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Facebook чат бот python

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Facebook chatbot that I trained to talk like me using Seq2Seq

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adeshpande3/Facebook-Messenger-Bot

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README.md

The FB Messenger chatbot that I trained to talk like me. The associated blog post.

For this project, I wanted to train a Sequence To Sequence model on my past conversation logs from various social media sites. You can read more about the motivation behind this approach, the details of the ML model, and the purpose of each Python script in the blog post, but I want to use this README to explain how you can train your own chatbot to talk like you.

Requirements and Installation

In order to run these scripts, you’ll need the following libraries.

How You Can Train Your Own 

git clone https://github.com/adeshpande3/Facebook-Messenger-Bot.git
    Facebook Data: Download your data from here. Once downloaded, you should have a fairly large file called messages.htm. It’ll be a pretty large file (over 190 MB for me). We’re going to need to parse through this large file, and extract all of the conversations. To do this, we’ll use this tool that Dillon Dixon has kindly open sourced. You’ll go ahead and install that tool by running 
pip install fbchat-archive-parser
fbcap ./messages.htm > fbMessages.txt

This will give you all your Facebook conversations in a fairly unified text file. Thanks Dillon! Go ahead and then store that file in your Facebook-Messenger-Bot folder.

  • LinkedIn Data: Download your data from here. Once downloaded, you should see an inbox.csv file. We won’t need to take any other steps here, we just want to copy it over to our folder.
  • Google Hangouts Data: Download your data form here. Once downloaded, you’ll get a JSON file that we’ll need to parse through. To do this, we’ll use this parser found through this phenomenal blog post. We’ll want to save the data into text files, and then copy the folder over to ours. At the end of all this, you should have a directory structure that looks like this. Make sure you rename the folders and file names if yours are different.
  • Discord Data : You can extract your discord chatlogs by using this awesome DiscordChatExporter made by Tyrrrz. Follow its documentation to extract your desired singular chat logs in .txt format (this is important). You can then put them all in a folder named DiscordChatLogs in the repo directory.
  • WhatsApp Data: Make sure you have a cell phone and put it in the US date-format if it is not already (this will be important later when you parse the log file to .csv). You can not use whatsApp web for this purpose. Open the chat you want to send, tap the menu button, tap more, then click «Email Chat». Send the email to yourself and download it to your computer. This will give you a .txt file, to parse it, we’ll convert it to .csv. To do this go to this link and enter all the text in your log file. Click export, download the csv file and simply store it in your Facebook-Messenger-Bot folder under the name «whatsapp_chats.csv». NOTE: The parser provided in the above link seems to have been removed. If you still have a .csv file in the correct format, you may still use that. Otherwise download your whatsapp chat logs as .txt files and put them all in a folder named WhatsAppChatLogs in the repo directory. createDataset.py will work with these files instead if, and only if, it DOES NOT find a .csv file named whatsapp_chats.csv . In case you use .txt chat logs, note that the expected format is-

    [20.06.19, 15:58:57] Loris: Welcome to the chat example [20.06.19, 15:59:07] John: Thanks 
    12/28/19, 21:43 - Loris: Welcome to the chat example 12/28/19, 21:43 - John: Thanks
    1. Now that we have all our conversation logs in a clean format, we can go ahead and create our dataset. In our directory, let’s run:
    • Build the server, and host on Heroku.
    • Create a Facebook App/Page, set up the webhook, get page token, and trigger the app.
    • Add an API endpoint to index.js so that the bot can respond with messages.

    After following the steps correctly, you should be able to message the chatbot, and get responses back.

    In this app.py file, we want to create a route (/prediction in my case) where the input to the route will be fed into our saved model, and the decoder output is the string that is returned. Go ahead and take a closer look at app.py if that’s still a bit confusing. Now that you have your app.py and your models (and other helper files if you need them), you can deploy your server. We’ll be using Heroku again. There are a lot of different tutorials on deploying Flask servers to Heroku, but I like this one in particular (Don’t need the Foreman and Logging sections).

    1. Once you have your Flask server deployed, you’ll need to edit your index.js file so that the Express app can communicate with your Flask server. Basically, you’ll need to send a POST request to the Flask server with the input message that your chatbot receives, receive the output, and then use the sendTextMessage function to have the chatbot respond to the message. If you’ve cloned my repository, all you really need to do is replace the URL of the request function call with the URL of your own server.

    There ya go. You should be able to send messages to the chatbot, and see some interesting responses that (hopefully) resemble yourelf in some way.

    Please let me know if you have any issues or if you have any suggestions for making this README better. If you thought a certain step was unclear, let me know and I’ll try my best to edit the README and make any clarifications.

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    Facebook chatbot that I trained to talk like me using Seq2Seq

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    A simple python chatbot for Facebook messenger

    License

    hult/facebook-chatbot-python

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    README.md

    A simple python chatbot for Facebook Messenger.

    The bot reads incoming Facebook Messenger messages, and responds by generating random text from a corpus. The length of the response is randomly selected around the length of the incoming message, and a random word from the incoming message is selected as the starting word of the response.

    Use of virtualenv is highly recommended, especially since the old version of nltk used requires an old version of setuptools .

    $ pip install -r requirements.txt

    Setting up a Facebook app for Facebook messenger

    • You’re going to need a publicly routed https address. I used ngrok to create a tunnel to my local development machine.
    • The server will need to be started for you to verify the webhook. See «Starting the server» below.
    • Follow the instructions provided in the Facebook quickstart tutorial for creating a page and an app.
    • Set the VERIFY_TOKEN and FACEBOOK_TOKEN environment variables to the values you get from following the tutorial.

    Before you start the server, there has to be a file called corpus.txt in the root directory. This can be any text, but I found it interesting to use all of my sent emails as the corpus.

    If you have your emails in mbox format (you can get them from Gmail using Google Takeout), you can use the provided mail_corpus.py to build a corpus.

    $ python mail_corpus.py emails.mbox youremail1@example.com youremail2@example.com .

    This will grab the text from all emails from the specified addresses (it tries to not include text responded to, signatures and so on), and create a corpus from it.

    • A big corpus takes a loooong time to load
    • Tests
    • Send structured messages
    • Handle postbacks

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    A simple Chatbot for Facebook’s personal profile that can chat with any of your friends in python using fbchat & dialogflow/Api.ai

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    the-javapocalypse/Chatbot-For-Facebook

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    README.md

    Chatbot For Facebook Personal Profile in Python

    A simple Chatbot for Facebook’s personal profile that can chat with any of your friends and reply to them. Complete text and video based detailed tutorial link

    You will need to register a free account on dialogflow. After that you need to create an agent and train it (how to create and train an agent using dialogflow). After that copy Client Access Token somewhere because we will be needing that later.

    Download or Clone the repo, Navigate to the directory containing the files and run

    or if you have different versions of python installed then

    to install the dependencies.

    Note: If you’re facing problems in installation of fbchat in Ubuntu then run the following command.

    sudo apt-get --yes --force-yes install libxml2-dev libxslt1-dev libxml2 python-dev python3-dev zlib1g-dev

    After running setup.py, open bot.py and plug in your Client Accecss Token, email and password and run the bot. Now you can test the bot by logging into some other facebook account and starting a chat with the account you used to run the bot.

    1. Fork it
    2. Create your feature branch: git checkout -b my-new-feature
    3. Commit your changes: git commit -am ‘Add some feature’
    4. Push to the branch: git push origin my-new-feature
    5. Submit a pull request

    This project is licensed under the MIT License — see the LICENSE.md file for details

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    A simple Chatbot for Facebook’s personal profile that can chat with any of your friends in python using fbchat & dialogflow/Api.ai

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