Merge branch 'develop' into RymMichaut-patch-10

docs/knowledge_base/demos/sentiment_analysis.md

@@ -0,0 +1,37 @@
+Welcome to Taipy's Sentiment Analysis demo page, where you can witness how Taipy leverages an AI-powered text analysis tool. In this demonstration, we will delve into the description and functionality of our Sentiment Analysis use case, showcasing how you can harness Taipy's potential for unlocking valuable insights from text data.
+
+# Understanding Sentiment Analysis
+Sentiment analysis, also called "opinion mining", is a technique in Natural Language Processing (NLP) used to figure out the emotional tone conveyed in a text. It helps businesses and individuals better grasp the feelings, viewpoints, and attitudes expressed in written content. Taipy's sentiment analysis model excels in making this process highly efficient and accurate.
+
+# The Two-Page Application
+
+## Page 1: Analyzing User Input
+The initial page of our Sentiment Analysis app, named "Line", is meant for instantly analyzing user input. Whether it's a brief sentence or a longer paragraph, just type or paste the text into the input box, and Taipy will quickly evaluate the sentiments conveyed in the text. This is perfect for rapidly gauging the sentiments in social media comments, customer reviews, or any text you come across online.
+
+## Page 2: Uploading and Analyzing Text Files
+The second page, named "Text", takes sentiment analysis to the next level by allowing users to upload entire text files for comprehensive sentiment analysis. Users can select a text file from their device, and Taipy will analyze the entire content to provide insights into the sentiments expressed throughout the document. This feature is particularly useful for processing longer texts such as articles, reports, or extensive customer feedback.
+
+# Powered by Taipy
+Both pages of this Sentiment Analysis application were built using Taipy's advanced technology. Taipy is an open-source Python library for building web applications' front-end and back-end. And in this demo, it allowed building the GUI to interact with the AI-powered text analysis tool.
+
+# How to Use Taipy for Sentiment Analysis
+To make use of Taipy's sentiment analysis abilities for your own projects, just follow these easy steps:
+
+1. Go on this [page](https://sentiment-analysis.taipy.cloud/line)
+
+2. On the first page, input the text you want to analyze directly into the provided text box.
+
+3. Click the "Analyze" button to receive instant sentiment analysis results.
+
+4. To analyze an entire text file, proceed to the second page.
+
+5. Upload the text file you wish to analyze.
+
+6. The application will process the entire text and display the sentiments detected within the document.
+
+
+
+# Conclusion
+Sentiment analysis is a valuable tool for comprehending the emotions and viewpoints conveyed in text data, and Taipy makes it easy and effective for various uses. Whether you require swift insights from user comments or thorough sentiment analysis of extensive documents, Taipy's Sentiment Analysis feature is available to assist you in uncovering valuable information concealed within text.
+
+Would you be ready to get started? Visit Taipy's [Sentiment Analysis demo page](https://sentiment-analysis.taipy.cloud/line) today and discover the future of text analysis with Taipy.

docs/knowledge_base/tutorials/complete_application/index.md

@@ -2,12 +2,12 @@
 
     Taipy requires **Python 3.8** or newer.
 
-Welcome to the **Tutorial** guide, which will walk you through creating a complete application 
-from the front end to the back end. You don't need any prior knowledge to complete this tutorial.
+This tutorial guide will walk you through creating a complete application from the front end to 
+the back end. You don't need any prior knowledge to complete this tutorial.
 
 ![Tutorial application](step_01/overview.gif){ width=700 style="margin:auto;display:block;border: 4px solid rgb(210,210,210);border-radius:7px" }
 
-In the **"Tutorial"** each step concentrates on fundamental ideas about *Taipy*.
+Each step concentrates on fundamental ideas about *Taipy*.
 
 ## Objective of the Application
 
@@ -55,7 +55,6 @@ Does the application use scenario management or version management? [No]: yes
 Does the application use Rest API? [No]: no
 ```
 
-
 So, without further delay, let's begin to code!
 
 ## Steps

mkdocs.yml_template

@@ -52,6 +52,7 @@ nav:
         - "Data Nodes and Tasks": knowledge_base/tutorials/data_nodes.md
     - "Demos":
         - knowledge_base/demos/index.md
+        - "Sentiment Analysis": knowledge_base/demos/sentiment_analysis.md
     - "Tips and tricks":
         - knowledge_base/tips/index.md
   - "Manuals":