Question 1

Create and deploy an AI chatbot [No-code]

Accepted Answer

Create your custom ChatGPT to answer questions about your documents

From the dashboard view, let's click on "Create a new experiment".
Select Text based.
Click on Ask questions to your documents

Create Chatbot

Choose a name and click "Next"
Upload your documents. We currently support PDF, Word or txt files. you can drag and drop directly from your computer or a zip file containing all your documents. In this tutorial we will use a document which is the transcription of the YC Startup School video: Should You Start A Startup?

|| To transcribe the video we used the Cogniflow transcription model. To learn more read our tutorial

You can download the file directly from here.

Upload your documents or Knowledge base

Click Next Step and Run and Create Experiment.

|| Note: It takes around 3 mins to set up everything and be ready to use it. If you want to be notified when the model is ready remember to mark the check.

Try your model

Once the model is ready,

Question 2

Create your Info Smart Extractor using AI

Accepted Answer

This tutorial explains how to create a Smart Extractor in Cogniflow to extract any specific text from images, text, or PDF documents.

From the dashboard view, click “New Project”
In this tutorial, select "Image and PDF" to extract from images of PDF documents. Please use Plain Text if you are planning to integrate Cogniflow with services that typically use text as input such as Gmail, X, WhatsApp, etc.

Select Smart Extractor project

In this step, you have to specify the entities that you want to use as extraction criteria. For this example, let’s say we want to extract information from a receipt that you have taken a photo with your cell phone. Click on "Add manually"

Define the entities you want to extract

Then, in the modal that pops up, you have to specify each field.

Define entity

Name: This is the entity or field name you want to extract, and it will be used in the output as the key identifier.
Description: This is optional, but it could be very helpful to give extra instructions to be more effective in identifying an entity that is not a common type like a date, number, or currency.
Output format: You can use this to convert an e

Question 3

Deploy and share your AI model as a Web App in one click

Accepted Answer

Share any AI model as a Web App or embed it into your website

Q: How to Create a Dataset for Object Detection using the YOLO Labeling Format

The YOLO (You Only Look Once) format is a specific format for annotating object bounding boxes in images for object detection tasks. In this format, each image in the dataset should have a corresponding text file with the same name as the image, containing the bounding box annotations for that image. The text file should have the following format: <img src="https://storage.crisp.chat/users/helpdesk/website/447a0bfe5ea4e800/image_x3zdo8.png" alt="YOLO text file format" /> Where: <ul> <li><object-class> is an integer representing the class of the object. The class index should start from 0 and increase by 1 for each unique class in the dataset.</li> <li>**<x-center> **and **<y-center> **are the coordinates of the center of the bounding box, normalized by the width and height of the image, respectively. The values should be in the range of [0, 1].</li> <li><width> and <height> are the width and height of the bounding box, normalized by the width and height of the image, respectively. The values should be in the range of [0, 1].</li> </ul> Here's an example of a YOLO format annotation file for an image containing two objects, a car and a pedestrian: <img src="https://storage.crisp.chat/users/helpdesk/website/447a0bfe5ea4e800/image_lh3pyz.png" alt="Example of YOLO format annotation" /> This means that there is a car (class 0) with its center at (0.45 x image width, 0.6 x image height), and a width and height of 0.1 x image width and 0.2 x image height, respectively. There is also a pedestrian (class 1) with its

|| This feature is available from the Starter Plan. If you need more information about our plans, please check the Cogniflow pricing page.

This feature allows users to create a web application (Web App) for any AI model the user has access. Users can quickly deploy their AI models as a Web App and share them with others without requiring them to have a Cogniflow account. In this article, we will provide a step-by-step guide on how to create and share a Web App.

Let's start by choosing the model you want to share. In this tutorial, we will use our "Face Similarity" model, which allows us to compare two photos and predict if it is the same person.

Go to our Public experiments and choose Face Similarity

Cogniflow Face Similarity public model

Click on Use this model!

Face Similarity: use this model

Click on the Web App tab, and then press the Publish button to create your Web App.

Face Similarity Test model page

Publish Face Similarity model

You can then copy this URL and share it with others, who will be

Question 4

How to use our Make integration

Accepted Answer

Integrate AI with 1000+ apps using Cogniflow and Make

|| Make (previously Integromat) is a no-code workflow automation platform that connects with over 1400 apps to build powerful workflows to automate any work.. Visit Make website and create a free account.

This tutorial will explain how to use our Make app integration to automate powerful workflows in Make using Cogniflow AI models.

Add Cogniflow to Your Workspace

Go to the Make platform and log in to your account.
Click on this link to install our application.
Once you're on the page, click the "Install" button, and then select the organization where you want to install the application. Once the installation is complete, a message will be displayed indicating a successful installation.

Install Make app

Use the search bar to search for the Cogniflow application using its name in your workspace.

Search Cogniflow App

Once you find our application, click on it to access the application details.
Inside the application, you will find our different modules, such as OCR English, OCR Multilingual, Text Classification, Image Classification, Audio Classification, Speech Recognition, Face Similarity, Named Entity Recognition, and

Question 5

Chatbot WhatsApp Integration

Accepted Answer

This guide will help you use your Cogniflow Chatbot in WhatsApp.

1. Create a WhatsApp Meta App

First you need to create a Meta App in Meta for Developers platform. Create or login to your account here

Go to “My Apps” and click on “Create App”

Select Other

Create App Other

Select Business

Business

Name your App, add your email and click on Create app

Meta Create App Details

then scroll down to find WhatsApp and click Set up

2. Generate Token to connect to Cogniflow

To continue you need a business Meta account. If you have already one you will see it there to select, if not go to Meta Business Suite

Under your Meta Business Suite, go to Users → System users

Add an new user for example “admin” with the role “Admin”.

Meta System Users

After creating the user, click on “Assign Assets”, go

Question 6

How to Create a Dataset for Object Detection using the YOLO Labeling Format

Accepted Answer

The YOLO (You Only Look Once) format is a specific format for annotating object bounding boxes in images for object detection tasks. In this format, each image in the dataset should have a corresponding text file with the same name as the image, containing the bounding box annotations for that image. The text file should have the following format:

YOLO text file format

Where:

is an integer representing the class of the object. The class index should start from 0 and increase by 1 for each unique class in the dataset.
** **and ** **are the coordinates of the center of the bounding box, normalized by the width and height of the image, respectively. The values should be in the range of [0, 1].
and are the width and height of the bounding box, normalized by the width and height of the image, respectively. The values should be in the range of [0, 1].

Here's an example of a YOLO format annotation file for an image containing two objects, a car and a pedestrian:

Example of YOLO format annotation

This means that there is a car (class 0) with its center at (0.45 x image width, 0.6 x image height), and a width and height of 0.1 x image width and 0.2 x image height, respectively. There is also a pedestrian (class 1) with its

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