# chatbot123

**Repository Path**: bugno/chatbot123

## Basic Information

- **Project Name**: chatbot123
- **Description**: [unmaintained]
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-02-28
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## tensorflow seq2seq chatbot

> *Note: the repository is not maintained. Feel free to PM me if you'd like to take up the maintainance.*

Build a general-purpose conversational chatbot based on a hot 
seq2seq approach implemented in [tensorflow](https://www.tensorflow.org/versions/master/tutorials/seq2seq/index.html#sequence-to-sequence_basics).
Since it doesn't produce good results so far, also consider other implementations of [seq2seq](https://github.com/nicolas-ivanov/seq2seq_chatbot_links).

The current results are pretty lousy:

    hello baby	        - hello
    how old are you ?   - twenty .
    i am lonely	        - i am not
    nice                - you ' re not going to be okay .
    so rude	            - i ' m sorry .
    
Disclaimer: 

* the answers are hand-picked (it looks cooler that way)
* chatbot has no power to follow the conversation line so far; in the example above it's a just a coincidence (hand-picked one)

Everyone is welcome to investigate the code and suggest the improvements.

**Actual deeds**

* realise how to diversify chatbot answers (currently the most probable one is picked and it's dull)


**Papers**

* [Sequence to Sequence Learning with Neural Networks](http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf)
* [A Neural Conversational Model](http://arxiv.org/pdf/1506.05869v1.pdf)

**Nice picture**

[![seq2seq](https://4.bp.blogspot.com/-aArS0l1pjHQ/Vjj71pKAaEI/AAAAAAAAAxE/Nvy1FSbD_Vs/s640/2TFstaticgraphic_alt-01.png)](http://4.bp.blogspot.com/-aArS0l1pjHQ/Vjj71pKAaEI/AAAAAAAAAxE/Nvy1FSbD_Vs/s1600/2TFstaticgraphic_alt-01.png)

Curtesy of [this](http://googleresearch.blogspot.ru/2015/11/computer-respond-to-this-email.html) article.

**Setup**

    git clone git@github.com:nicolas-ivanov/tf_seq2seq_chatbot.git
    cd tf_seq2seq_chatbot
    bash setup.sh
    
**Run**

Train a seq2seq model on a small (17 MB) corpus of movie subtitles:

    python train.py
    
(this command will run the training on a CPU... GPU instructions are coming)

Test trained trained model on a set of common questions:

    python test.py
    
Chat with trained model in console:

    python chat.py
    
All configuration params are stored at `tf_seq2seq_chatbot/configs/config.py`

**GPU usage**

If you are lucky to have a proper gpu configuration for tensorflow already, this should do the job:

    python train.py
    
Otherwise you may need to build tensorflow from source and run the code as follows:

    cd tensorflow  # cd to the tensorflow source folder
    cp -r ~/tf_seq2seq_chatbot ./  # copy project's code to tensorflow root
    bazel build -c opt --config=cuda tf_seq2seq_chatbot:train  # build with gpu-enable option
    ./bazel-bin/tf_seq2seq_chatbot/train  # run the built code

**Requirements**

* [tensorflow](https://www.tensorflow.org/versions/master/get_started/os_setup.html)