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Trending Kits in Utilities

text-summarizer

Build Text Summarizer in Python

<img src="https://kandi.dev/owassets/text-summarizer-banner.png" alt="Text Summarizer Banner" style="height:auto;max-width:100%;"/> NLP text summarizer, is a Python package that summarizes texts and extracts the most important sentences from a given text. Text summarizer is commonly used in news feeding websites to summarize long articles. Summarizer shortens long texts such that the summarized text preserves all the essential points of the actual text. It uses spaCy, nltk, and NumPy to do the job. This solution is also used to summarize texts (in Extractive and abstractive techniques), extract key sentences and find their TF-IDF values. You can use this package for your own projects; we are sure you'll find it useful! Extraction-based summarization involves selecting sentences from an original document and organizing them into a cohesive summary. In contrast to extraction-based summarization, abstraction-based summaries are created by using algorithms to produce abstracts that can be used as templates. spaCy is a library for Natural Language Processing (NLP). It provides functions for tokenization, part of speech tagging, and parsing. The library also includes pre-trained models for some languages. NLTK (Natural Language Toolkit) is another popular toolkit for NLP tasks. It is used in many research papers to solve different problems related to NLP. There are several popular open-source libraries available for developers: <button class="MuiButtonBase-root MuiButton-root MuiButton-contained editexp MuiButton-containedSecondary" onclick="location.href='https://github.com/kandi1clickkits/bert-extractive-summarizer/raw/main/kit_installer.zip'" type="button"> ⬇️ Download 1-Click Installer </button>

kandi

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food-wastage-recommender-system

food Wastage Recommender system

Data Summary : Resources that have been shared for the problem statement has info about food items and their description. Also we had order info from both donor and from consumer side orders on daily basis. We have done data cleaning and preprocessing as required. Recommendation system : In order to control the food wastage we have built Recommendation engine using "item-item based collaborative filtering" to recommend the items which expire early and are more in consumption. Data Analysis : We have developed a dashboard on tableau using cleaned datasets and these analysis can be used to match supply-demand of different types of food and to give an overview to the NGO, donors and consumers on how to reduce the food wastage. Use the open source, cloud APIs, or public libraries listed below in your application development based on your technology preferences, such as primary language. The below list also provides a view of the components' rating on different dimensions such as community support availability, security vulnerability, and overall quality, helping you make an informed choice for implementation and maintenance of your application. Please review the components carefully, having a no license alert or proprietary license, and use them appropriately in your applications. Please check the component page for the exact license of the component. You can also get information on the component's features, installation steps, top code snippets, and top community discussions on the component details page. The links to package managers are listed for download, where packages are readily available. Otherwise, build from the respective repositories for use in your application. You can also use the source code from the repositories in your applications based on the respective license types.

kandi

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ai-hulks-app-kit

Ai Hulks App Kit

SPEAKER COUNTING It enhances understanding through automatic speech recognition Beneficial for real - world applications like call-center transcription and meeting transcription analytics Speaker Diarization is a developing field of study, with new approaches being published on a frequent basis. The Problem Not many studies have been done for estimating a large number of speakers. Diarization becomes extremely difficult when the number of speakers is huge. Providing the number of speakers to the diarization system can be advantageous Complete solution Architecture - Machine Learning model - To predict the no. of speakers and the time stamps of the speaker. Web App - Frontend for the user to use the feature. Middleware Flask Api - To connect Frontend and ML Model. We have build a Web App that a user can use to communicate and leverage the advantages of the our Machine learning model. Since the model we build and the web app are build on different platforms, we used REST API as a middleware to connect frontend and model.

osint-solutions

Open Source Intelligence

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python-animation

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<img src="https://kandi.dev/owassets/python-animation-libraries-banner.png" alt="Python Animation" style="height:auto;max-width:100%;"/> Animations have been used in gaming industries and other industries as well for a long time now. Animations are a great way to tell a story and create characters that people can relate to. There are many different types of animations out there but understanding how they work is important for anyone who wants to build their own game or animation project. Designers and Developers have to work in tandem to create immersive experiences and interact with users, leveraging the diversity of Animation libraries available. But which one is the right one? Find out here! Build interactive Python applications, animate elements, elegant UI effects. Top recommended libraries are manim - Animation engine for explanatory math videos; termtosvg - Record terminal sessions as SVG animations. Following is a list of the 13 best Python Animation libraries in 2022:

text-summarizer

Build Text Summarizer in Python

<img src="https://kandi.dev/owassets/text-summarizer-banner.png" alt="Text Summarizer Banner" style="height:auto;max-width:100%;"/> NLP text summarizer, is a Python package that summarizes texts and extracts the most important sentences from a given text. Text summarizer is commonly used in news feeding websites to summarize long articles. Summarizer shortens long texts such that the summarized text preserves all the essential points of the actual text. It uses spaCy, nltk, and NumPy to do the job. This solution is also used to summarize texts (in Extractive and abstractive techniques), extract key sentences and find their TF-IDF values. You can use this package for your own projects; we are sure you'll find it useful! Extraction-based summarization involves selecting sentences from an original document and organizing them into a cohesive summary. In contrast to extraction-based summarization, abstraction-based summaries are created by using algorithms to produce abstracts that can be used as templates. spaCy is a library for Natural Language Processing (NLP). It provides functions for tokenization, part of speech tagging, and parsing. The library also includes pre-trained models for some languages. NLTK (Natural Language Toolkit) is another popular toolkit for NLP tasks. It is used in many research papers to solve different problems related to NLP. There are several popular open-source libraries available for developers: <button class="MuiButtonBase-root MuiButton-root MuiButton-contained editexp MuiButton-containedSecondary" onclick="location.href='https://github.com/kandi1clickkits/bert-extractive-summarizer/raw/main/kit_installer.zip'" type="button"> ⬇️ Download 1-Click Installer </button>

kandi

1-Click Install

food-wastage-recommender-system

food Wastage Recommender system

Data Summary : Resources that have been shared for the problem statement has info about food items and their description. Also we had order info from both donor and from consumer side orders on daily basis. We have done data cleaning and preprocessing as required. Recommendation system : In order to control the food wastage we have built Recommendation engine using "item-item based collaborative filtering" to recommend the items which expire early and are more in consumption. Data Analysis : We have developed a dashboard on tableau using cleaned datasets and these analysis can be used to match supply-demand of different types of food and to give an overview to the NGO, donors and consumers on how to reduce the food wastage. Use the open source, cloud APIs, or public libraries listed below in your application development based on your technology preferences, such as primary language. The below list also provides a view of the components' rating on different dimensions such as community support availability, security vulnerability, and overall quality, helping you make an informed choice for implementation and maintenance of your application. Please review the components carefully, having a no license alert or proprietary license, and use them appropriately in your applications. Please check the component page for the exact license of the component. You can also get information on the component's features, installation steps, top code snippets, and top community discussions on the component details page. The links to package managers are listed for download, where packages are readily available. Otherwise, build from the respective repositories for use in your application. You can also use the source code from the repositories in your applications based on the respective license types.

kandi

1-Click Install

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QUESTION

Getting the error &quot;Nested CSS was detected, but CSS nesting has not been configured correctly&quot; in React app?

Asked 2022-Mar-23 at 09:04

I've been upgrading my CRA project to TailwindCSS 3, but now CSS nesting no longer works. Upon starting the server, the console spits out:

1(8:3) Nested CSS was detected, but CSS nesting has not been configured correctly.
2Please enable a CSS nesting plugin *before* Tailwind in your configuration.
3See how here: https://tailwindcss.com/docs/using-with-preprocessors#nesting
4

However, I don't see what must be done to correct this. I've tried setting up a plain CRA project with Tailwind (following this guide) just to make sure I have no conflicts, and still no success.

postcss.config.js:

1(8:3) Nested CSS was detected, but CSS nesting has not been configured correctly.
2Please enable a CSS nesting plugin *before* Tailwind in your configuration.
3See how here: https://tailwindcss.com/docs/using-with-preprocessors#nesting
4module.exports = {
5  plugins: {
6    &quot;tailwindcss/nesting&quot;: {},
7    tailwindcss: {},
8    autoprefixer: {},
9  },
10};
11

As you can see, I have added the nesting plugin before Tailwind. It appears to me as if the plugin isn't being detected whatsoever. I've also tried replacing it with postcss-nesting with same outcome.

Note: I've also tried using the array syntax with require('tailwind/nesting') like the guide suggests.

Interestingly, removing all plugins from postcss.config.js (or using a require that fails to resolve) still outputs the same error, implying that this file isn't needed to get Tailwind to load. Maybe I am missing something that causes the whole postcss.config.js file to not be loaded in the first place?


index.js:

1(8:3) Nested CSS was detected, but CSS nesting has not been configured correctly.
2Please enable a CSS nesting plugin *before* Tailwind in your configuration.
3See how here: https://tailwindcss.com/docs/using-with-preprocessors#nesting
4module.exports = {
5  plugins: {
6    &quot;tailwindcss/nesting&quot;: {},
7    tailwindcss: {},
8    autoprefixer: {},
9  },
10};
11import React from &quot;react&quot;;
12import ReactDOM from &quot;react-dom&quot;;
13import &quot;./index.css&quot;;
14
15ReactDOM.render(
16  &lt;React.StrictMode&gt;
17    &lt;div className=&quot;a&quot;&gt;
18      aaa
19      &lt;div className=&quot;b&quot;&gt;bbb&lt;/div&gt;
20    &lt;/div&gt;
21  &lt;/React.StrictMode&gt;,
22  document.getElementById(&quot;root&quot;)
23);
24

index.css:

1(8:3) Nested CSS was detected, but CSS nesting has not been configured correctly.
2Please enable a CSS nesting plugin *before* Tailwind in your configuration.
3See how here: https://tailwindcss.com/docs/using-with-preprocessors#nesting
4module.exports = {
5  plugins: {
6    &quot;tailwindcss/nesting&quot;: {},
7    tailwindcss: {},
8    autoprefixer: {},
9  },
10};
11import React from &quot;react&quot;;
12import ReactDOM from &quot;react-dom&quot;;
13import &quot;./index.css&quot;;
14
15ReactDOM.render(
16  &lt;React.StrictMode&gt;
17    &lt;div className=&quot;a&quot;&gt;
18      aaa
19      &lt;div className=&quot;b&quot;&gt;bbb&lt;/div&gt;
20    &lt;/div&gt;
21  &lt;/React.StrictMode&gt;,
22  document.getElementById(&quot;root&quot;)
23);
24@tailwind base;
25@tailwind components;
26@tailwind utilities;
27
28.a {
29  @apply text-blue-500;
30
31  .b {
32    @apply text-green-500;
33  }
34}
35

package.json: (omitted things for brevity)

1(8:3) Nested CSS was detected, but CSS nesting has not been configured correctly.
2Please enable a CSS nesting plugin *before* Tailwind in your configuration.
3See how here: https://tailwindcss.com/docs/using-with-preprocessors#nesting
4module.exports = {
5  plugins: {
6    &quot;tailwindcss/nesting&quot;: {},
7    tailwindcss: {},
8    autoprefixer: {},
9  },
10};
11import React from &quot;react&quot;;
12import ReactDOM from &quot;react-dom&quot;;
13import &quot;./index.css&quot;;
14
15ReactDOM.render(
16  &lt;React.StrictMode&gt;
17    &lt;div className=&quot;a&quot;&gt;
18      aaa
19      &lt;div className=&quot;b&quot;&gt;bbb&lt;/div&gt;
20    &lt;/div&gt;
21  &lt;/React.StrictMode&gt;,
22  document.getElementById(&quot;root&quot;)
23);
24@tailwind base;
25@tailwind components;
26@tailwind utilities;
27
28.a {
29  @apply text-blue-500;
30
31  .b {
32    @apply text-green-500;
33  }
34}
35{
36  &quot;name&quot;: &quot;tailwindtest&quot;,
37  &quot;dependencies&quot;: {
38    &quot;react&quot;: &quot;^17.0.2&quot;,
39    &quot;react-dom&quot;: &quot;^17.0.2&quot;,
40    &quot;react-scripts&quot;: &quot;5.0.0&quot;
41  },
42  &quot;scripts&quot;: {
43    &quot;start&quot;: &quot;react-scripts start&quot;,
44    &quot;build&quot;: &quot;react-scripts build&quot;,
45    &quot;test&quot;: &quot;react-scripts test&quot;,
46    &quot;eject&quot;: &quot;react-scripts eject&quot;
47  },
48  &quot;devDependencies&quot;: {
49    &quot;autoprefixer&quot;: &quot;^10.4.2&quot;,
50    &quot;postcss&quot;: &quot;^8.4.5&quot;,
51    &quot;tailwindcss&quot;: &quot;^3.0.12&quot;
52  }
53}
54

ANSWER

Answered 2022-Feb-03 at 18:38

This is mostly just bad news.

Create React App's Tailwind support means that they will detect tailwind.config.js in the project and add tailwindcss to their existing postcss configuration. Source in CRA

The guide that Tailwind offers on their site creates a dummy postcss.config.js - Making changes in this file does not change the actual postcss configuration. (misleading if anything)

This is a known issue currently - Github discussion on Tailwind support PR between Adam Wathan (Tailwind founder) and Ian Sutherland (CRA maintainer). But it does not seem like there is an intention to be fixed soon.

If you want to use nesting (or any PostCSS plugin really) is to eject from CRA using:

copy icondownload icon

1(8:3) Nested CSS was detected, but CSS nesting has not been configured correctly.
2Please enable a CSS nesting plugin *before* Tailwind in your configuration.
3See how here: https://tailwindcss.com/docs/using-with-preprocessors#nesting
4module.exports = {
5  plugins: {
6    &quot;tailwindcss/nesting&quot;: {},
7    tailwindcss: {},
8    autoprefixer: {},
9  },
10};
11import React from &quot;react&quot;;
12import ReactDOM from &quot;react-dom&quot;;
13import &quot;./index.css&quot;;
14
15ReactDOM.render(
16  &lt;React.StrictMode&gt;
17    &lt;div className=&quot;a&quot;&gt;
18      aaa
19      &lt;div className=&quot;b&quot;&gt;bbb&lt;/div&gt;
20    &lt;/div&gt;
21  &lt;/React.StrictMode&gt;,
22  document.getElementById(&quot;root&quot;)
23);
24@tailwind base;
25@tailwind components;
26@tailwind utilities;
27
28.a {
29  @apply text-blue-500;
30
31  .b {
32    @apply text-green-500;
33  }
34}
35{
36  &quot;name&quot;: &quot;tailwindtest&quot;,
37  &quot;dependencies&quot;: {
38    &quot;react&quot;: &quot;^17.0.2&quot;,
39    &quot;react-dom&quot;: &quot;^17.0.2&quot;,
40    &quot;react-scripts&quot;: &quot;5.0.0&quot;
41  },
42  &quot;scripts&quot;: {
43    &quot;start&quot;: &quot;react-scripts start&quot;,
44    &quot;build&quot;: &quot;react-scripts build&quot;,
45    &quot;test&quot;: &quot;react-scripts test&quot;,
46    &quot;eject&quot;: &quot;react-scripts eject&quot;
47  },
48  &quot;devDependencies&quot;: {
49    &quot;autoprefixer&quot;: &quot;^10.4.2&quot;,
50    &quot;postcss&quot;: &quot;^8.4.5&quot;,
51    &quot;tailwindcss&quot;: &quot;^3.0.12&quot;
52  }
53}
54npm run eject
55

After ejecting you can find CRA's postcss configuration in config/webpack.config.js - look for postcss-loader. Editing the configuration there can enable any postcss features.

PS: Look out for postcss-preset-env in the default configuration while enabling nesting. Tailwind requires you to edit configuration if this is present.

Source https://stackoverflow.com/questions/70665302