Merge pull request #467 from microsoft/workshop-page

fix: workshop doc fixes

Author: blessing-sanusi

.github/workflows/deploy.yml

@@ -39,5 +39,4 @@ jobs:
         with:
           github_token: ${{ secrets.GITHUB_TOKEN }}
           publish_dir: docs/workshop/site
-          # optionally set the branch to deploy to:
-          # publish_branch: gh-pages
+          

docs/workshop/README.md

@@ -23,13 +23,13 @@ The current repository is instrumented with a `workshop/docs` folder that contai
 1. Install the `mkdocs-material` package
 
     ```bash
-    pip install mkdocs-material
+    pip install mkdocs-material mkdocs-jupyter
     ```
 
 2. Run the `mkdocs serve` command from the `workshop` folder
 
     ```bash
-    cd workshop/docs
+    cd docs/workshopcd
     mkdocs serve -a localhost:5000
     ```
 

docs/workshop/docs/workshop/Challenge-1/Code_Walkthrough/02_Frontend.md

@@ -5,7 +5,9 @@
 
 The frontend is a **React-based web interface** that allows users to explore insights from conversations, interact with an AI-powered chatbot, and view dynamic visualizations.
 
-![image](../../../../../../documents/Images/ReadMe/ui.png)
+
+![image](../../img/ReadMe/ckm-ui.png)
+
 
 ### Features
 

docs/workshop/docs/workshop/Challenge-1/Solution_Overview.md

@@ -2,7 +2,9 @@
 <!-- ## Overview -->
 The Conversation Knowledge Mining Solution Accelerator is a robust application designed to extract actionable insights from conversational data. It leverages Azure AI services and provides an interactive user interface for querying and visualizing data. The solution is built with a modular architecture, combining a React-based frontend, a FastAPI backend, and Azure services for data processing and storage.
 
-![image](../../../../../documents/Images/ReadMe/solution-architecture.png)
+
+![image](../img/ReadMe/ckm-sol-arch.png)
+
 
 The solution extracts insights from call audio files or transcripts and enables users to interact with the data via a chatbot and dynamic charts:
 

docs/workshop/docs/workshop/Challenge-5/notebooks/video_chapter_generation.ipynb

@@ -23,7 +23,7 @@
    "source": [
     "\n",
     "## Pre-requisites\n",
-    "1. Follow [README](../README.md#configure-azure-ai-service-resource) to create essential resource that will be used in this sample.\n",
+    "1. Follow [README](../docs/create_azure_ai_service.md) to create essential resource that will be used in this sample.\n",
     "1. Install required packages"
    ]
   },
@@ -92,7 +92,7 @@
    "metadata": {},
    "source": [
     "## Create a custom analyzer and submit the video to generate the description\n",
-    "The custom analyzer schema is defined in [../analyzer_templates/video_content_understanding.json](../analyzer_templates/video_content_understanding.json). The main custom field is `segmentDescription` as we need to get the descriptions of video segments and feed them into chatGPT to generate the scenes and chapters. Adding transcripts will help to increase the accuracy of scenes/chapters segmentation results. To get transcripts, we will need to set the`returnDetails` parameter in the `config` field to `True`.\n",
+    "The custom analyzer schema is defined in **../analyzer_templates/video_content_understanding.json**. The main custom field is `segmentDescription` as we need to get the descriptions of video segments and feed them into chatGPT to generate the scenes and chapters. Adding transcripts will help to increase the accuracy of scenes/chapters segmentation results. To get transcripts, we will need to set the`returnDetails` parameter in the `config` field to `True`.\n",
     "\n",
     "In this example, we will use the utility class `AzureContentUnderstandingClient` to load the analyzer schema from the template file and submit it to Azure Content Understanding service. Then, we will analyze the video to get the segment descriptions and transcripts.\n"
    ]

docs/workshop/docs/workshop/Challenge-5/notebooks/video_tag_generation.ipynb

@@ -23,7 +23,7 @@
    "source": [
     "\n",
     "## Pre-requisites\n",
-    "1. Follow [README](../README.md#configure-azure-ai-service-resource) to create essential resource that will be used in this sample.\n",
+    "1. Follow [README](../docs/create_azure_ai_service.md) to create essential resource that will be used in this sample.\n",
     "1. Install required packages"
    ]
   },
@@ -92,7 +92,7 @@
    "metadata": {},
    "source": [
     "## Create a custom analyzer and submit the video to generate tags\n",
-    "The custom analyzer schema is defined in [../analyzer_templates/video_tag.json](../analyzer_templates/video_tag.json). The custom fields are `segmentDescription`, `transcript` and `tags`. Adding description and transcripts helps to increase the accuracy of tag generation results. To get transcripts, we will need to set the`returnDetails` parameter in the `config` field to `True`.\n",
+    "The custom analyzer schema is defined in **../analyzer_templates/video_tag.json**. The custom fields are `segmentDescription`, `transcript` and `tags`. Adding description and transcripts helps to increase the accuracy of tag generation results. To get transcripts, we will need to set the`returnDetails` parameter in the `config` field to `True`.\n",
     "\n",
     "In this example, we will use the utility class `AzureContentUnderstandingClient` to load the analyzer schema from the template file and submit it to Azure Content Understanding service. Then, we will analyze the video to get the segment tags.\n"
    ]

docs/workshop/mkdocs.yml

@@ -117,7 +117,7 @@ nav:
       - CU-AI Foundry: workshop/Challenge-0/CU-Challenge.md
   - Challenge 1:
       - Solution Overview: workshop/Challenge-1/Solution_Overview.md
-      - Deployment: workshop/Challenge-1/index.md
+      - Deployment: workshop/Challenge-1/Deployment.md
       # - App Authentication: workshop/Challenge-1/App_Authentication.md
       - Explore Data: workshop/Challenge-1/Code_Walkthrough/01_Data_Explore.md
       - Frontend: workshop/Challenge-1/Code_Walkthrough/02_Frontend.md
@@ -134,8 +134,8 @@ nav:
       - Knowledge Mining API Notebook: workshop/Challenge-3-and-4/knowledge_mining_api.ipynb
   - Challenge 5:
       - Overview: workshop/Challenge-5/index.md
-      - Chapter Generation Notebook: workshop/Challenge-5/video_chapter_generation.ipynb
-      - Tag Generation Notebook: workshop/Challenge-5/video_tag_generation.ipynb
+      - Chapter Generation Notebook: workshop/Challenge-5/notebooks/video_chapter_generation.ipynb
+      - Tag Generation Notebook: workshop/Challenge-5/notebooks/video_tag_generation.ipynb
   - Challenge 6:
       - Overview: workshop/Challenge-6/index.md
       - Content Safety Evaluation Notebook: workshop/Challenge-6/Content_safety_evaluation.ipynb