ibra-deme/projet-master

chatbot specialiser à l'orientation des nouveaux bacheriers et etudiants du senegal # Implementation of a Contextual Chatbot in PyTorch. Simple chatbot implementation with PyTorch. - The implementation should be easy to follow for beginners and provide a basic understanding of chatbots. - The implementation is straightforward with a Feed Forward Neural net with 2 hidden layers. - Customization for your own use case is super easy. Just modify `intents.json` with possible patterns and responses and re-run the training (see below for more info). The approach is inspired by this article and ported to PyTorch: chatbotsmagazine.com. ## Watch the Tutorial ## Installation ### Create an environment Whatever you prefer (e.g. `conda` or `venv`) ```console mkdir myproject $ cd myproject $ python3 -m venv venv ``` ### Activate it Mac / Linux: ```console . venv/bin/activate ``` Windows: ```console venv\Scripts\activate ``` ### Install PyTorch and dependencies For Installation of PyTorch see official website. You also need `nltk`: ```console pip install nltk ``` If you get an error during the first run, you also need to install `nltk.tokenize.punkt`: Run this once in your terminal: ```console $ python >>> import nltk >>> nltk.download('punkt') ``` ## Usage Run ```console python train.py ``` This will dump `data.pth` file. And then run ```console python chat.py ``` ## Customize Have a look at intents.json. You can customize it according to your own use case. Just define a new `tag`, possible `patterns`, and possible `responses` for the chat bot. You have to re-run the training whenever this file is modified. ```console { "intents": [ { "tag": "greeting", "patterns": [ "Hi", "Hey", "How are you", "Is anyone there?", "Hello", "Good day" ], "responses": [ "Hey :-)", "Hello, thanks for visiting", "Hi there, what can I do for you?", "Hi there, how can I help?" ] }, ... ] } ```