Explore all artificial intelligence open source software, libraries, packages, source code, cloud functions and APIs.

Popular New Releases in Artificial Intelligence

tensorflow

TensorFlow 2.9.0-rc1

youtube-dl

youtube-dl 2021.12.17

models

TensorFlow Official Models 2.7.1

transformers

v4.18.0: Checkpoint sharding, vision models

opencv

OpenCV 4.5.5

Popular Libraries in Artificial Intelligence

tensorflow

by tensorflow doticonc++doticon

star image 164372 doticonApache-2.0

An Open Source Machine Learning Framework for Everyone

youtube-dl

by ytdl-org doticonpythondoticon

star image 108335 doticonUnlicense

Command-line program to download videos from YouTube.com and other video sites

models

by tensorflow doticonpythondoticon

star image 73392 doticonNOASSERTION

Models and examples built with TensorFlow

transformers

by huggingface doticonpythondoticon

star image 61400 doticonApache-2.0

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

opencv

by opencv doticonc++doticon

star image 60896 doticonNOASSERTION

Open Source Computer Vision Library

pytorch

by pytorch doticonc++doticon

star image 55457 doticonNOASSERTION

Tensors and Dynamic neural networks in Python with strong GPU acceleration

keras

by keras-team doticonpythondoticon

star image 55007 doticonApache-2.0

Deep Learning for humans

awesome-machine-learning

by josephmisiti doticonpythondoticon

star image 51223 doticonNOASSERTION

A curated list of awesome Machine Learning frameworks, libraries and software.

scikit-learn

by scikit-learn doticonpythondoticon

star image 49728 doticonBSD-3-Clause

scikit-learn: machine learning in Python

Trending New libraries in Artificial Intelligence

ML-For-Beginners

by microsoft doticonjupyter notebookdoticon

star image 30013 doticonMIT

12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all

yolov5

by ultralytics doticonpythondoticon

star image 25236 doticonGPL-3.0

YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite

MockingBird

by babysor doticonpythondoticon

star image 20425 doticonNOASSERTION

🚀AI拟声: 5秒内克隆您的声音并生成任意语音内容 Clone a voice in 5 seconds to generate arbitrary speech in real-time

PaddleOCR

by PaddlePaddle doticonpythondoticon

star image 19581 doticonApache-2.0

Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)

GFPGAN

by TencentARC doticonpythondoticon

star image 17269 doticonNOASSERTION

GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.

fastbook

by fastai doticonjupyter notebookdoticon

star image 14674 doticonGPL-3.0

The fastai book, published as Jupyter Notebooks

jina

by jina-ai doticonpythondoticon

star image 14316 doticonApache-2.0

Cloud-native neural search framework for 𝙖𝙣𝙮 kind of data

AI-Expert-Roadmap

by AMAI-GmbH doticonjavascriptdoticon

star image 13925 doticonMIT

Roadmap to becoming an Artificial Intelligence Expert in 2021

datasets

by huggingface doticonpythondoticon

star image 13088 doticonApache-2.0

🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools

Top Authors in Artificial Intelligence

1

PacktPublishing

457 Libraries

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2

microsoft

370 Libraries

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3

facebookresearch

310 Libraries

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4

IBM

242 Libraries

star icon12510

5

google

162 Libraries

star icon131107

6

aws-samples

131 Libraries

star icon4838

7

llSourcell

118 Libraries

star icon16623

8

Apress

109 Libraries

star icon2299

9

google-research

96 Libraries

star icon92917

10

allenai

96 Libraries

star icon24831

1

457 Libraries

star icon23374

2

370 Libraries

star icon217404

3

310 Libraries

star icon270473

4

242 Libraries

star icon12510

5

162 Libraries

star icon131107

6

131 Libraries

star icon4838

7

118 Libraries

star icon16623

8

109 Libraries

star icon2299

9

96 Libraries

star icon92917

10

96 Libraries

star icon24831

Trending Kits in Artificial Intelligence


AI fake news detector helps detect fake news through binary classification methods. It helps build experiences by controlling the flow of disinformation. It's built on top of various powerful machine learning libraries. The tool works by training a neural network to spot fake articles based on their text content. When you run your own data through the tool, it gives you back a list of articles that it thinks are likely to be fake. You can then train the model further or decide if those results are acceptable or not. In addition to identifying fake news, this model can also be trained to identify real news. This allows you to compare the model's performance across different domains (e.g., politics vs. sports). The following installer and deployment instructions will walk you through the steps of creating an AI fake news detector by using fakenews-detection, jupyter, vscode, and pandas. We will use fake news detection libraries (having fully modifiable source code) to customize and build a simple classifier that can detect fake news articles. kandi kit provides you with a fully deployable AI Fake News Detector. Source code included so that you can customize it for your requirement.

With this kit, you can

1. Use a pre-trained model for detecting fake news.

2. Train the model on your custom dataset.

3. Expose the fake news detection as an API


Add-on on examples are also included as given below

1. Use web scraper to automatically make your training dataset.

2. Visualise training and prediction data for useful insights.

Instructions to Run

Follow the below instructions to run the solution.


1. Locate and open the FakeNewsDetection-starter.ipynb notebook from the Jupyter Notebook browser window.

2. Execute cells in the notebook by selecting Cell --> Run All from Menu bar

3. Once all the cells of the notebook are executed, the prediction result will be written to the file 'fake_news_test_output.csv'


Training with your dataset:

1. Add news articles to a csv file under a column name 'news_text'.

2. Add corresponding labels as 'real' or 'fake' denoting whether a news article is real or not.

3. You can refer to the file 'fake_news_train.csv' for an example.

4. Set the variable for training file in the notebook under Variables section.


Testing with your dataset:

1. Add news articles to a csv file under a column name 'news_text'.

2. You can refer to the file 'fake_news_test.csv' for an example.

3. Set the variable for testing file in the notebook under Variables section.


You can execute the cells of notebook by selecting Cell from the menu bar.


For any support, you can reach us at FAQ & Support

Libraries useful for this solution

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

Exploratory Data Analysis

For extensive analysis and exploration of data, and to deal with arrays, these libraries are used. They are also used for performing scientific computation and data manipulation.

Text mining

Libraries in this group are used for analysis and processing of unstructured natural language. The data, as in its original form aren't used as it has to go through processing pipeline to become suitable for applying machine learning techniques and algorithms.

Machine Learning

Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning.

Data Visualization

The patterns and relationships are identified by representing data visually and below libraries are used for generating visual plots of the data.

Troubleshooting

1. If you encounter any error related to MS Visual C++, please install MS Visual Build tools

2.While running batch file, if you encounter Windows protection alert, select More info --> Run anyway.

3.During kit installer, if you encounter Windows security alert, click Allow.

4. If you encounter Memory Error, check if the available memory is sufficient and it is proportion to the size of the data being used. For our dataset, the minimum required memory is 8GB.


If your computer doesn't support standard commands from windows 10, you can follow the instructions below to finish the kit installation.

1. Click here to install python

2. Click here to download the repository

3. Extract the zip file and navigate to the directory 'fakenews-detection-main'

4. Open terminal in the extracted directory 'fakenews-detection-main'

5. Install dependencies by executing the command 'pip install -r requirements.txt'

6. Run the command ‘jupyter notebook’ and select the notebook ‘FakeNewsdetection-starter.ipynb’ on the browser window.

Support

For any support, you can reach us at FAQ & Support

kandi 1-Click Install


Deepfake detection is identifying manipulated or synthetic media content using machine learning algorithms and computer vision techniques. It detects anomalies in facial and body movements, and other visual artifacts.


In this kit, we build a Deepfake Detection Engine using the popular Facenet_pytorch is a Python library that provides implementations of deep learning models for face recognition tasks. It includes pre-trained models such as


  1. MTCNN (Multi-Task Cascaded Convolutional Networks) for face detection and alignment, and
  2. InceptionResnetV1 for detecting whether an image is fake or real.


We use these two models to detect and recognize faces in images with high accuracy. The library is built on top of PyTorch, a popular open-source machine learning framework, and provides an easy-to-use API for face recognition tasks

Libraries used in this solution


Development Environment


VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.


Jupyter Notebook is used for our development.

Machine Learning


Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning

Kit Solution Source


API Integration

Support


For any support, you can reach us at OpenWeaver Community Support

kandi 1-Click Install



Generative artificial intelligence (AI) describes algorithms that help in creating/generating new content, including audio, code, images, text and videos. 

 

In this kit, we build a real-time Voice-to-Image Generator using the concept of Generative AI. It is carried out in two steps:

 

  • Voice-to-text conversion - The speech is captured in real-time through the microphone and converted to text using state-of-the-art Opensource AI models from OpenAI and Whisper libraries.

 

  • Text to Image Generation - The converted text is provided as input to the state-of-the-art Image Generation models like Dalle-2, and the image is thus generated.

Libraries used in this solution


Development Environment


VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.


Jupyter Notebook is used for our development.

Machine Learning


Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning

Kit Solution Source

UI App Integration

Support


For any support, you can reach us at OpenWeaver Community Support

kandi 1-Click Install


This Predictive Analytics kit provides an analytical view of students’ performance in mathematics and predicts grades to be scored in the final test.


The key features of this solution are:


  • Analysis of grades of students
  • Visualisation of patterns
  • Prediction of grade in the final test

For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

Data Mining

Our solution integrates data from various sources, and we have used below libraries for exploring patterns in these data and understanding correlation between the features.

Data Visualisation

The patterns and relationships are identified by representing data visually and below libraries are used for that.

Machine learning

Below libraries and model collections helps to create the machine learning models for the core prediction of use case in our solution.

Support

If you need help using this kit, you may reach us at the OpenWeaver Community.

kandi 1-Click Install

The use case of AI Course Recommender System is to provide personalized recommendation to the user based on their interest, course they can take and their current knowledge. This system will be able to recommend course based on user’s interest, current knowledge, analytical view of students’ performance in mathematics and recommends if a student can consider math subject for his/ her higher education. The recommended course will be based on the information of user’s profile, analysis of grades of students, visualization of patterns, prediction of grade in final test, and some rules that were set by their instructor. Using machine learning algorithms, we can train our model on a set of data and then predict the ratings for new items. This is all done in Python using numpy, pandas, matplotlib, scikit-learn and seaborn. kandi kit provides you with a fully deployable AI Course Recommender System. Source code included so that you can customize it for your requirement.

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.

Data Mining

Our solution integrates data from various sources, and we have used below libraries for exploring patterns in these data and understanding correlation between the features.

Data Visualisation

The patterns and relationships are identified by representing data visually and below libraries are used.

Machine learning

Below libraries and model collections helps to create the machine learning models for the core prediction of use case in our solution.


Federated Learning can train machine learning models on data from different hospitals, banks and autonomous vehicles without sharing sensitive data. But how do you create a Federated learning application? The answer is the kandi 1-click solution kit for Credit-risk-federated-learning.


Certainly, Federated Learning can be applied in the credit risk scenario to improve credit risk models' accuracy without compromising customer data privacy.


Banks collect and centralize customer data to train their credit risk models in the traditional approach. However, this approach can be challenging due to regulatory compliance, data privacy, and security concerns. Federated Learning addresses these challenges by allowing banks to train their credit risk models on customer data without transferring it to a centralized location.


This fully editable source code builds your Credit risk federated learning in minutes. The entire solution is available as a package to download from the source code repository.


Federated Learning in credit risk scenarios can have several benefits, including:


  • Improved accuracy: Federated Learning allows banks to train models on a larger and more diverse dataset, leading to better accuracy.
  • Data privacy: Federated Learning ensures that sensitive customer data is kept private and secure, which is critical in the context of credit risk.
  • Regulatory compliance: Federated Learning can help banks comply with regulations around data privacy and security.

Troubleshooting


  1. Install the Microsoft Visual C++ Redistributable for Visual Studio 2022 in case the kit doesn't successfully run on your windows system.
  2. In case, step 1 doesn't solve your issue, set up Microsoft build Tools.

For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

Data Pre-processing

Numpy and Pandas are powerful tools for data preprocessing in machine learning. They provide tools for handling missing data, feature scaling, one-hot encoding, data normalization, and transformation.

These tools can help you to prepare your data for machine learning and improve the performance of your models.

Machine learning

Scikit-learn is a powerful and versatile machine learning library in Python that provides a wide range of tools and algorithms for building and training machine learning models. It is widely used in academia and industry for various machine learning applications.

Federated Learning Framework

Flower is an open-source framework for Federated Learning that provides tools and APIs to simplify the development and deployment of Federated Learning models. Flower is designed to make it easier for developers to implement Federated Learning in their applications by providing a flexible and scalable platform for building and training models.

Kit Solution Source

Support

If you need help using this kit, you may reach us at the OpenWeaver Community.

kandi 1-Click Install


Large Language Models are foundation models that utilize deep learning in natural language processing and natural language generation tasks. Typically these models are trained on billions of parameters with a huge corpus of data.


GPT4all provides an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue. GPT4All is a 7B parameter LLM trained using a Low-Rank Adaptation (LoRA) method, yielding 430k post-processed instances, on a vast curated corpus of over 800k high-quality assistant interactions.


In this kit, we will use GPT4All to create a content generator, similar to ChatGPT, without the need for API keys and Internet to create content.

Libraries used in this solution

Development Environment


VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.


Jupyter Notebook is used for our development.

Machine Learning


Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning

Kit Solution Source


API Integration


Support


For any support, you can reach us at OpenWeaver Community Support

kandi 1-Click Install


The next word predictor is an exciting feature that helps you type faster on your mobile phone. It predicts the next word in the context you want to type. It is a very useful tool for people who type often and make mistakes while typing. It can be leveraged for auto-suggestion features in messenger and search engine apps.


The next word predictor makes it easy for readers to understand what exactly you are trying for them to read about.

  • Next word predictor is a very useful feature as it increases the readability of your content as well as makes it more understandable for readers.
  • Saves time by reducing the number of typos and grammatical errors in your content.
  • Modify source code to customize as per your requirements.

Instructions to Run

Follow the below instructions to run the solution.

  1. Locate and open the 'Next Word Predictor.ipynb' notebook from the Jupyter Notebook browser window.
  2. Execute cells in the notebook by selecting Cell --> Run All from the Menu bar.
  3. Once all the cells of the notebook are executed, the last interactive cell (Customisation) will be active, there we can give the input data or we can give the input text in the variable 'text_seq' under the variable section.


Input

text_seq = "I'm gonna make him an offer he can't"


Output

['refuse', 'resist', 'take', 'deny', 'get']

Troubleshooting

  1. If you encounter any error related to MS Visual C++, please install MS Visual Build tools
  2. While running batch file, if you encounter Windows protection alert, select More info --> Run anyway.
  3. During kit installer, if you encounter Windows security alert, click Allow.
  4. If you encounter Memory Error, check if the available memory is sufficient and it is proportion to the size of the data being used. For our dataset, the minimum required memory is 8GB.


If your computer doesn't support standard commands from windows 10, you can follow the instructions below to finish the kit installation.

  1. Install python
  2. Download the repository
  3. Extract the zip file and navigate to the directory 'next-word-prediction-main'
  4. Open terminal in the extracted directory 'next-word-prediction-main'
  5. Install dependencies by executing the command 'pip install -r requirements.txt'
  6. Run the command ‘jupyter notebook’ and select the notebook ‘Next Word Predictor.ipynb’ on the browser window.

For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web-based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

Exploratory Data Analysis

For extensive analysis and exploration of data, and to deal with arrays, these libraries are used. They are also used for performing scientific computation and data manipulation.

Text Mining

Libraries in this group are used for analysis and processing of unstructured natural language.

Machine Learning

The library offers state-of-the-art pre-trained models for Natural Language Processing (NLP).

Support

If you need help using this kit, you may reach us at the OpenWeaver Community.

kandi 1-Click Install


AI-powered emoji detectors can help increase engagement with their customers. It will help them to build strong relationships with their customers. The emoji detector will help you in analyzing your audience and their preferences so that you can deliver the right content. You can also use the technology to provide customer support to your customers by providing customized answers.


One of the most important aspects of AI-Powered Emoji Detector is that it will help you in detecting any kind of emotions and expressions on your face OR hand gestures from a web camera. It will help in detecting whether you are happy, sad, or angry, and so on. This technology is also used for predicting different kinds of expressions like happiness, fear, sadness, etc.

For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

Image Preparation and Processing

These libraries help in preparing data by annotating and labelling images. Also processes images for running machine learning algorithm. We use opencv library for capturing frames from live streaming videocam.

Data Analysis/Manipulation

These libraries help in analyzing data and doing data manipulations.

Machine Learning

Below libraries and model collections helps to create the machine learning models for the core recognition use cases in our solution.

Utilities

The below utility library helps in storing huge amounts of numerical data and manipulate that data easily from NumPy.

Support

If you need help using this kit, you may reach us at the OpenWeaver Community.

kandi 1-Click Install


AI Object Detection is used to build computer vision-based applications. It helps in face & vehicle detection, pedestrian counting, and security systems. 


Using this 1-click install kandi kit you can build an application that can (a) localize and classify objects, (b) detect objects in a video stream. You can download this pre-trained model and run it on any device. It is fast and very effective at identifying objects in images with high accuracy (99%). It also provides many advanced features like face detection, smile detection, etc. without any extra effort from your side!

For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

Image Preparation and Processing

These libraries help in preparing data by annotating and labelling images. Also processes images for running machine learning algorithm. We use opencv library for capturing frames from live streaming videocam.

Machine Learning

There are libraries and model collections that help to create machine learning models for the core recognition use cases in our solution. We will use pytorch to load pre-trained models of Object detection.

Support

If you need help using this kit, you may reach us at the OpenWeaver Community.

kandi 1-Click Install


Real-time object tracking system is a technology used to track objects in real time. It can be used for security purposes or for commercial purposes. Tracking can be done for video formats and live streaming webcam.


The real-time object tracking system has many applications, such as in retail stores, airports, stadiums and other places where security is important. The system can be used to monitor customer activity in stores, track inventory and detect shoplifting. It can also be used to increase safety in public places by monitoring the movements of pedestrians or vehicles.

For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community

Development Environment

VSCode and Jupyter Notebook can be used for development and debugging. Jupyter Notebook is a web-based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.

Object Detection and Tracking

The following libraries have a set of pre-trained models which could be used to identify objects and track them from live streaming videos.

Machine Learning Libraries

The following libraries could be used to create machine learning models which focus on the vision, extraction of data, image processing, and more. Thus making it handy for the users.

Support

If you need help using this kit, you may reach us at the OpenWeaver Community.

kandi 1-Click Install


Disease predictor is a way to recognize patient’s health by applying data mining and machine learning techniques on patient treatment history.


Symptoms, Diagnosis for Personalized Healthcare Services for a Predictive Analytic Perspective. Pandas library is used in this kandi kit to predict the probability of disease. The kit has used pandas to load datasets and visualize the data, NumPy to implement our algorithm, and sklearn-pandas to build our model.


In this project we will be using Pandas and Scikit-Learn to create a model that predicts whether or not a patient has a disease based on their demographics and lab results. We will also be using Jupyter Notebook to write code interactively so that we can see how our model performs when we change various parameters such as the number of features, amount of training data, etc.


kandi kit provides you with a fully deployable Disease Predictor. Source code included so that you can customize it for your requirement.

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

Exploratory Data Analysis

For extensive analysis and exploration of data, and to deal with arrays, these libraries are used. They are also used for performing scientific computation and data manipulation.

Data Visualization

The patterns and relationships are identified by representing data visually and below libraries are used for generating visual plots of the data.

Support

If you need help to use this kit, you can email us at kandi.support@openweaver.com or direct message us on Twitter Message @OpenWeaverInc .

kandi 1-Click Install

Python Machine Learning libraries help develop supervised and unsupervised learning, data pre-processing, feature extraction tools, and deep learning. 

 

Following are the top use cases of these shortlisted libraries for Python Machine Learning,

  • Pre-processing of data that includes data cleaning and feature engineering tasks such as normalization, imputation, missing value treatment, and outlier detection.
  • Model selecting and optimizing, such as cross-validation, hyperparameter tuning, and model selection metrics.
  • Visualizations to understand data and results. This includes visualizing data distributions, feature importance, and model performance.
  • Developing algorithms, including supervised learning algorithms (e.g. linear regression, logistic regression, support vector machines, decision trees, random forests, and neural networks) as well as unsupervised learning algorithms (e.g. clustering, dimensionality reduction, and anomaly detection).
  • Calculating performance metrics such as accuracy, precision, recall, and F1 score.

 

The following is a list of the 18 most popular open-source Python libraries for Machine Learning,

keras:  

  • It provides a high-level API for building and training deep neural networks.   
  • Keras allows you to define and incorporate custom layers and loss functions.    
  • Configure Keras to run on top of deep learning frameworks like TensorFlow, etc.

Scikit-Learn:  

  • It is an essential library in the field of machine learning and data science.  
  • It provides tools for cross-validation, hyperparameter tuning, and model selection.    
  • The library runs on top of other scientific Python libraries like NumPy and SciPy.   

Pandas:  

  • It is a popular Python library for data manipulation and analysis.   
  • It offers tools for data cleaning. This includes handling missing values, data alignment, and data type conversion.   
  • It supports time series data, making it valuable for financial analysis and forecasting.   

YOLOv5:  

  • "You Only Look Once version 5," is a popular computer vision model for object detection. 
  • It is popular for its real-time object detection capabilities. 
  • It has improved upon the accuracy of its predecessors while maintaining its speed. 

Ray:  

  • It is an open-source distributed computing framework used in Python.   
  • It enables you to parallelize and distribute Python applications.   
  • It helps with low-latency, high-throughput computing tasks.   

ML-From-Scratch:  

  • This helps you gain a deep understanding of the underlying algorithms and mathematics.   
  • This allows you to customize it for your specific problem and data. This makes it more effective and efficient.   
  • Building models from scratch provides insight into optimization techniques.   

examples:  

  • It helps in AI, ML, DL, Pytorch, TensorFlow applications.   
  • This library in PyTorch is essential for working with computer vision tasks.   
  • You can access pre-trained models like ResNet, VGG, and AlexNet through "torchvision.models".   

Paddle:  

  • It is an open-source deep learning platform developed by Baidu.  
  • It is a powerful deep learning framework, like TensorFlow and PyTorch.   
  • It focuses on simplicity and efficiency.  

rasa:  

  • It is an open-source Python library designed for building conversational AI apps.   
  • It provides tools for creating and managing conversational flows.  
  • It supports many languages and can helps in a global context.  

horovod:  

  • It is a popular library in Python used for distributed deep learning.   
  • It enables you to scale your DL models to many GPUs and even across many machines.   
  • It supports various deep learning frameworks like TensorFlow, PyTorch, and MXNet.   

mlflow:  

  • It is an open-source platform for managing the end-to-end machine learning lifecycle.  
  • It allows you to log and compare experiments.  
  • It provides tools for packaging models in a standard format.

imgaug:  

  • It is an important tool for image augmentation. It is especially used in machine learning and computer vision tasks.   
  • It allows you to customize augmentation pipelines to suit your specific needs.    
  • It works well with other popular libraries like OpenCV and NumPy.  

ChatterBot:  

  • It provides a framework and pre-built components. That makes it easier to create chatbots.   
  • This library often includes NLP capabilities. This allows chatbots to understand and generate human-like text responses.  
  • These libraries offer options for customizing the behavior and responses of chatbots.

nni:  

  • NNI handles distributed training, making it suitable for large-scale experiments.  
  • NNI is important for streamlining and improving the machine learning model development process.  
  • It automates and optimizes ML model selection and hyperparameter tuning.    

numpy-ml:  

  • It is a fundamental library in the Python ecosystem. It is especially used in the context of machine learning and data science.  
  • It is open-source and has a large and active community.  
  • It is crucial for performing efficient numerical and array-based operations.  

tpot: 

  • It is a Python library for automated machine learning (AutoML).  
  • This includes feature selection, data preprocessing, and the choice of models.
  • It employs techniques like cross-validation to reduce the risk of overfitting.    

autokeras:  

  • It is an open-source library for automated machine learning (AutoML).  
  • It simplifies the process of building and training machine learning models.  
  • It is accessible to both beginners and experienced ML practitioners.  

pattern:  

  • It is often referred to as a design pattern library.   
  • It is a collection of reusable solutions to common software design problems.
  • These patterns help developers create more efficient, maintainable, and scalable code.  

FAQ 

1. What is scikit-learn?  

It is an ML library for Python. That provides simple and efficient tools for data analysis and modeling. It offers a wide range of algorithms for classification, regression, clustering, and more.    

 

2. What is PyTorch?   

PyTorch is an open-source machine learning library. It is developed by Facebook's AI Research lab. It helps with deep learning and provides dynamic computation graphs. This makies it popular among researchers.    

 

3. What is Keras?   

Keras is an open-source deep learning API. That runs on top of other deep learning frameworks like TensorFlow and Theano. It's designed to be and allows for rapid prototyping of neural networks.   

 

4. How do I install these libraries?  

You can install these libraries using Python's package manager, pip. For example, you can install scikit-learn with pip install scikit-learn. Also, install TensorFlow with pip install tensorflow, and PyTorch with pip install torch.    

 

5. What is the difference between a tensor and an array in TensorFlow?  

In TensorFlow, a tensor is a multi-dimensional array. This array can be placed on GPU for accelerated computation. It is like NumPy arrays but optimized for deep learning tasks. 

Real-time speech recognition in Python refers to the ability of a computer program to transcribe spoken words into written text in real-time. You can use a library like SpeechRecognition to recognize speech in real time in Python. It supports several various engines and APIs, such as Microsoft Bing Voice Recognition and Google Speech Recognition. 


Real-time voice recognition in Python has a wide range of uses, including: 

  • Voice-controlled assistants: These virtual assistants, like Siri or Alexa, can be operated via voice commands. 
  • Speech-to-text transcription: This tool turns audible words into written text and is useful in professions including journalism, law, and medicine. 
  • Voice biometrics: This application uses a person's distinctive voice patterns to authenticate and identify them. 
  • Real-time language translation: This program helps people who speak various languages communicate more easily by translating spoken words from one language to another. 
  • Speech-based accessibility: Applications that assist people with disabilities, such as text-to-speech or speech-to-text for the visually impaired. 


Here is how you can recognize speech in real-time in Python: 

Fig 1: Preview of the output that you will get on running this code from your IDE


Code


In this solution, we use the Recognizer function of the Speech Recognition library

  1. Copy the code using the "Copy" button above, and paste it in a Python file in your IDE.
  2. Run the file. You will be prompted to speak something through your microphone
  3. The speech in real-time gets processed and displayed on screen


I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.


I found this code snippet by searching for "speech recognition in python" in kandi. You can try any such use case!


Dependent Libraries

If you do not have Speech Recognition that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the Speech Recognition page in kandi.


You can search for any dependent library on kandi like SpeechRecognition.

Environment Tested


I tested this solution in the following versions. Be mindful of changes when working with other versions.

  1. The solution is created in Python3.9.
  2. The solution is tested on SpeechRecognition 3.8.1 and PyAudio 0.2.12 versions.


Using this solution, we are able to make blurred images using the OpenCV library in Python with simple steps. This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us to recognize speech in real-time in Python.

Support


  1. For any support on kandi solution kits, please use the chat
  2. For further learning resources, visit the Open Weaver Community learning page.


AI has been to build intelligent agents that can understand the vision and language inputs and communicate with humans through natural language. 


Vision and language, two of the most fundamental methods for humans to perceive the world, are also two key cornerstones of AI. A longstanding goal of AI has been to build intelligent agents that can understand the world through vision and language inputs, and communicate with humans through natural language.


In order to achieve this goal, vision-language pre-training has emerged as an effective approach, where deep neural network models are pre-trained on large scale image-text datasets to improve performance on downstream vision-language tasks, such as image-text retrieval, image captioning, and visual question answering.


Image Captioning and Visual Question and Answering involves the usage of Large Multimodal Models (LMMs). Multimodal Learning seeks to allow computers to represent real-world objects and concepts using multiple data streams. We make use of one such model - Saleforce's BLIP (Bootstrapping Language-Image Pre-training)

Libraries used in this solution


Development Environment


VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.


Jupyter Notebook is used for our development.

Machine Learning


Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning

Kit Solution Source


App User Interface

Support


For any support, you can reach us at OpenWeaver Community Support

kandi 1-Click Install


Image Restoration is the process of converting a noisy/blur/low quality image to a better-quality image in terms of resolution, color, noise, and blur.


Image Restoration techniques involve Deblurring, colorization, super resolution enhancement and other popular techniques as well.


This kit provides the solution for

  • Restoring old images to colored images
  • Upscale and enhance image resolution


The Image colorization is done by a technique called NoGAN from an open source project called "deoldify". Deoldify is a shading rendering model for images and videos that enables the restoration of native color to black and white photos and videos.


The Image Upscaling and enhancement is done through a technique called GFPGAN which uses Generative Adversarial Networks comprising of Deep learning techniques for Face/Image restoration.

Libraries used in this solution

Development Environment


VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.


Jupyter Notebook is used for our development.

Machine Learning


Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning

App User Interface

Kit Solution Source


Support


For any support, you can reach us at OpenWeaver Community Support

kandi 1-Click Install


Emotion Detection and Recognition is related to Sentiment Analysis. Sentiment Analysis aims to detect positive, neutral, or negative feelings from text. 


Emotion Analysis aims to detect and recognize types of feelings through the expression of texts, such as joy, anger, fear, sadness.


In this kit, we build an AI based Speech Emotion Detector using open-source libraries. The concepts covered in the kit are:

 

  • Voice-to-text transcription- The speech can be captured in real-time through the microphone or by uploading an audio file. It is then converted to text using state-of-the-art Opensource AI models from OpenAI Whisper library.
  • Emotion detection- Emotion detection on the transcribed text is carried out using a finetuned XLM-RoBERTa model.


Whisper is a general-purpose speech recognition model released by OpenAI that can perform multilingual speech recognition as well as speech translation and language identification. Combined with an emotion detection model, this allows for detecting emotion directly from speech in multiple languages.


XLM-RoBERTa is a multilingual version of RoBERTa. It is pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It can be finetuned to perform any specific task such as emotion classification, text completion etc. Combining these, the emotion detection model could be used to transcribe and detect different emotions to enable a data-driven analysis.

Libraries used in this solution

Development Environment


VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.


Jupyter Notebook is used for our development.

Machine Learning


Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning

Kit Solution Source


APP Interface

Support


For any support, you can reach us at OpenWeaver Community Support

kandi 1-Click Install


Generative artificial intelligence (AI) describes algorithms that help in creating/generating new content, including audio, code, images, text and videos.


In this kit, we build a Music generator from Text and Audio prompts using the Meta's MusicGen model. Meta's Audiocraft research team has released MusicGen, an open source deep learning language model that can generate new music based on text prompts and even be aligned to an existing song.


MusicGen is a single stage auto-regressive Transformer model capable of generating high-quality music samples conditioned on text descriptions or audio prompts. The text descriptions are passed through a frozen text encoder model to obtain a sequence of hidden-state representations. MusicGen is then trained to predict discrete audio tokens, or audio codes, conditioned on these hidden states. These audio tokens are then decoded using an audio compression model, such as EnCodec, to recover the audio waveform.


MusicGen has been trained on 20000 hours of licensed music. MusicGen is exceptional in its capacity to handle both text and musical cues, in addition to the effectiveness of the design and the speed of creation. The text establishes the fundamental style, which the audio file’s music subsequently follows.

Libraries used in this solution


Development Environment


VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.


Jupyter Notebook is used for our development.

Artificial Intelligence


AI libraries and frameworks here are helpful in providing state-of-the-art solutions using AI

Kit Solution Source

UI app Integration

Support


For any support, you can reach us at OpenWeaver Community Support

kandi 1-Click Install

The Generative AI Kandi Kit for Image Generation is an exciting and innovative toolkit that enables users to explore the fascinating field of Generative Artificial Intelligence (AI) and unleash their creativity through the generation of unique and diverse images. This kit harnesses the power of open-source libraries, such as PyTorch and TorchVision, to create a fully functional Generative Adversarial Network (GAN) for generating high-quality images.


With this Kandi Kit, users can delve into the world of AI-driven image generation and witness the magic of AI creating realistic and novel images. The kit provides a user-friendly and customizable script that allows users to specify various hyperparameters, including batch size, number of epochs, latent dimension, and image size, providing full control over the image generation process.


The Kandi Kit comes with pre-defined Generator and Discriminator models, built using PyTorch's neural network module, and optimized using the Adam optimizer for efficient training. The Generator network cleverly generates images from random noise, while the Discriminator network efficiently distinguishes between real and fake images.


Additionally, users can leverage their own image datasets by specifying the path to the image folder, enabling them to train the GAN on custom datasets, leading to the creation of images tailored to their specific requirements.


The training loop provided in the script ensures the GAN iteratively learns to produce increasingly realistic and diverse images over a specified number of epochs. As training progresses, the Generator learns to create images that become almost indistinguishable from real images, making the process of generating images a truly magical and awe-inspiring experience.


The Kandi Kit also allows users to visualize the progress of image generation, with images being saved periodically during training. This feature enables users to observe the gradual improvement of the GAN over time and generate impressive images at different stages of the training process.


Overall, the Generative AI Kandi Kit for Image Generation offers an accessible and enjoyable way to explore the potential of AI in creating unique and visually captivating images. Whether for artistic endeavors, data augmentation, or creating realistic synthetic data, this kit empowers users to unlock the endless possibilities of Generative AI for image generation.

Screenshots


Test run



With Gradio GUI


kandi 1-Click Install


You can build predictive analytic based applications with this ready to deploy template application. Fully modifiable source code modifies your needs. 


Use this kandi 1-Click Solution kit to build your own AI-based Breast Cancer Detection Engine in minutes.


✅ Using this application you can do early stage detection for breast cancer and help in identifying it as malignant(cancerous) or benign(non-cancerous).

✅ You can build predictive analytic based applications with this ready to deploy template application.

✅ Fully modifiable source code is provided to enable you to modify for your requirements.

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.


Jupyter Notebook is used for our development.

Machine Learning

Simple and efficient tools for predictive data analysis.


Scikit-learn is a free software machine learning library which features various classification, regression and clustering algorithms including support-vector machines,etc. Similar libraries for ML support in Java, Scala and R programming language

Support

If you need help using this kit, you may reach us at the OpenWeaver Community.

kandi 1-Click Install

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QUESTION

Space Complexity in Breadth First Search (BFS) Algorithm

Asked 2022-Apr-11 at 08:08

According to Artificial Intelligence A Modern Approach - Stuart J. Russell , Peter Norvig (Version 4), space complexity of BFS is O(b^d), where 'b' is branching factor and 'd' is depth.

Complexity of BFS is obtained by this assumption: we store all nodes till we arrive to target node, in other word: 1 + b + b^2 + b^3 + ... + b^d => O(b^d)

But why should we store all nodes? don't we use queue for implementation?

If we use queue, don't need to store all nodes, because we enqueue and dequeue some nodes in steps, then when we find target node(s), we can say some nodes are in queue (but not all of them).

Is my understanding wrong?

ANSWER

Answered 2022-Apr-10 at 06:16

At any moment while we apply BFS, the queue would have at most two levels of nodes, for example if we just started searching in depth d, then the queue now contains all nodes at depth d and as we proceed the queue would finish all nodes at depth d and have all nodes at depth d+1, so at any moment we have O(b^d) space.

Also 1+b+b^2+...+b^d = (b^(d+1)-1)/(b-1).

Source https://stackoverflow.com/questions/71814173

QUESTION

Process fast api multi-user

Asked 2022-Mar-28 at 02:20

I'm studying the process of distributing artificial intelligence modules through fastapi.

I'm going to take a load test

I created an api that answers questions through fastapi using a pre-learned model.

In this case, it is not a problem for one user to use it, but when multiple users use it at the same time, the response may be too slow.

So when multiple users enter a question, is there any way to copy the model and put it in at once?

1
2class sentencebert_ai():
3    def __init__(self) -> None:
4        super().__init__()
5
6 def ask_query(self,query, topN):
7        startt = time.time()
8
9        ask_result = []
10        score = []
11        result_value = []  
12        embedder = torch.load(model_path)
13        corpus_embeddings = embedder.encode(corpus, convert_to_tensor=True)
14        query_embedding = embedder.encode(query, convert_to_tensor=True)
15        cos_scores = util.pytorch_cos_sim(query_embedding, corpus_embeddings)[0] #torch.Size([121])121개의 말뭉치에 대한 코사인 유사도 값이다.
16        cos_scores = cos_scores.cpu()
17
18        top_results = np.argpartition(-cos_scores, range(topN))[0:topN]
19
20        for idx in top_results[0:topN]:        
21            ask_result.append(corpusid[idx].item())
22            #.item()으로 접근하는 이유는 tensor(5)에서 해당 숫자에 접근하기 위한 방식이다.
23            score.append(round(cos_scores[idx].item(),3))
24
25        #서버에 json array 형태로 내보내기 위한 작업
26        for i,e in zip(ask_result,score):
27            result_value.append({"pred_id":i,"pred_weight":e})
28        endd = time.time()
29        print('시간체크',endd-startt)
30        return result_value
31        # return ','.join(str(e) for e in ask_result),','.join(str(e) for e in score)
32
33
34
35class Item_inference(BaseModel):
36    text : str
37    topN : Optional[int] = 1
38
39@app.post("/retrieval", tags=["knowledge recommendation"])
40async def Knowledge_recommendation(item: Item_inference):
41  
42    # db.append(item.dict())
43    item.dict()
44    results = _ai.ask_query(item.text, item.topN)
45
46    return results
47
48
49if __name__ == "__main__":
50    parser = argparse.ArgumentParser()
51    parser.add_argument("--port", default='9003', type=int)
52    # parser.add_argument("--mode", default='cpu', type=str, help='cpu for CPU mode, gpu for GPU mode')
53    args = parser.parse_args()
54
55    _ai = sentencebert_ai()
56    uvicorn.run(app, host="0.0.0.0", port=args.port,workers=4)
57

corrected version

1
2class sentencebert_ai():
3    def __init__(self) -> None:
4        super().__init__()
5
6 def ask_query(self,query, topN):
7        startt = time.time()
8
9        ask_result = []
10        score = []
11        result_value = []  
12        embedder = torch.load(model_path)
13        corpus_embeddings = embedder.encode(corpus, convert_to_tensor=True)
14        query_embedding = embedder.encode(query, convert_to_tensor=True)
15        cos_scores = util.pytorch_cos_sim(query_embedding, corpus_embeddings)[0] #torch.Size([121])121개의 말뭉치에 대한 코사인 유사도 값이다.
16        cos_scores = cos_scores.cpu()
17
18        top_results = np.argpartition(-cos_scores, range(topN))[0:topN]
19
20        for idx in top_results[0:topN]:        
21            ask_result.append(corpusid[idx].item())
22            #.item()으로 접근하는 이유는 tensor(5)에서 해당 숫자에 접근하기 위한 방식이다.
23            score.append(round(cos_scores[idx].item(),3))
24
25        #서버에 json array 형태로 내보내기 위한 작업
26        for i,e in zip(ask_result,score):
27            result_value.append({"pred_id":i,"pred_weight":e})
28        endd = time.time()
29        print('시간체크',endd-startt)
30        return result_value
31        # return ','.join(str(e) for e in ask_result),','.join(str(e) for e in score)
32
33
34
35class Item_inference(BaseModel):
36    text : str
37    topN : Optional[int] = 1
38
39@app.post("/retrieval", tags=["knowledge recommendation"])
40async def Knowledge_recommendation(item: Item_inference):
41  
42    # db.append(item.dict())
43    item.dict()
44    results = _ai.ask_query(item.text, item.topN)
45
46    return results
47
48
49if __name__ == "__main__":
50    parser = argparse.ArgumentParser()
51    parser.add_argument("--port", default='9003', type=int)
52    # parser.add_argument("--mode", default='cpu', type=str, help='cpu for CPU mode, gpu for GPU mode')
53    args = parser.parse_args()
54
55    _ai = sentencebert_ai()
56    uvicorn.run(app, host="0.0.0.0", port=args.port,workers=4)
57@app.post("/aaa") def your_endpoint(request: Request, item:Item_inference): start = time.time() model = request.app.state.model item.dict() #커널 실행시 필요 _ai = sentencebert_ai() results = _ai.ask_query(item.text, item.topN,model) end = time.time() print(end-start) return results ``` 
58

ANSWER

Answered 2022-Mar-25 at 09:09

Firstly, you should not load your model every time a request arrives, but rahter have it loaded once at startup (you could use the startup event for this) and store it on the app instance, which you can later retrieve, as described here and here. For instance:

1
2class sentencebert_ai():
3    def __init__(self) -> None:
4        super().__init__()
5
6 def ask_query(self,query, topN):
7        startt = time.time()
8
9        ask_result = []
10        score = []
11        result_value = []  
12        embedder = torch.load(model_path)
13        corpus_embeddings = embedder.encode(corpus, convert_to_tensor=True)
14        query_embedding = embedder.encode(query, convert_to_tensor=True)
15        cos_scores = util.pytorch_cos_sim(query_embedding, corpus_embeddings)[0] #torch.Size([121])121개의 말뭉치에 대한 코사인 유사도 값이다.
16        cos_scores = cos_scores.cpu()
17
18        top_results = np.argpartition(-cos_scores, range(topN))[0:topN]
19
20        for idx in top_results[0:topN]:        
21            ask_result.append(corpusid[idx].item())
22            #.item()으로 접근하는 이유는 tensor(5)에서 해당 숫자에 접근하기 위한 방식이다.
23            score.append(round(cos_scores[idx].item(),3))
24
25        #서버에 json array 형태로 내보내기 위한 작업
26        for i,e in zip(ask_result,score):
27            result_value.append({"pred_id":i,"pred_weight":e})
28        endd = time.time()
29        print('시간체크',endd-startt)
30        return result_value
31        # return ','.join(str(e) for e in ask_result),','.join(str(e) for e in score)
32
33
34
35class Item_inference(BaseModel):
36    text : str
37    topN : Optional[int] = 1
38
39@app.post("/retrieval", tags=["knowledge recommendation"])
40async def Knowledge_recommendation(item: Item_inference):
41  
42    # db.append(item.dict())
43    item.dict()
44    results = _ai.ask_query(item.text, item.topN)
45
46    return results
47
48
49if __name__ == "__main__":
50    parser = argparse.ArgumentParser()
51    parser.add_argument("--port", default='9003', type=int)
52    # parser.add_argument("--mode", default='cpu', type=str, help='cpu for CPU mode, gpu for GPU mode')
53    args = parser.parse_args()
54
55    _ai = sentencebert_ai()
56    uvicorn.run(app, host="0.0.0.0", port=args.port,workers=4)
57@app.post("/aaa") def your_endpoint(request: Request, item:Item_inference): start = time.time() model = request.app.state.model item.dict() #커널 실행시 필요 _ai = sentencebert_ai() results = _ai.ask_query(item.text, item.topN,model) end = time.time() print(end-start) return results ``` 
58@app.on_event("startup")
59async def startup_event():
60    app.state.model = torch.load(model_path)
61
62from fastapi import Request
63
64@app.post("/")
65def your_endpoint(request: Request):
66        model = request.app.state.model
67        # then pass it to your ask_query function
68

Secondly, if you do not have to await for coroutines in your route, then you should rather define your route with def instead of async def. In this way, FastAPI will process the requests concurrently (each will run in a separate thread), whereas async def routes run on the main thread, i.e., the server processes the requests sequentially (as long as there is no await call to I/O-bound operations inside such routes). Please have a look at the answers here and here, as well as all the references included in them, to understand the concept of async/await, and the difference between using def and async def.

Source https://stackoverflow.com/questions/71613305

QUESTION

I'm having a problem with lists in my basic quiz software

Asked 2022-Mar-11 at 01:38

I am running the code block written below:

1class Question:
2    
3    def __init__(self,text,choices,answer):
4        self.text = text
5        self.choices = choices
6        self.answer = answer
7        
8    def checkAnswer(self, answer):
9        return self.answer == answer
10 class Quiz:
11    
12    def __init__(self, questions):
13        self.questions = questions
14        self.score = 0
15        self.questionsIndex = 0
16        
17    def getQuestion(self):
18        return self.questions[self.questionsIndex]
19    
20    def displayQuestion(self):
21        question = self.getQuestion()
22        print(f"Question: {self.questionsIndex +1}: {question.text}")   
23        for q in question.choices:
24            print("-"+ q)
25        answer = input("Your Answer:  ")
26        self.guess(answer)
27        self.loadQuestion()
28        
29    def guess(self, answer):
30        question = self.getQuestion()
31        if question.checkAnswer(answer):
32            self.score += 1
33        self.questionsIndex += 1
34        self.displayQuestion()
35        
36    def loadQuestion(self):
37        if len(self.questions) == self.questionsIndex:
38            self.showScore()
39        else:
40            self.displayProgress()
41            self.displayQuestion()
42            
43    def showScore(self):
44        print("Score: ", self.score)
45        
46    def displayProgress(self):
47        totalQuestion = len(self.questions)
48        questionNumber = self.questionsIndex + 1
49        if questionNumber > totalQuestion:
50            print("Quiz Finished")
51        else:
52            print(f"*************************Question {questionNumber} of {totalQuestion}***********************************")
53           
54
55q1 = Question("Which programming language is the most profitable?["C#","Python","Java","HTML"],"Python")
56q2 = Question("Which is the easiest programming language?", ["C#","Python","Java","HTML"],"Python")
57q3 = Question("What is the most popular programming language?", ["C#","Python","Java","HTML"],"Python")
58questions = [q1,q2,q3]
59quiz = Quiz(questions)
60quiz.loadQuestion()
61

And I am facing the following problem:

1class Question:
2    
3    def __init__(self,text,choices,answer):
4        self.text = text
5        self.choices = choices
6        self.answer = answer
7        
8    def checkAnswer(self, answer):
9        return self.answer == answer
10 class Quiz:
11    
12    def __init__(self, questions):
13        self.questions = questions
14        self.score = 0
15        self.questionsIndex = 0
16        
17    def getQuestion(self):
18        return self.questions[self.questionsIndex]
19    
20    def displayQuestion(self):
21        question = self.getQuestion()
22        print(f"Question: {self.questionsIndex +1}: {question.text}")   
23        for q in question.choices:
24            print("-"+ q)
25        answer = input("Your Answer:  ")
26        self.guess(answer)
27        self.loadQuestion()
28        
29    def guess(self, answer):
30        question = self.getQuestion()
31        if question.checkAnswer(answer):
32            self.score += 1
33        self.questionsIndex += 1
34        self.displayQuestion()
35        
36    def loadQuestion(self):
37        if len(self.questions) == self.questionsIndex:
38            self.showScore()
39        else:
40            self.displayProgress()
41            self.displayQuestion()
42            
43    def showScore(self):
44        print("Score: ", self.score)
45        
46    def displayProgress(self):
47        totalQuestion = len(self.questions)
48        questionNumber = self.questionsIndex + 1
49        if questionNumber > totalQuestion:
50            print("Quiz Finished")
51        else:
52            print(f"*************************Question {questionNumber} of {totalQuestion}***********************************")
53           
54
55q1 = Question("Which programming language is the most profitable?["C#","Python","Java","HTML"],"Python")
56q2 = Question("Which is the easiest programming language?", ["C#","Python","Java","HTML"],"Python")
57q3 = Question("What is the most popular programming language?", ["C#","Python","Java","HTML"],"Python")
58questions = [q1,q2,q3]
59quiz = Quiz(questions)
60quiz.loadQuestion()
61runfile('C:/Users/Onur/Desktop/Artificial Intelligence A-Z/sorularclass.py', wdir='C:/Users/Onur/Desktop/Artificial Intelligence A-Z')
62*************************Question 1 of 3***********************************
63Question: 1: Which programming language is the most profitable?
64-C#
65-Python
66-Java
67-HTML
68 Your Answer:  a
69Question: 2: Which is the easiest programming language?
70-C#
71-Python
72-Java
73-HTML
74Your Answer:  a
75Question: 3: What is the most popular programming language?
76-C#
77-Python
78-Java
79-HTML
80Your Answer:  a
81Traceback (most recent call last):
82File "C:\Users\Onur\Desktop\Artificial Intelligence A-Z\sorularclass.py", line 63, in <module>
83    quiz.loadQuestion()
84File "C:\Users\Onur\Desktop\Artificial Intelligence A-Z\sorularclass.py", line 44, in loadQuestion
85    self.displayQuestion()
86File "C:\Users\Onur\Desktop\Artificial Intelligence A-Z\sorularclass.py", line 29, in displayQuestion
87    self.guess(answer)
88File "C:\Users\Onur\Desktop\Artificial Intelligence A-Z\sorularclass.py", line 37, in guess
89    self.displayQuestion()
90File "C:\Users\Onur\Desktop\Artificial Intelligence A-Z\sorularclass.py", line 29, in displayQuestion
91    self.guess(answer)
92File "C:\Users\Onur\Desktop\Artificial Intelligence A-Z\sorularclass.py", line 37, in guess
93    self.displayQuestion()
94File "C:\Users\Onur\Desktop\Artificial Intelligence A-Z\sorularclass.py", line 29, in displayQuestion
95    self.guess(answer)
96File "C:\Users\Onur\Desktop\Artificial Intelligence A-Z\sorularclass.py", line 37, in guess
97    self.displayQuestion()
98File "C:\Users\Onur\Desktop\Artificial Intelligence A-Z\sorularclass.py", line 24, in displayQuestion
99    question = self.getQuestion()
100File "C:\Users\Onur\Desktop\Artificial Intelligence A-Z\sorularclass.py", line 21, in getQuestion
101    return self.questions[self.questionsIndex]
102
103IndexError: list index out of range
104

Can you tell me the reason for this? Why is there a problem with lists? I'm adding this because stackoverflow wants me to add more details: I tried to build a quiz using basic class methods in this software, but I ran into a problem.

ANSWER

Answered 2022-Mar-11 at 01:38

In the displayQuestion method you call the guess method. In the guess method you increase the questionsIndex value, and call displayQuestion method again.

This process repeats and repeats infinitely until the questionIndex goes out of range. It seems that you need to remove calling the displayQuestion method from the guess method.

Source https://stackoverflow.com/questions/71431066

QUESTION

Discretize continuous target variable using sklearn

Asked 2022-Jan-30 at 23:08

I have to discretize into at least 5 bins a continuous target variable in order to lower the complexity of a classification model using the sklearn library

In order to do this, I've used the KBinsDiscretizer but I don't know how can I split in balanced parts the dataset now that I've discretized the target variable. This is my code:

1X = df.copy()
2y = X.pop('shares') 
3
4# scaling the dataset so all data in the same range
5scaler = preprocessing.MinMaxScaler()
6X = scaler.fit_transform(X)
7
8discretizer = preprocessing.KBinsDiscretizer(n_bins=5,  encode='ordinal', strategy='uniform')
9y_discretized = discretizer.fit_transform(y.values.reshape(-1, 1))
10
11# is this correct?
12X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, shuffle=True, stratify=y_discretized) 
13

For completeness, I'm trying to recreate a less complex model than the one showed in: [1] K. Fernandes, P. Vinagre and P. Cortez. A Proactive Intelligent Decision Support System for Predicting the Popularity of Online News. Proceedings of the 17th EPIA 2015 - Portuguese Conference on Artificial Intelligence, September, Coimbra, Portugal

ANSWER

Answered 2022-Jan-23 at 20:35

Your y_train and y_test are parts of y, which has (it seems) the original continuous values. So you're ending up fitting multiclass classification models, with probably lots of different classes, which likely causes the crashes.

I assume what you wanted is

1X = df.copy()
2y = X.pop('shares') 
3
4# scaling the dataset so all data in the same range
5scaler = preprocessing.MinMaxScaler()
6X = scaler.fit_transform(X)
7
8discretizer = preprocessing.KBinsDiscretizer(n_bins=5,  encode='ordinal', strategy='uniform')
9y_discretized = discretizer.fit_transform(y.values.reshape(-1, 1))
10
11# is this correct?
12X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, shuffle=True, stratify=y_discretized) 
13X_train, X_test, y_train, y_test = train_test_split(X, y_discretized, test_size=0.33, shuffle=True, stratify=y_discretized)
14

Whether discretizing a continuous target to turn a regression into a classification is a topic for another site, see e.g. https://datascience.stackexchange.com/q/90297/55122

Source https://stackoverflow.com/questions/70821138

QUESTION

Webpage starts zoomed out on mobile devices

Asked 2022-Jan-15 at 20:33

I have created a website for desktop and mobile, and it has to be responsive. My problem is that when I resize the browser all the content gets zoomed out instead of adapting. I also have an issue with the HTML. why is it only taking up 1/3 of the page according to dev tools and when I add width:1100px to my sections it renders the desktop version, but when I take it away it floats to the left side? Why is this happening?

Images of the problem:

1* {
2     margin: 0;
3     padding: 0;
4     box-sizing: border-box;
5}
6 body {
7     font-family: 'Source Sans Pro', sans-serif;
8     background-color: black;
9     color: white;
10     line-height: 30px;
11}
12 html {
13     width:100%;
14}
15 img {
16     width: 100%;
17}
18 h1 {
19     font-weight: 700;
20     font-size: 44px;
21     margin-bottom: 40px;
22     line-height: 50px;
23}
24 h3 {
25     width: 100%;
26}
27/* header */
28 header {
29     display: flex;
30     background-color: black;
31     height: 80px;
32     min-width: 1100px;
33     justify-content: right;
34     align-items: center;
35     margin-bottom: 50px;
36     border-bottom: 1px solid white;
37}
38 nav ul li {
39     display: inline-block;
40     list-style-type: none;
41     margin-right: 20px;
42}
43 .nav-links{
44     color: white;
45     font-size: 18px;
46}
47/* Banner */
48 .banner {
49     display: flex;
50     justify-content: space-around;
51     align-items: center;
52     min-height: 500px;
53     width: 100%;
54}
55 .banner-text-container {
56     max-width: 30%;
57     font-size: 22px;
58}
59 span {
60     color: #11cc9e;
61}
62 .consultation-link{
63     color: #11cc9e;
64     text-decoration: none;
65     margin-top: 30px;
66     font-weight: 900;
67     display: block;
68     border: 1px solid white;
69     max-width: 40%;
70     text-align: center;
71     padding: 5px;
72}
73 .consultation-link:hover{
74     background-color: #fff;
75}
76/* About */
77 .about {
78     display: flex;
79     justify-content: space-around;
80     align-items: center;
81     min-height: 600px;
82     min-width: 1100px;
83}
84 .about-text-container {
85     max-width: 40%;
86     font-size: 22px;
87     margin-left: 20px;
88}
89 .about-img{
90     width: 400px;
91     margin-right: 22px;
92}
93 .about-title {
94     margin-bottom: 40px;
95}
96 .about-us-link{
97     color: #11cc9e;
98     text-decoration: none;
99     margin-top: 30px;
100     font-weight: 900;
101     display: block;
102     border: 1px solid white;
103     text-align: center;
104     max-width: 25%;
105     padding: 5px;
106}
107 .about-us-link:hover{
108     background-color: #fff;
109}
110/* Join */
111 .join {
112     min-height: 600px;
113     min-width: 1100px;
114     max-width: 100%;
115}
116 .join-header{
117     width: 100%;
118     text-align: center;
119     margin-top: 150px;
120     font-size: 40px;
121}
122 .container-boxes{
123     position: relative;
124     top: 0;
125     bottom: 0;
126     display: flex;
127     flex-wrap: wrap;
128     justify-content: space-evenly;
129     align-items: center;
130     min-height: 500px;
131     min-width: 1100px;
132}
133 .box {
134     position: relative;
135     overflow: hidden;
136     transition: 0.5s;
137     height: 200px;
138     width: 300px;
139}
140 .box:hover{
141     z-index: 1;
142     transform: scale(1.25);
143     box-shadow: 0 25px 40px rgba(0, 0, 0, .5);
144     cursor: pointer;
145}
146 .box .imgBX{
147     position: absolute;
148     top: 0;
149     left: 0;
150     width: 100%;
151     height: 100%;
152}
153 .box .imgBX img{
154     position: absolute;
155     top: 0;
156     left: 0;
157     width: 100%;
158     height: 100%;
159     object-fit: cover;
160}
161 .box .imgBX:before{
162     content: '';
163     position: absolute;
164     top: 0;
165     left: 0;
166     width: 100%;
167     height: 100%;
168     z-index: 1;
169     background: linear-gradient(180deg,rgba(0,0,0.7),#79dbc3);
170     mix-blend-mode: multiply;
171     opacity: 0;
172     transition: 0.5s;
173}
174 .box:hover .imgBX:before {
175     opacity: 1;
176}
177 .box .imgBX img{
178     position: absolute;
179     top: 0;
180     left: 0;
181     width: 100%;
182     height: 100%;
183     object-fit: cover;
184}
185 .content{
186     display: flex;
187     flex-direction: column;
188     text-align: center;
189     position: absolute;
190     top: 20%;
191     bottom: 40%;
192     width: 100%;
193     height: 100%;
194     z-index: 1;
195     padding: 20px;
196     visibility: hidden;
197}
198 .box:hover .content{
199     visibility: visible;
200}
201/* Quote section */
202 .quote-section {
203     display: flex;
204     justify-content: center;
205     max-width: 100%;
206     min-height: 500px;
207     min-width: 1100px;
208}
209 .quote-container {
210     display: flex;
211     flex-direction: column;
212     flex-wrap: wrap;
213     align-items: center;
214     justify-items: center;
215     max-width: 50%;
216     font-size: 22px;
217     text-align: center;
218}
219 .quote {
220     line-height: 90px;
221     font-size: 150px;
222     font-style: italic;
223     color: #11cc9e;
224     text-indent: -37px;
225     font-weight: 600;
226     width: 37px;
227}
228 .quote-img{
229     width: 90px;
230     margin: 40px auto;
231}
232 .person-name{
233     color: #ccc;
234}
235 .person-role{
236     font-size: 17px;
237     color: #ccc;
238}
239/* Footer */
240 footer {
241     text-align: center;
242     margin-top: 100px;
243     padding-top: 50px;
244     max-width: 100%;
245     min-height: 200px;
246     min-width: 1100px;
247     border-top: 1px solid #fff;
248}
1* {
2     margin: 0;
3     padding: 0;
4     box-sizing: border-box;
5}
6 body {
7     font-family: 'Source Sans Pro', sans-serif;
8     background-color: black;
9     color: white;
10     line-height: 30px;
11}
12 html {
13     width:100%;
14}
15 img {
16     width: 100%;
17}
18 h1 {
19     font-weight: 700;
20     font-size: 44px;
21     margin-bottom: 40px;
22     line-height: 50px;
23}
24 h3 {
25     width: 100%;
26}
27/* header */
28 header {
29     display: flex;
30     background-color: black;
31     height: 80px;
32     min-width: 1100px;
33     justify-content: right;
34     align-items: center;
35     margin-bottom: 50px;
36     border-bottom: 1px solid white;
37}
38 nav ul li {
39     display: inline-block;
40     list-style-type: none;
41     margin-right: 20px;
42}
43 .nav-links{
44     color: white;
45     font-size: 18px;
46}
47/* Banner */
48 .banner {
49     display: flex;
50     justify-content: space-around;
51     align-items: center;
52     min-height: 500px;
53     width: 100%;
54}
55 .banner-text-container {
56     max-width: 30%;
57     font-size: 22px;
58}
59 span {
60     color: #11cc9e;
61}
62 .consultation-link{
63     color: #11cc9e;
64     text-decoration: none;
65     margin-top: 30px;
66     font-weight: 900;
67     display: block;
68     border: 1px solid white;
69     max-width: 40%;
70     text-align: center;
71     padding: 5px;
72}
73 .consultation-link:hover{
74     background-color: #fff;
75}
76/* About */
77 .about {
78     display: flex;
79     justify-content: space-around;
80     align-items: center;
81     min-height: 600px;
82     min-width: 1100px;
83}
84 .about-text-container {
85     max-width: 40%;
86     font-size: 22px;
87     margin-left: 20px;
88}
89 .about-img{
90     width: 400px;
91     margin-right: 22px;
92}
93 .about-title {
94     margin-bottom: 40px;
95}
96 .about-us-link{
97     color: #11cc9e;
98     text-decoration: none;
99     margin-top: 30px;
100     font-weight: 900;
101     display: block;
102     border: 1px solid white;
103     text-align: center;
104     max-width: 25%;
105     padding: 5px;
106}
107 .about-us-link:hover{
108     background-color: #fff;
109}
110/* Join */
111 .join {
112     min-height: 600px;
113     min-width: 1100px;
114     max-width: 100%;
115}
116 .join-header{
117     width: 100%;
118     text-align: center;
119     margin-top: 150px;
120     font-size: 40px;
121}
122 .container-boxes{
123     position: relative;
124     top: 0;
125     bottom: 0;
126     display: flex;
127     flex-wrap: wrap;
128     justify-content: space-evenly;
129     align-items: center;
130     min-height: 500px;
131     min-width: 1100px;
132}
133 .box {
134     position: relative;
135     overflow: hidden;
136     transition: 0.5s;
137     height: 200px;
138     width: 300px;
139}
140 .box:hover{
141     z-index: 1;
142     transform: scale(1.25);
143     box-shadow: 0 25px 40px rgba(0, 0, 0, .5);
144     cursor: pointer;
145}
146 .box .imgBX{
147     position: absolute;
148     top: 0;
149     left: 0;
150     width: 100%;
151     height: 100%;
152}
153 .box .imgBX img{
154     position: absolute;
155     top: 0;
156     left: 0;
157     width: 100%;
158     height: 100%;
159     object-fit: cover;
160}
161 .box .imgBX:before{
162     content: '';
163     position: absolute;
164     top: 0;
165     left: 0;
166     width: 100%;
167     height: 100%;
168     z-index: 1;
169     background: linear-gradient(180deg,rgba(0,0,0.7),#79dbc3);
170     mix-blend-mode: multiply;
171     opacity: 0;
172     transition: 0.5s;
173}
174 .box:hover .imgBX:before {
175     opacity: 1;
176}
177 .box .imgBX img{
178     position: absolute;
179     top: 0;
180     left: 0;
181     width: 100%;
182     height: 100%;
183     object-fit: cover;
184}
185 .content{
186     display: flex;
187     flex-direction: column;
188     text-align: center;
189     position: absolute;
190     top: 20%;
191     bottom: 40%;
192     width: 100%;
193     height: 100%;
194     z-index: 1;
195     padding: 20px;
196     visibility: hidden;
197}
198 .box:hover .content{
199     visibility: visible;
200}
201/* Quote section */
202 .quote-section {
203     display: flex;
204     justify-content: center;
205     max-width: 100%;
206     min-height: 500px;
207     min-width: 1100px;
208}
209 .quote-container {
210     display: flex;
211     flex-direction: column;
212     flex-wrap: wrap;
213     align-items: center;
214     justify-items: center;
215     max-width: 50%;
216     font-size: 22px;
217     text-align: center;
218}
219 .quote {
220     line-height: 90px;
221     font-size: 150px;
222     font-style: italic;
223     color: #11cc9e;
224     text-indent: -37px;
225     font-weight: 600;
226     width: 37px;
227}
228 .quote-img{
229     width: 90px;
230     margin: 40px auto;
231}
232 .person-name{
233     color: #ccc;
234}
235 .person-role{
236     font-size: 17px;
237     color: #ccc;
238}
239/* Footer */
240 footer {
241     text-align: center;
242     margin-top: 100px;
243     padding-top: 50px;
244     max-width: 100%;
245     min-height: 200px;
246     min-width: 1100px;
247     border-top: 1px solid #fff;
248}<!DOCTYPE html>
249<html lang="en">
250   <head>
251      <title>Codes</title>
252      <link rel="preconnect" href="https://fonts.googleapis.com">
253      <ink rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
254      <link href="https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@400;600&display=swap" rel="stylesheet">
255      <meta charset="UTF-8">
256      <meta http-equiv="X-UA-Compatible" content="IE=edge">
257      <meta name="viewport" content="width=device-width, initial-scale=1">
258      <link rel="stylesheet" href="./Resources/styles.css">
259   </head>
260   <body>
261      <header>
262         <!-- insert logo -->
263         <nav class="nav-links">
264            <ul>
265               <li>About</li>
266               <li>Peer group</li>
267               <li>Review</li>
268            </ul>
269         </nav>
270      </header>
271      <section class="banner">
272         <div class="banner-text-container">
273            <h1>Build. Grow. <span class="color-Learn">Learn.</span></h1>
274            <p>Unlock your potential with your peers!, using Blockchain, Fintech or the IT outsourcing company Boosty Labs helps you create an innovative end to end product or augment your team with the right experts.</p>
275            <a class="consultation-link" href="#">Free consultation </a>
276         </div>
277         <div class="banner-img">
278            <img src="./Resources/Images/banner.png" alt="">
279         </div>
280      </section>
281      <section class="about">
282         <div class="about-text-container">
283            <h2 class="about-title">Who we are</h2>
284            <p>Here you can find our ,collection of coding, data science and statistics tutorials with examples in R, Python, JavaScript and Python. As you click through, you'll notice that some tutorials have ribbons on their logos - they are part of our free and self-paced online course Data Science for Ecologists and Environmental Scientists! Yellow for the Stats from Scratch stream, blue for Wiz of Data Viz and purple for Mastering Modelling.</p>
285            <a class="about-us-link" href="#">More about us </a>
286         </div>
287         <div class="about-img">
288            <img src="./Resources/Images/whoweare.png" alt="">
289         </div>
290      </section>
291      <section class="join">
292         <h3 class="join-header" >Join a peer group!</h3>
293         <div class="container-boxes">
294            <div class="box">
295               <div class="imgBX"> 
296                  <img src="./Resources/Images/box-1.png" alt="">
297               </div>
298               <div class="content">
299                  <h3>AI</h3>
300                  <P>Discover The Complete Range Of Artificial Intelligence Solutions.</P>
301               </div>
302            </div>
303            <div class="box">
304               <div class="imgBX">
305                  <img src="./Resources/Images/box-2.png" alt="">
306               </div>
307               <div class="content">
308                  <h3 class="frontend-title">Frontend Dev</h3>
309                  <p>Discover The Complete Range Of Frontend Solutions.</p>
310               </div>
311            </div>
312            <div class="box">
313               <div class="imgBX">
314                  <img src="./Resources/Images/box-3.png" alt="">
315               </div>
316               <div class="content">
317                  <h3>Microsoft systems</h3>
318                  <p>Discover The Complete Range Of Microsoft Solutions.</p>
319               </div>
320            </div>
321         </div>
322      </section>
323      <section class="quote-section">
324         <div class="quote-container">
325            <div class="quote">"</div>
326            <p class="p-quote">In coded, the progress of the topics and the exercises are really good. It's so nice to practice on good story told tasks. Also if you are stuck, it is nice to have a broad range of coders around in the peer groups  that you can get the answers you are looking for.</p>
327            <div class="quote-img">
328               <img src="./Resources/Images/person-img.png" alt="">
329            </div>
330            <div class="person-name">Peter Gangland </div>
331            <div class="person-role">Director of business dev at <span>Microsoft</span></div>
332         </div>
333      </section>
334      <footer>
335         <div id="contact">
336            <h2>
337            Contact us</h5>
338            <h5>coded@peers.com</h5>
339            <h5>831-867-5309</h5>
340         </div>
341         <div id="copyright">
342            <h5>@copyright coded Enterprises 2022</h5>
343         </div>
344      </footer>
345   </body>
346</html>

ANSWER

Answered 2022-Jan-15 at 19:43

For making your website responsive you need to use media queries. It's like you tell the browser how to style your website in different sizes. I think your problem with your sections might also get solved if you try to make your website responsive.

Source https://stackoverflow.com/questions/70724640

QUESTION

Pyttsx3 not working, process finished with exit code 0

Asked 2021-Dec-30 at 01:15

I am making an Artificial Intelligence (AI) assistant and I wrote this to make it speak:

1engine = pyttsx3.init('sapi5')
2voices = engine.getProperty('voices')
3engine.setProperty('voices', voices[0].id)
4
5
6def speak(audio):
7    engine.say(audio)
8    print(audio)
9    engine.runAndWait()
10

it does not speak and shows:

1engine = pyttsx3.init('sapi5')
2voices = engine.getProperty('voices')
3engine.setProperty('voices', voices[0].id)
4
5
6def speak(audio):
7    engine.say(audio)
8    print(audio)
9    engine.runAndWait()
10Process finished with exit code 0
11

how to fix it??

ANSWER

Answered 2021-Dec-30 at 01:15

You forgot to use the function. Use this code:

1engine = pyttsx3.init('sapi5')
2voices = engine.getProperty('voices')
3engine.setProperty('voices', voices[0].id)
4
5
6def speak(audio):
7    engine.say(audio)
8    print(audio)
9    engine.runAndWait()
10Process finished with exit code 0
11engine = pyttsx3.init('sapi5')
12voices = engine.getProperty('voices')
13engine.setProperty('voices', voices[0].id)
14
15
16def speak(audio):
17    engine.say(audio)
18    print(audio)
19    engine.runAndWait()
20
21
22# what you are missing
23# use your function to say something
24speak('Hello')
25

Hopefully, it works!

Source https://stackoverflow.com/questions/70517848

QUESTION

Expandable input and output in neural network

Asked 2021-Dec-18 at 14:30

What architecture/methods are used to make a neural network which can get infinite big input and/or return infinite big output?

I have an idea how to make infinite big output. I just need extra input neurons and after the first calculation send output (or part of it) to input neurons.

But I have no clue how to make extensible input. Maybe use multiple iterations, and plug output to input, and change the rest of the input neurons accordingly to the next portion of input data?

Artificial intelligence is new for me, so it is possible that I'm asking something that I don't want or something impossible. Please provide simple answers.

ANSWER

Answered 2021-Dec-17 at 20:52

The short answer is any RNN is capable of consuming, and producing, arbitrary length sequences. Depending on the structure of the data CNNs, Graph Nets etc. can also work with arbitrarily large inputs.

Source https://stackoverflow.com/questions/70392415

QUESTION

How to group elements of loop in a single list index

Asked 2021-Dec-01 at 15:36

I have a for loop in python which extracts data using beautifulsoup from a website and appends them into a list. I am trying to scrape tags from event names ex: AI, Big Data, ML etc.

My code:

1import requests
2from bs4 import BeautifulSoup
3
4URL = "https://aiml.events/"
5page = requests.get(URL)
6soup = BeautifulSoup(page.content, 'lxml')
7
8# Scrape Event Tags
9event_tags_list = []
10event_tag_div = soup.find_all('div', class_ = 'card-body')
11for event_div in event_tag_div:
12  event_span = event_div.find_all('span', class_  = 'badge badge-light badge-pill')
13  for event_tags in event_span:
14    print(event_tags.text)
15     
16

Tags I want to fetch

I am able to fetch the tags but they are all independent. I want to be able to group them together. Currently my list is like this:

1import requests
2from bs4 import BeautifulSoup
3
4URL = "https://aiml.events/"
5page = requests.get(URL)
6soup = BeautifulSoup(page.content, 'lxml')
7
8# Scrape Event Tags
9event_tags_list = []
10event_tag_div = soup.find_all('div', class_ = 'card-body')
11for event_div in event_tag_div:
12  event_span = event_div.find_all('span', class_  = 'badge badge-light badge-pill')
13  for event_tags in event_span:
14    print(event_tags.text)
15     
16tag_list = ['Artificial Intelligence', 'Artificial Intelligence','Machine Learning', 'Healthcare', 'Artificial Intelligence','Public Sector' ] 
17

My expectation:

1import requests
2from bs4 import BeautifulSoup
3
4URL = "https://aiml.events/"
5page = requests.get(URL)
6soup = BeautifulSoup(page.content, 'lxml')
7
8# Scrape Event Tags
9event_tags_list = []
10event_tag_div = soup.find_all('div', class_ = 'card-body')
11for event_div in event_tag_div:
12  event_span = event_div.find_all('span', class_  = 'badge badge-light badge-pill')
13  for event_tags in event_span:
14    print(event_tags.text)
15     
16tag_list = ['Artificial Intelligence', 'Artificial Intelligence','Machine Learning', 'Healthcare', 'Artificial Intelligence','Public Sector' ] 
17tag_list = ['Artificial Intelligence', 'Artificial Intelligence,Machine Learning, Healthcare', 'Artificial Intelligence,Public Sector' ] 
18

Any help is appreciated. Sorry if the question is too basic.

ANSWER

Answered 2021-Aug-30 at 15:45

Replace the inner loop with a generator that you join into a string.

1import requests
2from bs4 import BeautifulSoup
3
4URL = "https://aiml.events/"
5page = requests.get(URL)
6soup = BeautifulSoup(page.content, 'lxml')
7
8# Scrape Event Tags
9event_tags_list = []
10event_tag_div = soup.find_all('div', class_ = 'card-body')
11for event_div in event_tag_div:
12  event_span = event_div.find_all('span', class_  = 'badge badge-light badge-pill')
13  for event_tags in event_span:
14    print(event_tags.text)
15     
16tag_list = ['Artificial Intelligence', 'Artificial Intelligence','Machine Learning', 'Healthcare', 'Artificial Intelligence','Public Sector' ] 
17tag_list = ['Artificial Intelligence', 'Artificial Intelligence,Machine Learning, Healthcare', 'Artificial Intelligence,Public Sector' ] 
18for event_div in event_tag_div:
19    event_span = event_div.find_all('span', class_  = 'badge badge-light badge-pill')
20    event_tag_list.append(','.join(event_tag.text for event_tag in event_span))
21

Source https://stackoverflow.com/questions/68986779

QUESTION

Render image with json data | ReactJs

Asked 2021-Nov-19 at 18:00

So I'm trying to make the addition of project easier for me with a json data.

Basically I'm creating blocks of projects and each project comes with an image, however even when the id == to the name I gave the image, the image does not render. Is there is any option for that or should I just give up on json files ?


The reactjs code

1import Pdata from "../../api/projects.json";
2import p1 from "../../img/Project/PoleAnglais.png";
3import p2 from "../../img/Project/I-Art.png";
4import p3 from "../../img/Project/Hestia.png";
5import p4 from "../../img/Project/EvlV1.png";
6import p5 from "../../img/Project/Kelly.png";
7import p6 from "../../img/Project/EthLnyV2.png";
8import { Component } from "react";
9class Plist extends Component {
10  render() {
11    return (
12      <div
13        className="project-list"
14        data-aos="fade-right"
15        data-aos-duration="1200"
16      >
17        {Pdata.map((projectDetail, index) => {
18          return (
19            <div className="project-block">
20              <h2 className="project-title">{projectDetail.title}</h2>
21              <p className="date">{projectDetail.date}</p>
22              <p className="project-desc">{projectDetail.desc}</p>
23              <img src={projectDetail.id} alt="" />
24              <p className="madewith">made with {projectDetail.tags}</p>
25            </div>
26          );
27        })}
28      </div>
29    );
30  }
31}
32export default Plist;
33

The json data

1import Pdata from "../../api/projects.json";
2import p1 from "../../img/Project/PoleAnglais.png";
3import p2 from "../../img/Project/I-Art.png";
4import p3 from "../../img/Project/Hestia.png";
5import p4 from "../../img/Project/EvlV1.png";
6import p5 from "../../img/Project/Kelly.png";
7import p6 from "../../img/Project/EthLnyV2.png";
8import { Component } from "react";
9class Plist extends Component {
10  render() {
11    return (
12      <div
13        className="project-list"
14        data-aos="fade-right"
15        data-aos-duration="1200"
16      >
17        {Pdata.map((projectDetail, index) => {
18          return (
19            <div className="project-block">
20              <h2 className="project-title">{projectDetail.title}</h2>
21              <p className="date">{projectDetail.date}</p>
22              <p className="project-desc">{projectDetail.desc}</p>
23              <img src={projectDetail.id} alt="" />
24              <p className="madewith">made with {projectDetail.tags}</p>
25            </div>
26          );
27        })}
28      </div>
29    );
30  }
31}
32export default Plist;
33    [
34  {
35    "id": "p1",
36    "title": "Pole Anglais",
37    "date": "16/10/2019",
38    "desc": "This project was in association with Filip Zafirovski, my English teacher by the time who wanted students to get a source of inspiration by publishing articles and/or their work. It was my very first web project, and was kind of hard to pull off but I still enjoyed it.Since for the very first time i coded for a project and not myself.",
39    "tags": "Loads of crap"
40  },
41  {
42    "id": "p2",
43    "title": "Project I.Art",
44    "date": "3/07/2021",
45    "desc": "In France to go to college you have to get a diploma, which requires multiple exams to be validated. One of the subjects I had to do a presentation on was Art. I decided to create an idea around an Artificial Intelligence who would create art based on the likes and dislikes of the spectator. This panel is a website made for the occasion.",
46    "tags": "Html,Scss, & AOS librairie"
47  },
48  {
49    "id": "p3",
50    "title": "Hestia Real Estate",
51    "date": "18-26/10/2021",
52    "desc": "At the very start of my student life @hetic, They grouped student randomly to make a project. The subject of the project was to create an agency, a fake web-app and website that sells premium submarines to plus ultra rich people. For that project I designed the website of the agency, and the app for the complex.",
53    "tags": "Html & Scss"
54  },
55  {
56    "id": "p4",
57    "title": "EvL First Design",
58    "date": "30/10/2021",
59    "desc": "Before the design and dev of this portfolio, I had made a portfolio where I only putted my socials link. All of that because I had no idea of what to put on it. Even if I was satisfied with the first version it did not in any case represented the mood and emotion I wanted it to give. And so I gave birth to the actual design of the website on the 11/11/2021",
60    "tags": "Nextjs & Scss"
61  },
62  {
63    "id": "p5",
64    "title": "Kelly's Portfolio",
65    "date": "3/07/2021",
66    "desc": "Sometimes after arriving at my college, I met a freshly made friend who wanted to publish her portfolio. She knew how to design and do plenty others thing. To She didn't really like to code and was making her website with Wix. To which I proposed to remake her website by coding it myself.",
67    "tags": "VueJs & Scss"
68  },
69  {
70    "id": "p6",
71    "title": "EthLny V2",
72    "date": "11-12/11/2021",
73    "desc": "After doing the amazing portfolio of Kelly, I was kind of disappointed with my own. So I decided to remake a new design. Use a Random language, study the color psychology, searched a tagline. And TA-DA here it is, the website you're in right now is the result of 7 hours of researching, designing and coding and debugging.",
74    "tags": "ReactJs, Scss & AOS librairy"
75  }
76]
77

ANSWER

Answered 2021-Nov-19 at 17:53

I think the image is rendering but it is just too small to see

try adding width and height.

1import Pdata from "../../api/projects.json";
2import p1 from "../../img/Project/PoleAnglais.png";
3import p2 from "../../img/Project/I-Art.png";
4import p3 from "../../img/Project/Hestia.png";
5import p4 from "../../img/Project/EvlV1.png";
6import p5 from "../../img/Project/Kelly.png";
7import p6 from "../../img/Project/EthLnyV2.png";
8import { Component } from "react";
9class Plist extends Component {
10  render() {
11    return (
12      <div
13        className="project-list"
14        data-aos="fade-right"
15        data-aos-duration="1200"
16      >
17        {Pdata.map((projectDetail, index) => {
18          return (
19            <div className="project-block">
20              <h2 className="project-title">{projectDetail.title}</h2>
21              <p className="date">{projectDetail.date}</p>
22              <p className="project-desc">{projectDetail.desc}</p>
23              <img src={projectDetail.id} alt="" />
24              <p className="madewith">made with {projectDetail.tags}</p>
25            </div>
26          );
27        })}
28      </div>
29    );
30  }
31}
32export default Plist;
33    [
34  {
35    "id": "p1",
36    "title": "Pole Anglais",
37    "date": "16/10/2019",
38    "desc": "This project was in association with Filip Zafirovski, my English teacher by the time who wanted students to get a source of inspiration by publishing articles and/or their work. It was my very first web project, and was kind of hard to pull off but I still enjoyed it.Since for the very first time i coded for a project and not myself.",
39    "tags": "Loads of crap"
40  },
41  {
42    "id": "p2",
43    "title": "Project I.Art",
44    "date": "3/07/2021",
45    "desc": "In France to go to college you have to get a diploma, which requires multiple exams to be validated. One of the subjects I had to do a presentation on was Art. I decided to create an idea around an Artificial Intelligence who would create art based on the likes and dislikes of the spectator. This panel is a website made for the occasion.",
46    "tags": "Html,Scss, & AOS librairie"
47  },
48  {
49    "id": "p3",
50    "title": "Hestia Real Estate",
51    "date": "18-26/10/2021",
52    "desc": "At the very start of my student life @hetic, They grouped student randomly to make a project. The subject of the project was to create an agency, a fake web-app and website that sells premium submarines to plus ultra rich people. For that project I designed the website of the agency, and the app for the complex.",
53    "tags": "Html & Scss"
54  },
55  {
56    "id": "p4",
57    "title": "EvL First Design",
58    "date": "30/10/2021",
59    "desc": "Before the design and dev of this portfolio, I had made a portfolio where I only putted my socials link. All of that because I had no idea of what to put on it. Even if I was satisfied with the first version it did not in any case represented the mood and emotion I wanted it to give. And so I gave birth to the actual design of the website on the 11/11/2021",
60    "tags": "Nextjs & Scss"
61  },
62  {
63    "id": "p5",
64    "title": "Kelly's Portfolio",
65    "date": "3/07/2021",
66    "desc": "Sometimes after arriving at my college, I met a freshly made friend who wanted to publish her portfolio. She knew how to design and do plenty others thing. To She didn't really like to code and was making her website with Wix. To which I proposed to remake her website by coding it myself.",
67    "tags": "VueJs & Scss"
68  },
69  {
70    "id": "p6",
71    "title": "EthLny V2",
72    "date": "11-12/11/2021",
73    "desc": "After doing the amazing portfolio of Kelly, I was kind of disappointed with my own. So I decided to remake a new design. Use a Random language, study the color psychology, searched a tagline. And TA-DA here it is, the website you're in right now is the result of 7 hours of researching, designing and coding and debugging.",
74    "tags": "ReactJs, Scss & AOS librairy"
75  }
76]
77 <img style={{width: 200px, height: 200px}} src={projectDetail.id} alt="" />
78

Source https://stackoverflow.com/questions/70038925

QUESTION

Searching for a word/phrase in a string with all the possible approximations of the phrase

Asked 2021-Nov-18 at 17:53

Suppose I have the following string:

1string = 'machine learning ml is a type of artificial intelligence ai that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so machine12 learning algorithms use historical data as input to predict new output values machines learning is good'
2

Further suppose that I have a tag defined as:

1string = 'machine learning ml is a type of artificial intelligence ai that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so machine12 learning algorithms use historical data as input to predict new output values machines learning is good'
2tag = 'machine learning'
3

Now I wish to find the tag in my string. As you can see from my string there are three places that I have machine learning, one at the beginning of the string and one as machine12 learning and the last one as machines learning. I wish to find all of these and make an output list as

1string = 'machine learning ml is a type of artificial intelligence ai that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so machine12 learning algorithms use historical data as input to predict new output values machines learning is good'
2tag = 'machine learning'
3['machine learning', 'machine12 learning', 'machines learning']
4

To be able to do this I was tried to tokenize my tag using nltk. That is

1string = 'machine learning ml is a type of artificial intelligence ai that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so machine12 learning algorithms use historical data as input to predict new output values machines learning is good'
2tag = 'machine learning'
3['machine learning', 'machine12 learning', 'machines learning']
4tag_token = nltk.word_tokenize(tag)
5

I would then have ['machine','learning']. I would then search for tag[0].

I know that string.find(tag_token[0]) and data.rfind(tag_token[0]) would give the position of machine for the first and last finds, but what if I had more machine learning within the text (here we have 3)?

In that case I would not be able to extract them all. So my original idea to find all the occurrences of machine and then learning would have failed. I wished to use fuzzywuzzy to then analyze the ['machine learning', 'machine12 learning', 'machines learning'] with respect to the tag.

So my question is given then string I have, how can I search for the tag and its approximations and list them as follow?

1string = 'machine learning ml is a type of artificial intelligence ai that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so machine12 learning algorithms use historical data as input to predict new output values machines learning is good'
2tag = 'machine learning'
3['machine learning', 'machine12 learning', 'machines learning']
4tag_token = nltk.word_tokenize(tag)
5['machine learning', 'machine12 learning', 'machines learning']
6

Update: I now know that I can do the followings:

1string = 'machine learning ml is a type of artificial intelligence ai that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so machine12 learning algorithms use historical data as input to predict new output values machines learning is good'
2tag = 'machine learning'
3['machine learning', 'machine12 learning', 'machines learning']
4tag_token = nltk.word_tokenize(tag)
5['machine learning', 'machine12 learning', 'machines learning']
6pattern = re.compile(r"(machine[\s0-9]+learning)",re.IGNORECASE)
7matches = pattern.findall(data)
8#[output]: ['machine learning', 'machine12 learning']
9

also if I do

1string = 'machine learning ml is a type of artificial intelligence ai that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so machine12 learning algorithms use historical data as input to predict new output values machines learning is good'
2tag = 'machine learning'
3['machine learning', 'machine12 learning', 'machines learning']
4tag_token = nltk.word_tokenize(tag)
5['machine learning', 'machine12 learning', 'machines learning']
6pattern = re.compile(r"(machine[\s0-9]+learning)",re.IGNORECASE)
7matches = pattern.findall(data)
8#[output]: ['machine learning', 'machine12 learning']
9pattern = re.compile(r"(machine[\sA-Za-z]+learning)",re.IGNORECASE)
10matches = pattern.findall(data)
11#[output]: ['machine learning', 'machines learning']
12

But certainly, this is not a generalizable solution as it stands. So I wonder if there is a smart way to search in such scenarios?

ANSWER

Answered 2021-Nov-18 at 17:53

Maybe use pattern like this (string\w*)?

1string = 'machine learning ml is a type of artificial intelligence ai that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so machine12 learning algorithms use historical data as input to predict new output values machines learning is good'
2tag = 'machine learning'
3['machine learning', 'machine12 learning', 'machines learning']
4tag_token = nltk.word_tokenize(tag)
5['machine learning', 'machine12 learning', 'machines learning']
6pattern = re.compile(r"(machine[\s0-9]+learning)",re.IGNORECASE)
7matches = pattern.findall(data)
8#[output]: ['machine learning', 'machine12 learning']
9pattern = re.compile(r"(machine[\sA-Za-z]+learning)",re.IGNORECASE)
10matches = pattern.findall(data)
11#[output]: ['machine learning', 'machines learning']
12import re
13
14string = 'machine 12 learning ml is a type of artificial intelligence ai that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so machine12 learning algorithms use historical data as input to predict new output values machines learning is good'
15
16tag_token=['machine','learning']
17
18pattern='('+''.join(e+'\w*\s+(?:\S*\s+)?' for e in tag_token)[:-14]+')'
19
20rgx=re.compile(pattern,re.IGNORECASE)
21rgx.findall(string)
22#output
23#['machine 12 learning', 'machine12 learning', 'machines learning']
24

it will be more difficult to find matches with the changing position of words in the tag

and this code will find all combinations from tag_token. E.g. machine s learning and machine learning and machine12 12 learning and learning machine ... Also you can create new string and new tag_token that containing more than 2 words. All combinations of these words will be found.

Example tag_token = ['1', '2', '3'] will match 1 2 3 and 1a 2 b 3 and 2b2 1sss 3 and 333 2tt 1

1string = 'machine learning ml is a type of artificial intelligence ai that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so machine12 learning algorithms use historical data as input to predict new output values machines learning is good'
2tag = 'machine learning'
3['machine learning', 'machine12 learning', 'machines learning']
4tag_token = nltk.word_tokenize(tag)
5['machine learning', 'machine12 learning', 'machines learning']
6pattern = re.compile(r"(machine[\s0-9]+learning)",re.IGNORECASE)
7matches = pattern.findall(data)
8#[output]: ['machine learning', 'machine12 learning']
9pattern = re.compile(r"(machine[\sA-Za-z]+learning)",re.IGNORECASE)
10matches = pattern.findall(data)
11#[output]: ['machine learning', 'machines learning']
12import re
13
14string = 'machine 12 learning ml is a type of artificial intelligence ai that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so machine12 learning algorithms use historical data as input to predict new output values machines learning is good'
15
16tag_token=['machine','learning']
17
18pattern='('+''.join(e+'\w*\s+(?:\S*\s+)?' for e in tag_token)[:-14]+')'
19
20rgx=re.compile(pattern,re.IGNORECASE)
21rgx.findall(string)
22#output
23#['machine 12 learning', 'machine12 learning', 'machines learning']
24import re
25import itertools
26
27string = 'machine 12 learning ml is a type of artificial intelligence ai that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so machine12 learning algorithms use historical data as input to predict new output values machines learning is good. Learning machine can be used to train people. learning the machines is a great job'
28
29tag_token=['machine','learning']
30
31pattern='('
32for current_tag in itertools.permutations(tag_token, len(tag_token)):
33    pattern+=''.join(e+'\w*\s+(?:\S*\s+)?' for e in current_tag)[:-14]+'|'
34
35pattern=pattern.rstrip('|')+')'
36rgx=re.compile(pattern,re.IGNORECASE)
37
38rgx.findall(string)
39
40#output
41#['machine 12 learning', 'machine12 learning', 'machines learning', 'Learning machine', 'learning the machines']
42

Source https://stackoverflow.com/questions/69965741

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