A tool helps build a talk preview image by combining the given background image and talk event description

Overview

talk-preview-img-builder

A tool helps build a talk preview image by combining the given background image and talk event description

Installation and Usage

Install Dependencies

For running the app, you need to install the following dependencies by following command:

pipenv install -d

Run the Application

Before running the application, you need to prepare the material for building the talk preview images/slides. There are two materials that are required:

  • A background image named background.png which is located in the materials/img folder.

  • A talk event description named speeches.json which is located in the materials/ folder.

After preparing the material, you can run the application by following command:

pipenv run build_talk_preview_img   # build the talk preview images

or

pipenv run build_talk_preview_ppt  # build the talk preview slides

The generated talk preview images and slides are located in the export/ folder.

Configuring the Application

There are several options to configure the application, the default values are shown in the config.py file. You can override the default values by editing the config.py file or adding a .env file that setting theses variables before running the app.

Variable Description Default Value (Setting for Image/ Setting for Slides) Type (Setting for Image/ Setting for Slides)
BACKGROUND_IMG_PATH The path to the background image materials/img/background.png String
SPEECHES_PATH The path to the speech description materials/speeches.json String
PREVIEW_IMG_WIDTH The width of the generated preview image 700px / 30cm Integer / Float
PREVIEW_IMG_HEIGHT The height of the generated preview image 700px / 30cm Integer / Float
PREVIEW_IMG_TITLE_UPPER_LEFT_X The left position of the title in the upper left corner of the generated preview image 110px / 0.95cm Integer / Float
PREVIEW_IMG_TITLE_UPPER_LEFT_Y The top position of the title in the upper left corner of the generated preview image 110px / 1.04cm Integer / Float
PREVIEW_IMG_CONTENT_UPPER_LEFT_X The left position of the content in the upper left corner of the generated preview image 85px / 1.38cm Integer / Float
PREVIEW_IMG_CONTENT_UPPER_LEFT_Y The top position of the content in the upper left corner of the generated preview image 200px / 3.8cm Integer / Float
PREVIEW_IMG_FOOTER_UPPER_LEFT_X The left position of the footer in the upper left corner of the generated preview image 100px / 1.6cm Integer / Float
PREVIEW_IMG_FOOTER_UPPER_LEFT_Y The top position of the footer in the upper left corner of the generated preview image 650px / 12.2cm Integer / Float
PREVIEW_IMG_SPEAKER_UPPER_RIGHT_X The right position of the speaker name in the upper right corner of the generated preview image 600px / 13.5cm Integer / Float
PREVIEW_IMG_SPEAKER_UPPER_RIGHT_Y The top position of the speaker name in the upper right corner of the generated preview image 570px / 10cm Integer / Float
TITLE_HEIGHT The height of the title 70px / 1.84cm Integer / Float
CONTENT_HEIGHT The height of the content 90px / 7.5cm Integer / Float
PREVIEW_TEXT_COLOR The color of text used in the preview image #080A42 String
PREVIEW_HIGHTLIGHT_TEXT_COLOR The highlight color of text used in the preview image #EBCC73 String
PREVIEW_TEXT_FONT The font used in the preview image "PingFang.ttc"/"Taipei Sans TC Beta" String
PREVIEW_TEXT_BOLD_FONT The bold font used in the preview image "PingFang.ttc"/"Taipei Sans TC Beta" String

Coding Style

The coding style of the application is PEP8. You can use the following command to check the coding style:

pipenv run lint

and the following command to reformat the coding style which is leveraged by black and isort:

pipenv run reformat

TODO

  • Automatically generate the talk preview metadata file (e.g. speeches.json) from the PyConTW API server.
  • Implement hybrid language support text wrapping in title and content of the talk preview image.
  • Implement dynamic font size adjustment in the title and content of the talk preview image depending on the length of words.
  • Implement CI workflow by using GitHub Actions
Owner
PyCon Taiwan
PyCon Taiwan
Script to generate VAD dataset used in Asteroid recipe

About the dataset LibriVAD is an open source dataset for voice activity detection in noisy environments. It is derived from LibriSpeech signals (clean

11 Sep 15, 2022
ACL22 paper: Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little Cost

Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little Cost LOVE is accpeted by ACL22 main conference as a long pape

Lihu Chen 32 Jan 03, 2023
Course project of [email protected]

NaiveMT Prepare Clone this repository git clone [email protected]:Poeroz/NaiveMT.git

Poeroz 2 Apr 24, 2022
Text classification on IMDB dataset using Keras and Bi-LSTM network

Text classification on IMDB dataset using Keras and Bi-LSTM Text classification on IMDB dataset using Keras and Bi-LSTM network. Usage python3 main.py

Hamza Rashid 2 Sep 27, 2022
Quantifiers and Negations in RE Documents

Quantifiers-and-Negations-in-RE-Documents This project was part of my work for a

Nicolas Ruscher 1 Feb 01, 2022
Code for ACL 2021 main conference paper "Conversations are not Flat: Modeling the Intrinsic Information Flow between Dialogue Utterances".

Conversations are not Flat: Modeling the Intrinsic Information Flow between Dialogue Utterances This repository contains the code and pre-trained mode

ICTNLP 90 Dec 27, 2022
Natural language processing summarizer using 3 state of the art Transformer models: BERT, GPT2, and T5

NLP-Summarizer Natural language processing summarizer using 3 state of the art Transformer models: BERT, GPT2, and T5 This project aimed to provide in

Samuel Sharkey 1 Feb 07, 2022
Princeton NLP's pre-training library based on fairseq with DeepSpeed kernel integration 🚃

This repository provides a library for efficient training of masked language models (MLM), built with fairseq. We fork fairseq to give researchers mor

Princeton Natural Language Processing 92 Dec 27, 2022
Open source code for AlphaFold.

AlphaFold This package provides an implementation of the inference pipeline of AlphaFold v2.0. This is a completely new model that was entered in CASP

DeepMind 9.7k Jan 02, 2023
Easy to use, state-of-the-art Neural Machine Translation for 100+ languages

EasyNMT - Easy to use, state-of-the-art Neural Machine Translation This package provides easy to use, state-of-the-art machine translation for more th

Ubiquitous Knowledge Processing Lab 748 Jan 06, 2023
Uncomplete archive of files from the European Nopsled Team

European Nopsled CTF Archive This is an archive of collected material from various Capture the Flag competitions that the European Nopsled team played

European Nopsled 4 Nov 24, 2021
SpikeX - SpaCy Pipes for Knowledge Extraction

SpikeX is a collection of pipes ready to be plugged in a spaCy pipeline. It aims to help in building knowledge extraction tools with almost-zero effort.

Erre Quadro Srl 384 Dec 12, 2022
A natural language processing model for sequential sentence classification in medical abstracts.

NLP PubMed Medical Research Paper Abstract (Randomized Controlled Trial) A natural language processing model for sequential sentence classification in

Hemanth Chandran 1 Jan 17, 2022
Neural network sequence labeling model

Sequence labeler This is a neural network sequence labeling system. Given a sequence of tokens, it will learn to assign labels to each token. Can be u

Marek Rei 250 Nov 03, 2022
DANeS is an open-source E-newspaper dataset by collaboration between DATASET JSC (dataset.vn) and AIV Group (aivgroup.vn)

DANeS - Open-source E-newspaper dataset Source: Technology vector created by macrovector - www.freepik.com. DANeS is an open-source E-newspaper datase

DATASET .JSC 64 Aug 17, 2022
Longformer: The Long-Document Transformer

Longformer Longformer and LongformerEncoderDecoder (LED) are pretrained transformer models for long documents. ***** New December 1st, 2020: Longforme

AI2 1.6k Dec 29, 2022
Code for CVPR 2021 paper: Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers and Self-supervised Learning

Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers and Self-supervised Learning This is the PyTorch companion code for the paper: A

Amazon 69 Jan 03, 2023
Python api wrapper for JellyFish Lights

Python api wrapper for JellyFish Lights The hope is to make this a pip installable package Current capabalilities: Connects to a local JellyFish Light

10 Dec 18, 2022
ACL'22: Structured Pruning Learns Compact and Accurate Models

☕ CoFiPruning: Structured Pruning Learns Compact and Accurate Models This repository contains the code and pruned models for our ACL'22 paper Structur

Princeton Natural Language Processing 130 Jan 04, 2023
Package for controllable summarization

summarizers summarizers is package for controllable summarization based CTRLsum. currently, we only supports English. It doesn't work in other languag

Hyunwoong Ko 72 Dec 07, 2022