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| # chatbot | ||
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| A cross-platform chatbot based on MS Bot Framework, PyTorch and a fine-tuned BERT model, to demonstrate how to easily create complex dialogs and deploy it on Azure. | ||
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| Test is on [chatbot.joffreybvn.be](https://chatbot.joffreybvn.be), **tell him that you want to book a hotel room**! | ||
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| ### Bot features | ||
| - Advanced NLU (thanks to BERT) to understand custom intentions | ||
| - Strong entity and keywords matcher, to detect complex informations | ||
| - Flexible dialogs flow that adapt to user's responses | ||
| - Easy-to-interact on any platform, thanks to cards, buttons and more | ||
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| ### The dialog flow | ||
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| ## Cross-plateforme implementation | ||
| <p align="center"> | ||
| <img src="./assets/images/bot_dialogflow.svg"> | ||
| </p> | ||
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| The process of transforming and sanitizing a message in order to be able to classify it. | ||
| The process of recognising and giving a label to a message. | ||
| ### Techs and libraries used | ||
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| ## fonctionnalités du Bot : | ||
| The efficient implementation of our solution relies on a plethora of solid libraries: | ||
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| ### Traitement des inputs "utilisateurs" | ||
| | Library | Used in | Detail | | ||
| |---|---|---| | ||
| | BeautifulSoup | [Preprocessor.py](/src/nlu/preprocessing/preprocessor.py) | Preventing and removing tags and other HTML elements | | ||
| | Unidecode | [Preprocessor.py](/src/nlu/preprocessing/preprocessor.py) | Removing all accents | | ||
| | SpaCy | [Preprocessor.py](/src/nlu/preprocessing/preprocessor.py) | Lemmatize and detect numbers written in letters | | ||
| | word2number | [Preprocessor.py](/src/nlu/preprocessing/preprocessor.py) | Replace the numbers written in letters, into digits | | ||
| | contractions | [Preprocessor.py](/src/nlu/preprocessing/preprocessor.py) | Detecting and replacing contracted forms of language | | ||
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| | transformers | [Classifier.py](/src/nlu/classifying/classifier.py) | Downloading and using BERT | | ||
| | PyTorch | [Classifier.py](/src/nlu/classifying/classifier.py) | Fine-tuning the model based on our dataset | | ||
| | PolyFuzz | [Classifier.py](/src/nlu/classifying/classifier.py) | With regex, to detect keywords and complex intentions | | ||
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| - Bert ? | ||
| ### This bot is cross-platform | ||
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| ### Possible réponses | ||
| All these libraries have been brought together with Microsoft's Bot Framework, a tool that allows us to publish this bot on all the following platforms: | ||
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| - Accueillire | ||
| - Décrire les fonctionnalités du bot | ||
| - Répondre | ||
| - Donner les heures d'ouvertures | ||
| - Afficher une liste d'objet, de produit | ||
| <p align="center"> | ||
| <img src="./assets/images/cross_platform.svg"> | ||
| </p> | ||
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| ### Nice to have | ||
| As of today, the bot is available on: | ||
| - Skype: [@demo_rasa_bot]() | ||
| - Telegram: [@demo_rasa_bot]() | ||
| - Webpage: [chatbot.joffreybvn.be](https://chatbot.joffreybvn.be) | ||
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| - Réserver un service / un produit | ||
| - Gérer un agenda | ||
| - vérifier la disponibilité | ||
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| ### Project timeline | ||
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| "**In English**, the bot should be able to :\n", | ||
| "\n", | ||
| "- Understand phrases related to a room reservation.\n", | ||
| "Example that the bot will have to understand: \n", | ||
| "\n", | ||
| "\t\t> I wish to reserve a room for 2 people.\n", | ||
| "\t\t> I wish to reserve a room for 4 days\n", | ||
| "\t\t> Do you have rooms available from July 23rd?\n", | ||
| "\t\t> I would like to reserve a room for two days and for two people\n", | ||
| "\n", | ||
| "- Understand phrases related to a table reservation for the restaurant. \n", | ||
| "\n", | ||
| "\t\t> I would like to make a reservation for tonight.\n", | ||
| "\t\t> I'd like to reserve a table for four people.\n", | ||
| "\n", | ||
| "- Must ensure a continuous and ongoing conversation. Example of a complete conversation : \n", | ||
| "\n", | ||
| "\t\t> Customer : Hello !\n", | ||
| "\t\t> Bot : Hello, how can I help you? \n", | ||
| "\t\t> Customer: I would like to reserve a table for 4 people ? \n", | ||
| "\t\t> Bot : For which date would you like to reserve your table?\n", | ||
| "\t\t> Customer : Today at 7:00 pm\n", | ||
| "\t\t> Bot : What name should I make the reservation under?\n", | ||
| "\t\t> Customer : My name is Mr. Dupont! \n", | ||
| "\t\t> Bot : Very well Mr Dupont, I confirm you the reservation of a table for 4 people tonight at 7:00 pm. \n", | ||
| "\t\t> Bot : Can I help you with something else?\n", | ||
| "\t\t> Customer : No thanks\n", | ||
| "\t\t> Bot: Have a nice day. \n", | ||
| "\n", | ||
| "- Understand when the client is angry. In this case, the bot will indicate that it is transmitting the conversation to a human. \n", | ||
| "\n", | ||
| "\t\t> You're incompetent!\n", | ||
| "\t\t> My room is dirty! This is outrageous!\n", | ||
| "\t\t> I want to talk to a human. \n", | ||
| "\n", | ||
| "### Nice-to-have features\n", | ||
| "- Create an API of your bot to make it cross-platform \n", | ||
| "- Use Docker\n", | ||
| <p align="center"> | ||
| <img src="./assets/images/project_timeline.svg"> | ||
| </p> | ||
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| ## Hébergement du Bot | ||
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| Timeline: | ||
| - Etablir l'objectif (déployer bot cross-plateforme + créer propre modele) | ||
| - Trouver un framework >> MSBotFramework | ||
| - Créer un dataset | ||
| - Deployer dummy bot | ||
| This project was completed in 5 days by two Machine Learning students from BeCode: | ||
| - **Vincent Leurs**: [Twitter](https://twitter.com/VincentLeurs) - [Github](https://github.com/paradous) | ||
| - **Joffrey Bienvenu**: [Website](https://joffreybvn.be/) - [Twitter](https://twitter.com/Joffreybvn) - [Github](https://github.com/joffreybvn) |
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