Telegram chatbot created with deep learning model (LSTM) and telebot library.

Overview

Telegram chatbot

Telegram chatbot created with deep learning model (LSTM) and telebot library.

forthebadge badge-telegram-program

Description

This program will allow you to create very easily a custom chatbot capable of sending texts, images or videos.
The deep learning model used is a Long Short Term Memory (LSTM) but you can change his structure if you want.

Getting Started

Install the libraries

Execute the following command : pip install -r requirements.txt

Create a bot in telegram

To do that, you have to open your telegram application. Then speak to this user : @BotFather. It is a bot created by telegram itself that allows you to manage the creation and the editing of your bots. Just follow the instructions and get the API token of your bot.
You can follow this tutorial : tutorial

Change the settings

Once you got the api token of your bot, in the my_config.py file change the value of TOKEN.

Last step

Before you can run this program, you will have to complete the most important file : ìntents.json In this file you will have to write all the sentences you would like the chatbot learn.

  • tag : title of the question/answer. It does not matter for the model. This is just for you.
  • type : You have 3 possibilities
    • text : the chatbot will answer only with a text message
    • file : the chatbot will send a text message followed by a document
    • multiple_photos : the chatbot will send text messages with pictures attached.
  • patterns : write all possible turns of phrase for a question
  • responses : write the chatbot's answers. You can write more than one. The chatbot will choose randomly one of them.
  • link : path to your documents and photos

Launch

Execute the following command in a terminal : python main.py

You can now speak to your bot in your telegram application !

Unsupervised Image Generation with Infinite Generative Adversarial Networks

Unsupervised Image Generation with Infinite Generative Adversarial Networks Here is the implementation of MICGANs using DCGAN architecture on MNIST da

16 Dec 24, 2021
Official PyTorch implementation of Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval.

Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval PyTorch This is the PyTorch implementation of Retrieve in Style: Unsupervised Fa

60 Oct 12, 2022
Learning Lightweight Low-Light Enhancement Network using Pseudo Well-Exposed Images

Learning Lightweight Low-Light Enhancement Network using Pseudo Well-Exposed Images This repository contains the implementation of the following paper

Seonggwan Ko 9 Jul 30, 2022
This project contains an implemented version of Face Detection using OpenCV and Mediapipe. This is a code snippet and can be used in projects.

Live-Face-Detection Project Description: In this project, we will be using the live video feed from the camera to detect Faces. It will also detect so

Hassan Shahzad 3 Oct 02, 2021
Dynamic Head: Unifying Object Detection Heads with Attentions

Dynamic Head: Unifying Object Detection Heads with Attentions dyhead_video.mp4 This is the official implementation of CVPR 2021 paper "Dynamic Head: U

Microsoft 550 Dec 21, 2022
Tensorflow implementation and notebooks for Implicit Maximum Likelihood Estimation

tf-imle Tensorflow 2 and PyTorch implementation and Jupyter notebooks for Implicit Maximum Likelihood Estimation (I-MLE) proposed in the NeurIPS 2021

NEC Laboratories Europe 69 Dec 13, 2022
Ludwig is a toolbox that allows to train and evaluate deep learning models without the need to write code.

Translated in 🇰🇷 Korean/ Ludwig is a toolbox that allows users to train and test deep learning models without the need to write code. It is built on

Ludwig 8.7k Jan 05, 2023
A modification of Daniel Russell's notebook merged with Katherine Crowson's hq-skip-net changes

Edits made to this repo by Katherine Crowson I have added several features to this repository for use in creating higher quality generative art (featu

Paul Fishwick 10 May 07, 2022
PyTorch implementation of neural style transfer algorithm

neural-style-pt This is a PyTorch implementation of the paper A Neural Algorithm of Artistic Style by Leon A. Gatys, Alexander S. Ecker, and Matthias

770 Jan 02, 2023
The official PyTorch implementation for the paper "sMGC: A Complex-Valued Graph Convolutional Network via Magnetic Laplacian for Directed Graphs".

Magnetic Graph Convolutional Networks About The official PyTorch implementation for the paper sMGC: A Complex-Valued Graph Convolutional Network via M

3 Feb 25, 2022
🏎️ Accelerate training and inference of 🤗 Transformers with easy to use hardware optimization tools

Hugging Face Optimum 🤗 Optimum is an extension of 🤗 Transformers, providing a set of performance optimization tools enabling maximum efficiency to t

Hugging Face 842 Dec 30, 2022
This repository is based on Ultralytics/yolov5, with adjustments to enable polygon prediction boxes.

Polygon-Yolov5 This repository is based on Ultralytics/yolov5, with adjustments to enable polygon prediction boxes. Section I. Description The codes a

xinzelee 226 Jan 05, 2023
[ICCV 2021] FaPN: Feature-aligned Pyramid Network for Dense Image Prediction

FaPN: Feature-aligned Pyramid Network for Dense Image Prediction [arXiv] [Project Page] @inproceedings{ huang2021fapn, title={{FaPN}: Feature-alig

Shihua Huang 23 Jul 22, 2022
A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis

A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis Project Page | Paper A Shading-Guided Generative Implicit Model

Xingang Pan 115 Dec 18, 2022
Latex code for making neural networks diagrams

PlotNeuralNet Latex code for drawing neural networks for reports and presentation. Have a look into examples to see how they are made. Additionally, l

Haris Iqbal 18.6k Jan 01, 2023
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning

Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning This is the official repository for Conservative and Adaptive Penalty fo

7 Nov 22, 2022
基于Paddle框架的arcface复现

arcface-Paddle 基于Paddle框架的arcface复现 ArcFace-Paddle 本项目基于paddlepaddle框架复现ArcFace,并参加百度第三届论文复现赛,将在2021年5月15日比赛完后提供AIStudio链接~敬请期待 参考项目: InsightFace Padd

QuanHao Guo 16 Dec 15, 2022
The versatile ocean simulator, in pure Python, powered by JAX.

Veros is the versatile ocean simulator -- it aims to be a powerful tool that makes high-performance ocean modeling approachable and fun. Because Veros

TeamOcean 245 Dec 20, 2022
This source code is implemented using keras library based on "Automatic ocular artifacts removal in EEG using deep learning"

CSP_Deep_EEG This source code is implemented using keras library based on "Automatic ocular artifacts removal in EEG using deep learning" {https://www

Seyed Mahdi Roostaiyan 2 Nov 08, 2022
Quasi-Dense Similarity Learning for Multiple Object Tracking, CVPR 2021 (Oral)

Quasi-Dense Tracking This is the offical implementation of paper Quasi-Dense Similarity Learning for Multiple Object Tracking. We present a trailer th

ETH VIS Research Group 327 Dec 27, 2022