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uProc + Monkey Learn Integrations

Appy Pie Connect allows you to automate multiple workflows between uProc and Monkey Learn

  • No code
  • No Credit Card
  • Lightning Fast Setup
About uProc

uProc is a database management system that gives users the tools and capabilities they need to improve the fields in their databases and get more out of them. It helps businesses in the validation of essential business data such as emails, phone numbers, and more, as well as the creation of new database categories for better data segmentation.

About Monkey Learn

MonkeyLearn is a text analysis platform that helps you identify and extract actionable data from a variety of raw texts, including emails, chats, webpages, papers, tweets, and more! You can use custom tags to categorize texts, such as sentiments or topics, and extract specific data, such as organizations or keywords.

Monkey Learn Integrations

Best ways to Integrate uProc + Monkey Learn

  • uProc Pipedrive

    uProc + Pipedrive

    Add persons in Pipedrive from new uProc people list entries Read More...
    Close
    When this happens...
    uProc New Profile Added to List
     
    Then do this...
    Pipedrive Create Person
    Don't waste time entering data manually. Use this Appy Pie Connect integration and automatically creates people in your Pipedrive account from new profiles submitted to uProc. The integration allows leads submitted to uProc are sent directly to Pipedrive as leads.
    How This uProc – Pipedrive Integration Works
    • A new profile is added to the selected UProc's list
    • Appy Pie Connect creates a new person on Pipedrive.
    What You Need
    • uProc account
    • Pipedrive account
  • uProc uProc

    Gmail + uProc

    Select Tool in uProc when New Attachment is created in Gmail Read More...
    Close
    When this happens...
    uProc New Attachment
     
    Then do this...
    uProc Select Tool
  • uProc uProc

    Gmail + uProc

    Select Tool in uProc when New Labeled Email is created in Gmail Read More...
    Close
    When this happens...
    uProc New Labeled Email
     
    Then do this...
    uProc Select Tool
  • uProc uProc

    Gmail + uProc

    Select Tool in uProc when New Email Matching Search is created in Gmail Read More...
    Close
    When this happens...
    uProc New Email Matching Search
     
    Then do this...
    uProc Select Tool
  • uProc uProc

    Gmail + uProc

    Select Tool in uProc when New Starred Email is created in Gmail Read More...
    Close
    When this happens...
    uProc New Starred Email
     
    Then do this...
    uProc Select Tool
  • uProc {{item.actionAppName}}

    uProc + {{item.actionAppName}}

    {{item.message}} Read More...
    Close
    When this happens...
    {{item.triggerAppName}} {{item.triggerTitle}}
     
    Then do this...
    {{item.actionAppName}} {{item.actionTitle}}
Connect uProc + Monkey Learn in easier way

It's easy to connect uProc + Monkey Learn without coding knowledge. Start creating your own business flow.

    Triggers
    Actions
  • Select Tool

    Select a tool to perform verification or enrichment

  • Classify Text

    Classifies texts with a given classifier.

  • Extract Text

    Extracts information from texts with a given extractor.

  • Upload training Data

    Uploads data to a classifier.

How uProc & Monkey Learn Integrations Work

  1. Step 1: Choose uProc as a trigger app and authenticate it on Appy Pie Connect.

    (30 seconds)

  2. Step 2: Select "Trigger" from the Triggers List.

    (10 seconds)

  3. Step 3: Pick Monkey Learn as an action app and authenticate.

    (30 seconds)

  4. Step 4: Select a resulting action from the Action List.

    (10 seconds)

  5. Step 5: Select the data you want to send from uProc to Monkey Learn.

    (2 minutes)

  6. Your Connect is ready! It's time to start enjoying the benefits of workflow automation.

Integration of uProc and Monkey Learn

[Text from Introduction to uProc API from the API documentation]

uProcess is a Python API for extracting data from websites, building models and making predictions in a few lines of code. It is a powerful top created by a team of Data Scientists at Uber and used in production systems in multiple teams. uProcess is written in Python and allows you to extract structured data from websites and make predictions based on machine learning models. With this API, you can build your pipeline of web-scraping and model-training tasks in minutes, instead of hours or days. uProcess is built with extensibility and flexibility in mind, so that it can be used in many different scenarios. It makes heavy use of common libraries such as requests, BeautifulSoup and NLTK. uProcess is hosted on GitHub. https://github.com/uber/uproc We encourage you to contribute and improve the code base and the documentation.

  • Integration of uProc and Monkey Learn
  • [Text from uProc API Documentation]

    The fplowing example shows how you can extract data from an article using uProcess and train a classifier with MonkeyLearn. The classifier will predict whether an article is positive or negative. After training the classifier, we extract the text from the headline and extract some metadata (such as number of words. from the article. We then use some of these values to predict the sentiment of the headline. import json from uprocess import extract_from_url , extract_text_from_html , extract_metadata from monkeylearn import MonkeyLearnRegistry client = MonkeyLearnRegistry ( api_key = 'YOUR API KEY' . url = 'http://www.wired.com/2017/03/the-future-of-work/' client . set_model ( 'positive_negative' . response = extract_from_url ( url , client , extra_headers = { 'Authorization' . "Basic " + b64encode ( client . get_api_key ())}. article = response [ 'content' ] metadata = extract_metadata ( article . # Extract the text from the headline head_text = extract_text_from_html ( article [ 'headlines' ][ 0 ][ 'innerHTML' ]. # Training the classifier model = client . create_classifier ( 'positive_negative' . model . set_item ([ 'is_heading' ], 'true' . model . set_item ([ 'headlines' ], head_text . model . set_item ([ '0' ], metadata [ 'numberOfWords' ]. model . set_item ([ '1' ], metadata [ 'wordCount' ]. # Predicting the sentiment prediction = model . predict (. # Create a dictionary hpding all items to be returned result = {} result [ 'title' ] = article [ 'title' ] result [ 'sentiment' ] = prediction [ 0 ] print ( "Predicted sentiment:" + str ( prediction [ 0 ]. return json . dumps ( result )

  • Benefits of Integration of uProc and Monkey Learn
  • [Text from uProc API Documentation]

    For example, let's say you want to extract data about products on Amazon, to find products that are similar to an object you found in another website. You can do this with one line of code. products = extract_products ( productUrl . # products is a list of dictionaries with information about each product. The item dictionary contains all the information you need about each product, including title, description, price, availability etc... Now say you want to classify the products into groups, for example 5-star products and 1-star products. You can do this using uProcess like this. stars = extract_stars ( productUrl , 1 . # stars is a list of 1 if the product is a 5-star product, 0 otherwise Then you can cross reference your list of results with your list of similar products to find out which 1-star products are similar to your 5-star product. similarProducts = [] for item in products . if item [ 'stars' ] == 1 . similarProducts . append (( item [ "title" ], item [ "description" ]. # Get the list of similar items print ( sorted ( similarProducts , key = lambda x . x [ 1 ]))

    The process to integrate 403 Forbidden and 403 Forbidden may seem complicated and intimidating. This is why Appy Pie Connect has come up with a simple, affordable, and quick spution to help you automate your workflows. Click on the button below to begin.