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Chatbot_ML

This is a Restaurant Chatbot for helping on different task, like greeting users, take order on different food/drinks,among other functions

In the following lines I'm going to describe how to train and test the chatbot on the UI webpage.

1. Fisrt copy or download the repo

Note: Be sure to save the repo on your GOPATH, inside src folder, or else you will have problems when looking for functions in other packages

2. Once you have all the files you can run the file main.go, inside web_api

3. This file will initialize the server, serve the html file on port 3000, you can easily change this inside main.go file

4. Once is loaded, you can co to localhost:3000, and insert a user

5. And that's it !!, you can now star chatting with the bot

Neural Network

If you want to modify the data base of the bot, you have to edit the chatss.txt file inisde the text_neural_network folder. there you will find the following structure: #sentence (category), be sure to follow this format, as it was taken as a directive to set the database of the network following a REGEX syntax.

If you want to add new categories, be sure to add some examples to the chatss.txt, and add the respective responses inside intents.json

For the network to work after modifications, you need to re-train it, be sure to follow the following steps:

1. Navigate to the text_neural_network folder

2. Build the neural_network.go file with : go build text_neural_network

3. After building it, run the following for training: text_neural_network -command=train

4. Then after finishing the training you can test an input if you want with: text_neural_network -command=test user_input="test_sentence_here"

Final Comments

This is an early model, I'm currently workin on, I'm planning on keep doing improves to the code, and expanding the data base. Also implementing other features like, grammar mistakes identifier, and typo errors identification.

If you have any commments or suggestion please, you are welcome to contact me.

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Restaurant Chatbot implemented with a Deep ANN

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