How to create an AI that chats like you | by Simone Guardati | Towards Data Science

The goal of this guide is to build a system capable of chatting like you, using your own WhatsApp and Telegram chats as an ML dataset.
What we will do could be summarized in the following steps:
Get your WhatsApp and Telegram dataParse it to build an ML-ready datasetTrain a GPT-2 modelChat with the model
Get your WhatsApp and Telegram data
Parse it to build an ML-ready dataset
Train a GPT-2 model
Chat with the model
So let’s start our adventure!
Do you want to see some results? Check the official webpage, note that they’re in Italian as they’re based on on my personal chats.
It’s always better not to run random scripts on personal information (like personal chat messages).
I guarantee there’s no catch, but you can always check the native code that’s being used: messaging-chat-parser and pistoBot, all the sources are open-source on github.
First of all, we need to gather the data from our chat applications. We will now learn how to export data from two of the most commonly used instant messaging apps: WhatsApp and Telegram.
We have to export one .txt file for each chat we want to include in the final dataset. So, as described on the official WhatsApp website:
Open your WhatsApp mobile appPick an individual chat one individual chat (e.g. a chat with one friend) > Tap “more options” (three vertical dots)Select the More voice > Export chat voiceSelect Without Media on the pop-upSelect an Email service (e.g. Gmail app) and add your e-mail address as a recipientWait to receive the mail with chat history as txt file attachmentDownload and store the txt attachment on the computerRepeat those steps for every individual chat you want to include
Open your WhatsApp mobile app
Pick an individual chat one individual chat (e.g. a chat with one friend) > Tap “more options” (three vertical dots)
Select the More voice > Export chat voice
Select Without Media on the pop-up
Select an Email service (e.g. Gmail app) and add your e-mail address as a recipient
Wait to receive the mail with chat history as txt file attachment
Download and store the txt attachment on the computer
Repeat those steps for every individual chat you want to include
Note that only 1 to 1 chats are allowed (namely individual), we suggest to export chats with the highest number of messages, in order to achieve a bigger dataset and get better final results.
Now you should have more files, each with a structure that looks like the snippet below:
12/12/19, 02:09 — <FriendName>: bla bla bla bla12/12/19, 08:40 — <YourName>: bla bla bla bla
Take note of the text you find under <YourName> placeholder in your exported chats. This parameter is your name for the WhatsApp app and we will use this value later.
The process here will be faster than WhatsApp because Telegram will export everything in a single .json file, without having the limit of exporting one chat at a time.
So, as described on the official Telegram website:
Open the Telegram Desktop appOpen the menu on the upper left screen (three horizontal lines)Go to settings voice > Click on Advanced >Select Export Telegram dataOnly these fields should be selected: Account information, Contact list, Personal chats, Machine-readable JSONBe sure nothing is selected under Media export settings and set the size limit to the maximumLaunch the export and waitRename the output file as “telegram_dump.json”
Open the Telegram Desktop app
Open the menu on the upper left screen (three horizontal lines)
Go to settings voice > Click on Advanced >Select Export Telegram data
Only these fields should be selected: Account information, Contact list, Personal chats, Machine-readable JSON
Be sure nothing is selected under Media export settings and set the size limit to the maximum
Launch the export and wait
Rename the output file as “telegram_dump.json”
Now you should have one file named telegram_dump.json with this structure:
{ "about": "Here is ...", "personal_information": { "user_id": 123456789, "first_name": "your_name_here", "last_name": "", ....
To train a GPT-2 neural network, first of all we need to pre-process the data, in order to obtain a single .txt with a machine-learning compatible structure.
For the sake of simplicity and since the ML model we will use requires a GPU to work, we are going to use Google Colab for the next step.
If you don’t know what Google Colab is, check this other article:
towardsdatascience.com
Open this Colab notebook and follow these steps:
Run the first block of cells called under the “0️⃣ Init” chapterPress “Run Anyway” on the pop-upMake sure that the first command !nvidia-smi shows that a GPU is connected (p100 is suggested)If no GPU is connected, go to Runtime > Change Runtime type > Hardware accelerator > GPU
Run the first block of cells called under the “0️⃣ Init” chapter
Press “Run Anyway” on the pop-up
Make sure that the first command !nvidia-smi shows that a GPU is connected (p100 is suggested)
If no GPU is connected, go to Runtime > Change Runtime type > Hardware accelerator > GPU
To work with the data, we need to upload them on Colab, into the right folders.
WhatsApp chatsSelect all your .txt files and upload everything into the following notebook folder:./messaging-chat-parser/data/chat_raw/whatsapp/
Telegram JSONGet the file telegram_dump.json and upload it into the following notebook folder:./messaging-chat-parser/data/chat_raw/telegram/
Now, run all the cells up until the block “2️⃣ Parse the data”.
Here we need to replace the variable “whatsapp_user_name” with your WhatsApp name, called <YourName> on the 1.1 chapter.
You can also change the date format parsing system if some of the exported data show a different format due to local time formatting.
So, for example, if my name is “Bob” and I’m from America, the code I should use is the following:
Now execute the cell under the “3️⃣ Train a GTP2 model” notebook chapter, it will run a new training using your provided data.
A progress bar will be shown, and the training could take up to 10 hours, it depends mostly on which GPU type Colab is running and how many much messages were provided.
Wait until the process ends.
After the training is completed, run all the remaining notebook cells: the last one will show a text block with a ✍ symbol on the left.
You could use this text box to insert the messages you want to “send” to the ML model. So write your message and then press enter.
After the first message is sent, the system will prompt some information about the conversation.
You will now see the most interesting results as a list of messages:
Messages starting with the tag [others]:The ones you have sent to the model
Messages starting with the tag [me]:The ones generated by the model.
After the replied message is generated, you could continue to chat for a total of 5 messages. After this, you could re-run the cell to start a new conversation with the model.
So in this guide we have seen how simple it is to train your GPT-2 model from scratch, the task is simple (but not trivial!) only thanks to to aitextgen package that runs under the pistoBot hood.
Note that if your chat messages are in English you could easily obtain better results than the ones we got with this standard approach, since you could use the transfer learning from a GPT-2 pretrained model.
The pistoBot repository allows you to train (or fine-tune) different models, including the chance to start from a GPT-2 pretrained model: check the repository folder for more information.
We have chosen the standard, un-trained GPT-2 model so that even the non-english users could use this AI.
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