: At least one non empty parameter required (query, keywords, hashtags, and usernames)

The bug is in the TwitterSource class

def lookup(self, config: TwitterSourceConfig, **kwargs) -> List[TextPayload]:  # type: ignore[override]
    if (
        not config.query
        and not config.keywords
        and not config.hashtags
        and config.usernames
    ):

it should be

def lookup(self, config: TwitterSourceConfig, **kwargs) -> List[TextPayload]:  # type: ignore[override]
    if (
        not config.query
        and not config.keywords
        and not config.hashtags
        and not config.usernames
    ):

bug

opened by chxlium 5

[BUG] TypeError: '<' not supported between instances of 'datetime.datetime' and 'NoneType'
Describe the bug

I'm not sure if this actually is a bug, but today I realized while using lookup_period I was getting this error

TypeError: '<' not supported between instances of 'datetime.datetime' and 'NoneType'

To Reproduce Steps to reproduce the behavior:https://github.com/amrrs/youtube-comment-sentiment-analysis/blob/main/Download_YouTube_Comments_NLP_Sentiment_Analysis.ipynb

Expected behavior If it's a bug, suggest me how to fix it.

Or if it's actually that the comments aren't there for those time period then we can show that kind of message. But in my case, there were comments during those period and yet I got that above error.

Please complete the following information:

OS: Google Colab (Linux)

Version:

Additional context Happy to give more details!
bug
opened by amrrs 5
Bump actions/cache from 3.0.11 to 3.2.2
Bumps actions/cache from 3.0.11 to 3.2.2.

Release notes

Sourced from actions/cache's releases.

v3.2.2

What's Changed

Fix formatting error in restore/README.md by @me-and in actions/cache#1044

save/README.md: Fix typo in example by @mmuetzel in actions/cache#1040

README.md: remove outdated Windows cache tip link by @me-and in actions/cache#1042

Revert compression changes related to windows but keep version logging by @Phantsure in actions/cache#1049

New Contributors

@me-and made their first contribution in actions/cache#1044

@mmuetzel made their first contribution in actions/cache#1040

Full Changelog: https://github.com/actions/cache/compare/v3.2.1...v3.2.2

v3.2.1

What's Changed

Release compression related changes for windows by @Phantsure in actions/cache#1039

Upgrade codeql to v2 by @Phantsure in actions/cache#1023

Full Changelog: https://github.com/actions/cache/compare/v3.2.0...v3.2.1

v3.2.0

What's Changed

fix wrong timeout env var key in README.md by @walterddr in actions/cache#959

Updated release doc with correct env variable by @kotewar in actions/cache#960

Create pull_request_template.md by @pdotl in actions/cache#963

Update README with clearer info about cache-hit and its value by @kotewar in actions/cache#961

Change datadog/squid to Ubuntu/squid in CI check by @bishal-pdMSFT in actions/cache#976

Add more details to version section in readme by @bishal-pdMSFT in actions/cache#971

Update hashFiles documentation reference by @asaf400 in actions/cache#979

Updated link for cache segment download info by @kotewar in actions/cache#986

Readme update for deleting caches by @t-dedah in actions/cache#981

Add oncall logic to assign issues and PRs by @vsvipul in actions/cache#997

Bump minimatch from 3.0.4 to 3.1.2 by @dependabot in actions/cache#998

Revert "Bump minimatch from 3.0.4 to 3.1.2" by @vsvipul in actions/cache#1005

Fix npm vulnerability by @Phantsure in actions/cache#1007

refactor: Use early return pattern to avoid nested conditions by @jongwooo in actions/cache#1013

Use cache in check-dist.yml by @jongwooo in actions/cache#1004

chore: Use built-in cache action to cache dependencies by @jongwooo in actions/cache#1014

Updated node example by @t-dedah in actions/cache#1008

Fix: Node npm doc example by @apascualm in actions/cache#1026

docs: fix an invalid link in workarounds.md by @teatimeguest in actions/cache#929

General Availability release for granular cache by @kotewar in actions/cache#1035 More details here on beta release.

New Contributors

@walterddr made their first contribution in actions/cache#959

@asaf400 made their first contribution in actions/cache#979

@jongwooo made their first contribution in actions/cache#1013

@apascualm made their first contribution in actions/cache#1026

@teatimeguest made their first contribution in actions/cache#929

... (truncated)

Changelog

Sourced from actions/cache's changelog.

3.0.11

Update toolkit version to 3.0.5 to include @actions/core@^1.10.0

Update @actions/cache to use updated saveState and setOutput functions from @actions/core@^1.10.0

3.1.0-beta.1

Update @actions/cache on windows to use gnu tar and zstd by default and fallback to bsdtar and zstd if gnu tar is not available. (issue)

3.1.0-beta.2

Added support for fallback to gzip to restore old caches on windows.

3.1.0-beta.3

Bug fixes for bsdtar fallback if gnutar not available and gzip fallback if cache saved using old cache action on windows.

3.2.0-beta.1

Added two new actions - restore and save for granular control on cache.

3.2.0

Released the two new actions - restore and save for granular control on cache

3.2.1

Update @actions/cache on windows to use gnu tar and zstd by default and fallback to bsdtar and zstd if gnu tar is not available. (issue)

Added support for fallback to gzip to restore old caches on windows.

Added logs for cache version in case of a cache miss.

3.2.2

Reverted the changes made in 3.2.1 to use gnu tar and zstd by default on windows.

Commits

4723a57 Revert compression changes related to windows but keep version logging (#1049)

d1507cc Merge pull request #1042 from me-and/correct-readme-re-windows

3337563 Merge branch 'main' into correct-readme-re-windows

60c7666 save/README.md: Fix typo in example (#1040)

b053f2b Fix formatting error in restore/README.md (#1044)

501277c README.md: remove outdated Windows cache tip link

c1a5de8 Upgrade codeql to v2 (#1023)

9b0be58 Release compression related changes for windows (#1039)

c17f4bf GA for granular cache (#1035)

ac25611 docs: fix an invalid link in workarounds.md (#929)

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dependencies
opened by dependabot[bot] 1
Bump actions/checkout from 3.1.0 to 3.2.0
Bumps actions/checkout from 3.1.0 to 3.2.0.

Release notes

Sourced from actions/checkout's releases.

v3.2.0

What's Changed

Add GitHub Action to perform release by @rentziass in actions/checkout#942

Fix status badge by @ScottBrenner in actions/checkout#967

Replace datadog/squid with ubuntu/squid Docker image by @cory-miller in actions/checkout#1002

Wrap pipeline commands for submoduleForeach in quotes by @jokreliable in actions/checkout#964

Update @actions/io to 1.1.2 by @cory-miller in actions/checkout#1029

Upgrading version to 3.2.0 by @vmjoseph in actions/checkout#1039

New Contributors

@ScottBrenner made their first contribution in actions/checkout#967

@cory-miller made their first contribution in actions/checkout#1002

@jokreliable made their first contribution in actions/checkout#964

@vmjoseph made their first contribution in actions/checkout#1039

Full Changelog: https://github.com/actions/checkout/compare/v3...v3.2.0

Changelog

Sourced from actions/checkout's changelog.

Changelog

Commits

755da8c 3.2.0 (#1039)

26d48e8 Update @actions/io to 1.1.2 (#1029)

bf08527 wrap pipeline commands for submoduleForeach in quotes (#964)

5c3ccc2 Replace datadog/squid with ubuntu/squid Docker image (#1002)

1f9a0c2 README - fix status badge (#967)

8230315 Add workflow to update a main version (#942)

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dependencies
opened by dependabot[bot] 1
Tiyaro API integration for analyzer

Is your feature request related to a problem? Please describe. A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]

Describe the solution you'd like A clear and concise description of what you want to happen.

Describe alternatives you've considered A clear and concise description of any alternative solutions or features you've considered.

Additional context Add any other context or screenshots about the feature request here.
enhancement analyzer

opened by GirishPatel 0
Integrate Freshdesk, Salesforce and SAP

It is ambitious tasks but adding relevant link to it so we can reference them later.

Freshdesk

Ticket: https://developers.freshdesk.com/api/#create_ticket Discussion: https://developers.freshdesk.com/api/#discussions Satisfaction Rating: https://developers.freshdesk.com/api/#satisfaction-ratings MailBox: https://developers.freshdesk.com/api/#email-mailboxes Contact Search: https://developers.freshdesk.com/api/#filter_contacts Conversation: https://developers.freshchat.com/api/#conversation

Salesforce

Conversation: https://developer.salesforce.com/docs/atlas.en-us.chatterapi.meta/chatterapi/connect_resources_conversation_entries.htm Feed: https://developer.salesforce.com/docs/atlas.en-us.chatterapi.meta/chatterapi/connect_resources_feeds.htm Social Engagement: https://developer.salesforce.com/docs/atlas.en-us.chatterapi.meta/chatterapi/connect_resources_social_engagement_list.htm Push Notification: https://developer.salesforce.com/docs/atlas.en-us.chatterapi.meta/chatterapi/connect_resources_notifcations_push.htm

SAP

TBD
enhancement

opened by lalitpagaria 0
Explore NetworkX to create complex workflows
Currently we can create simple workflows consist of three node observer, analyzer and informer each. But as use cases demand for Obsei is growing better to allow user to create more complex workflows to enable user to use any nodes in any fashion with few constraints. There will be following type of nodes in DAG -

Observer: Output only nodes, which will not have standard input capability

Informer: Input only nodes, which will not have standard output capability

Analyzer: AI capability nodes, they can receive standard data as Input and produce standard output data

Misc: Few nodes like delay, joiner/merger, switch-case, splitter, aggregator, etc

This task is precursor to #145
enhancement breaking changes engineering excellence
opened by lalitpagaria 0

Releases(v0.0.14)

v0.0.14(Nov 19, 2022)
:star: Major Highlights

Making dependencies more strict

Updating more information on Readme

Adding code coverage reporting with CI job

Changes

Making dependencies strict @lalitpagaria (#270)

Source code(tar.gz)
Source code(zip)
v0.0.13(Nov 11, 2022)
:star: Major Highlights

Python 3.10 support

Segregated optional dependencies to support install on need basis to reduce the docker image size

Added about oraika our parent organization and user

🚀 Features

Segregated optional dependencies to support install on need basis @GirishPatel (#257)

Add Python 3.10 support and fix website @lalitpagaria (#218)

🐛 Bug Fixes

Fixing sample UI dependencies @lalitpagaria (#262)

Fix UI docker build @lalitpagaria (#255)

🧰 Maintenance

Updating Readme and fixing pypi release @lalitpagaria (#267)

Fixing mypy reported issues @lalitpagaria (#268)

Bump actions/checkout from 2 to 3.1.0 @dependabot (#263)

Bump actions/cache from 2 to 3.0.11 @dependabot (#265)

Bump docker/metadata-action from 3.6.2 to 4.1.1 @dependabot (#266)

Adding numpy to conda build @lalitpagaria (#256)

⚠️Breaking Changes

To add ElasticSearch 8.x support few input params are modified (#268)

By default it will only install bare minimal dependencies, in order to install all use pip install obsei[all] (#257)

Source code(tar.gz)
Source code(zip)
v0.0.12(Jul 23, 2022)
🐛 Bug Fixes

Fix outscrapper map review API along with moving to faster V3 API @lalitpagaria (#250)

Fix TwitterSource username bug, add Gnews proxy @chxlium (#246)

🧰 Maintenance

Upgrade dependencies and add dateparser in dependency list @lalitpagaria (#252)

Bump actions/setup-python from 2 to 4 @dependabot (#248)

Bump crazy-max/ghaction-docker-meta from 1 to 3.6.2 @dependabot (#226)

Upgraded click version to fix typer dependency @GirishPatel (#245)

🚀 Misc

Add Utilize.app to the list of companies using Obsei @arorajatin (#249)

Thanks to new contributors @chxlium and @arorajatin
Source code(tar.gz)
Source code(zip)
v0.0.11(Feb 9, 2022)
:star: Major Highlights

Youtube: Now fetch Youtube video comments (via Scrapper)

License: Removed all strong copyleft dependencies

Demo: Improved demo UI along with adding more detailed logging

Few bug fixes, dependencies upgrade, CI enhancements and fixing security issue

🚀 Features

Youtube integration via scrapper @lalitpagaria (#224)

Removing third party dependencies with strong copyleft licenses @lalitpagaria (#221)

Enhancing demo UI @lalitpagaria (#214)

🐛 Bug Fixes

Fixing typing-extensions dependency issue on python 3.7 @lalitpagaria (#217)

Google News max result fix @tanish36 (#211)

Bug: Updating long_term_token param to access_token for facebook source. @lalitpagaria (#210)

🧰 Maintenance

Updated the README @kuutsav (#222)

Fix security issue with lxml @lalitpagaria (#219)

Dep upgrade (to address Dependabot for NLTK as well) @lalitpagaria (#215)

Enabling CI caching @lalitpagaria (#213)

Source code(tar.gz)
Source code(zip)
v0.0.10(Oct 5, 2021)
:star: Major Highlights

Google Maps: Now observer able to fetch google maps reviews

Handle Long Text: Now use TextSplitter and InferenceAggregator to seamlessly process very long text for example news article

Analyzer: New TextClassification analyzer let you use non zero-shot classification model

Analyzer: New Spacy powered NER analyzer let you use spacy based NER models

Pandas: Now you can use Pandas DataFrame as an observer and informer, which enable you to load and store data from CSV, TSV, Excel and SQL DBs.

Miscellaneous: Jinja template support for Slack message, new tutorials and pre-commit hook to save dev time

🚀 Features

Jinja Template for Slack, RegEx & Lammatizer cleaner functions and Sentence based text splitting @lalitpagaria (#206)

Add SDK and UI-Demo image release job @lalitpagaria (#205)

Adding jinja template support for slack messages @lalitpagaria (#199)

Adding google maps review observer via outscrapper api @lalitpagaria (#195)

add text classification analyzer @shahrukhx01 (#191)

Add Pandas as Observer/Source @cnarte (#184)

Adding app_url support to appstore and playstore scrappers @lalitpagaria (#180)

Adding InferenceAggregator @akar5h (#166)

spacy ner analyzer , #165 enhancement @akar5h (#171)

Adding tutorials information and articles @lalitpagaria (#168)

Colab Tutorials Added @reenabapna (#167)

TextSplitter Preprocessor Pipeline @akar5h (#160)

Pre commit integration @salilmishra23 (#156)

add requirement files for development @salilmishra23 (#159)

Facebook source time range improvement @GirishPatel (#157)

🐛 Bug Fixes

Reverting to older messaging format for Twitter v2 API @lalitpagaria (#198)

Fixing import issues along @lalitpagaria (#193)

Fix Twitter Source Config import issue @lalitpagaria (#190)

Use of BaseSettings Causing regression so reverting the changes @lalitpagaria (#189)

Creating quoted query before passing to GNews client @lalitpagaria (#181)

Handle Null case when crawler failed to fetch article @lalitpagaria (#173)

Email source fixed, no duplicates in each iteration @namanjuneja771 (#158)

🧰 Maintenance

Adding support to dailyget message api @lalitpagaria (#179)

⚠️Breaking Changes

Fix Twitter Source Config import issue @lalitpagaria (#190)

Adding InferenceAggregator @akar5h (#166)

spacy ner analyzer , #165 enhancement @akar5h (#171)

🙏 Release Contributors! :heart:

@akar5h @cnarte @GirishPatel @lalitpagaria @namanjuneja771 @reenabapna @salilmishra23 @shahrukhx01

🥳 New Contributors

@namanjuneja771 made their first contribution in https://github.com/obsei/obsei/pull/158

@salilmishra23 made their first contribution in https://github.com/obsei/obsei/pull/159

@reenabapna made their first contribution in https://github.com/obsei/obsei/pull/167

@cnarte made their first contribution in https://github.com/obsei/obsei/pull/184

Full Changelog: https://github.com/obsei/obsei/compare/0.0.9...v0.0.10
Source code(tar.gz)
Source code(zip)
0.0.9(Jul 3, 2021)
:star: Major Highlights

Pre-Processing (https://github.com/obsei/obsei/pull/110)

This added new component to Obsei. It's main idea to provide simple but configurable step to pre-process text before sending it for model prediction. Currently TextCleaning step is added, which helps user to clean raw text's. It is great contribution by @shahrukhx01

Facebook Integration (https://github.com/obsei/obsei/pull/150)

Obsei can now observe comments from Facebook page's posts thanks to contribution by @GirishPatel

Google News Integration (https://github.com/obsei/obsei/pull/111)

Obsei can now search news on GoogleNews and scrap full news article in text

Website Scrapper Integration (https://github.com/obsei/obsei/pull/111)

Obsei can now scrap particular URL or full website if it contains sitemap

Pandas DataFrame Integration (https://github.com/obsei/obsei/pull/107)

Obsei can now have Pandas DataFrame as Informer to publish Analyzer's data to DataFrame.

🔆 Other Changes

Added contribution guideline and code of conduct (https://github.com/obsei/obsei/commit/e102f8915d99e7241974e1b2360b3d075300338a and https://github.com/obsei/obsei/commit/fcc9a9121efc0b049024b100ce28d8bab521f072)

Adding version tag along with default logging config (https://github.com/obsei/obsei/pull/149)

Ignoring error during cleaning and fixing exception in google news module (https://github.com/obsei/obsei/pull/143)

Add analyzer batching (https://github.com/obsei/obsei/pull/118)

Adding app name support for app and play store (https://github.com/obsei/obsei/pull/128)

Mypy integration (https://github.com/obsei/obsei/pull/135 and https://github.com/obsei/obsei/pull/125

Fixing error regarding offset-naive and offset-aware datetimes comparison (https://github.com/obsei/obsei/pull/119)

Remove import from configuration.py as it is causing loop of import (https://github.com/obsei/obsei/pull/133)

Trimming excessive text before passing to model (https://github.com/obsei/obsei/pull/114)

Ignoring error during cleaning and fixing exception in google news module (https://github.com/obsei/obsei/pull/143)

Adding version tag along with default logging config (#149)

[BUG] Tokenizer loading error the NER Analyzer (https://github.com/obsei/obsei/pull/152)

⚠️ Breaking Changes

Unifying analyzer request and response (https://github.com/obsei/obsei/pull/148)

🙏 Release Contributors! :heart:

@GirishPatel @shahrukhx01 @akar5h @lalitpagaria
Source code(tar.gz)
Source code(zip)
0.0.8(May 21, 2021)
This release includes (Refer for detailed changes https://github.com/lalitpagaria/obsei/projects/6) -

New Analyzers: Personal Information Anonymizer and Translation

GPU support: Analyzer's now can utilize GPU

Conda release: Adding initial support to install package from Conda as well as creation of development environment

Window support: Adding support for windows platform

Obsei UI demo: Adding streamlit based UI to try Obsei

Various bug fixes

Source code(tar.gz)
Source code(zip)
0.0.7(Mar 27, 2021)
This release include -

Email Observer: Currently do not segment attachment (linked issue https://github.com/lalitpagaria/obsei/issues/30)

Zendesk Informer (linked issue https://github.com/lalitpagaria/obsei/issues/31)

Add extensive example, colab and binder support in Readme (linked issue https://github.com/lalitpagaria/obsei/issues/39)

Remove hydra dependency (linked issue https://github.com/lalitpagaria/obsei/issues/33)

Adding Logger Informer, so user can easily test out end-to-end pipeline (linked commit https://github.com/lalitpagaria/obsei/commit/ca99da8b72611788e5969565438f02d7d356cfd1)

Adding security policy to repo (linked commit https://github.com/lalitpagaria/obsei/commit/2685fffdd36b57acdeb6a8adfc6f9920dfa7082b)

[Bug/Regression]: Rest interface via docker build was failing (linked commit https://github.com/lalitpagaria/obsei/commit/ef48b36792e1ce0d7118df6f3081f1e3b368c65c)

Source code(tar.gz)
Source code(zip)
0.0.6(Feb 5, 2021)
This release include following bug fixes -

Play store scrapper is failing (linked issue https://github.com/lalitpagaria/obsei/issues/34)

Correct analyzer usages in examples (linked issue https://github.com/lalitpagaria/obsei/issues/36)

Source code(tar.gz)
Source code(zip)
0.0.5(Feb 3, 2021)
+! Release Detail !+

Reddit and Slack integration (https://github.com/lalitpagaria/obsei/commit/b69b64c439fda4d92fee5be18904a3f347d2ea12 and https://github.com/lalitpagaria/obsei/commit/7a0f6ab846a30c7fda606d4bee42bedc81eaa6c7)

New Analyzers NER and Dummy apart from Sentiment, Classification (https://github.com/lalitpagaria/obsei/commit/e1115595372e83708e081a3c05138abb9116b9e9 and https://github.com/lalitpagaria/obsei/commit/870c1eaea3494e00dcda4b9c94aa0203b85082d4)

Image and Readme improvements

Documentation website: https://lalitpagaria.github.io/obsei/

Discussion forum: https://github.com/lalitpagaria/obsei/discussions

-! Breaking Changes !-

This release have breaking changes related to change in package and arguments for Analyzers. Refer https://github.com/lalitpagaria/obsei/commit/e1115595372e83708e081a3c05138abb9116b9e9
Source code(tar.gz)
Source code(zip)
0.0.4(Jan 13, 2021)
This release include -

Google play store reviews Observer, this need authentication (linked issue https://github.com/lalitpagaria/obsei/issues/4)

Apple app store reviews Observer via scrapping, this do not need authentication (linked issue https://github.com/lalitpagaria/obsei/issues/9)

Google play store reviews Observer via scrapping, this do not need authentication (linked issue https://github.com/lalitpagaria/obsei/issues/10)

Persist Observer current state to persistent storage of user choice like SQLite, MySQL, Postgres etc (linked issue https://github.com/lalitpagaria/obsei/issues/6)

Source code(tar.gz)
Source code(zip)
0.0.3(Dec 29, 2020)
This release includes -

Google play store reviews as source

Jira client fix

Sending markdown in Jira description

Few updated to readme

Source code(tar.gz)
Source code(zip)
v0.0.2(Dec 23, 2020)
First release with support for following components -

Source: Twitter

Analyzer: Sentiment and Text classification

Sink: HTTP API, ElasticSearch, DailyGet, and Jira

Processor: Simple integration between Source, Analyser and Sink

API Server: Rest interface

Source code(tar.gz)
Source code(zip)