Use AI for sentiment analysis to keep track of how your clients are perceiving your website or brand and SNS marketing campaigns without hiring a Data Analysis expert. Save money and make your systems smarter. This product is an API that will analyze the text from a line of text or tweet, searching by hashtag or username within a range of dates retiring a Json response containing the polarity, classification of the sentiment (positive, negative, neural) and the subjectivity. You can repurpose it to analyze other sources of texts by editing the code.
AI Facial Recognition web system Facial recognition and facial detection web system in Python and Django. Upload images and let the AI find matches for people. With the following project you will be able to: Use it as it is for web facial recognition and facial detection Upload single images or multiple images to build a known faces library Find a match by facial recognition for a single image or multiple files Submit an image for facial detection Browse the results as Web client Access to the database and source code Use the recognition class for other projects Includes pre-trained data (pre-trained data from public libraries , you can also use your own training dataset or another public one) External Sources The model was trained using the following dataset: https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/. How to Use: First, copy the two .dat files from the Part2 zip folder inside the Part1 zip folder (where the manage.py file is).
Now you don t need to waste your time removing toxic comments in your comment section. Deploy the code easily at a PHP web server. Includes mock article pages and database structure. Execution Steps: Install a webserver (for example: apache, nginx, IIS etc) Install PHP into the web server and mysqlRun the sql script into your database serverEdit the file DAL.php with your database connection credentials Extract the compressed file and copy the folder into the Document root folder of your webserver.
Word clouds can help to easily and quickly assessed the data from a user, and represent the data in a simple way: easier for the everyday user to understand. Visual representations tend to have a positive impact and create interest amongst your audience. Add visual representation to the data and help to provide an overall sense of the text by using Word Clouds. Details included in the documentation.
Tutorial Resources Python Webservice API Server deployment using Ubuntu From the tutorial: https://medium.com/analytics-vidhya/python-webservice-api-server-deployment-using-ubuntu-201d48f477d9Or: https://www.piecex.com/articles/Python-Webservice-API-Server-deployment-using-Ubuntu-282Here are the files used during the article/tutorial. Docker ExampleIf you downloaded the resources, then you just need to go to the terminal, cd the folder tutorial_resources Build the Dockerfile:docker build tag flask_tutorial:1.0 .Then run the container, for example:docker run --publish 81:80 --detach --name flask_container flask_tutorial:1.0This will publish the service into your localhost port 81 (make sure your port 81 is available or change it in the above docker command).Go to your browser and access http://localhost:81/It should show: PieceX tutorial : HELLO WORLD