Merge pull request #122 from openvinotoolkit/barabanov/readme-update

Readme update

Author: AlexanderBarabanov

README.md

@@ -8,45 +8,21 @@
 
 Get started with OpenVINO™ Test Drive, an application that allows you to run generative AI and vision models trained by [Intel® Geti™](https://docs.geti.intel.com/) directly on your computer or edge device using [OpenVINO™ Runtime](https://github.com/openvinotoolkit/openvino).
 
-
 <p align="center">
   <img src="./docs/llm_model_chat.gif" width="600" alt="sample">
 </p>
 
 With use of OpenVINO™ Test Drive you can:
-+ **Chat with LLMs** and evaluating model performance on your computer or edge device
-+ **Experiment with different text prompts** to generate images using Stable Diffusion and Stable DiffusionXL models
-+ **Transcribe speech from video** using Whisper models, including generation of timestamps
-+ **Run and visualize results of models** trained by Intel® Geti™ using single image inference or batch inference mode
-
-## Installation
-
-Download the latest release from the [Releases repository](https://storage.openvinotoolkit.org/repositories/openvino_testdrive/).
-
-> [!NOTE]
-> To verify downloaded file integrity, you can generate a SHA-256 of the downloaded file and compare it to the SHA-256 from corresponding `.sha256` file published in Releases repository. 
-
-### Installation on Windows
-
-> [!IMPORTANT]
-> For Intel® NPU, please use the Intel® NPU Driver latest available version.
-
-1. Downloading the zip archive [Releases repository](https://storage.openvinotoolkit.org/repositories/openvino_testdrive/) `Windows` folder .
-
-<p align="left">
-  <img src="./docs/win_inst.gif" width="500">
-</p>
-
-2. Extract zip archive double-click the MSIX installation package, click `Install` button and it will display the installation process
-
-3. Click on the application name on Windows app list to launch OpenVINO™ Test Drive.
-
 
-## Quick start
+- **Chat with LLMs** and evaluating model performance on your computer or edge device
+- **Experiment with different text prompts** to generate images using Stable Diffusion and Stable DiffusionXL models
+- **Transcribe speech from video** using Whisper models, including generation of timestamps
+- **Run and visualize results of models** trained by Intel® Geti™ using single image inference or batch inference mode
 
-Upon starting the application, you can import a model using either Hugging Face for LLMs or upload Intel® Geti™ models from local disk.
+## Key features
 
-### Text generation and LLM performance evaluation
+<details>
+<summary>📝 Text generation and LLM performance evaluation </summary>
 
 1. Choose a model from predefined set of popular models or pick one from Hugging Face using `Import model` -> `Hugging Face` and import it.
 <p align="left">
@@ -62,8 +38,10 @@ Upon starting the application, you can import a model using either Hugging Face
 <p align="left">
   <img src="./docs/metrics.gif" width="500">
 </p>
+</details>
 
-### Retrieval-Augmented Generation with LLM
+<details>
+<summary>📚 Retrieval-Augmented Generation with LLM </summary>
 
 1. It is possible to upload files and create knowledge base for RAG (Retrieval-Augmented Generation) using `Knowledge base` tab
 <p align="left">
@@ -78,8 +56,10 @@ This knowledge base can be used during text generation with LLM models.
 <p align="left">
   <img src="./docs/rag2.gif" width="500">
 </p>
+</details>
 
-### Work with Visual Language Models
+<details>
+<summary>🧠 Work with Visual Language Models </summary>
 
 1. Try Visual Language Model (VLM) for image analysis.
 <p align="left">
@@ -90,8 +70,10 @@ This knowledge base can be used during text generation with LLM models.
 <p align="left">
   <img src="./docs/vlm2.gif" width="500">
 </p>
+</details>
 
-### Transcribe speech from video
+<details>
+<summary>✍️ Transcribe speech from video </summary>
 
 1. Try Whisper for video transcription.
 <p align="left">
@@ -105,8 +87,10 @@ This knowledge base can be used during text generation with LLM models.
 </p>
 
 3. Use `Performance metrics` tab to get LLM performance metrics on your computer.
+</details>
 
-### Image generation
+<details>
+<summary>🎨 Image generation </summary>
 
 1. Choose an image generation LLM from predefined set of popular models or pick one from Hugging Face using `Import model` -> `Hugging Face` and import it.
 
@@ -120,9 +104,12 @@ This knowledge base can be used during text generation with LLM models.
 
 You can export LLM via `Export model` button.
 
-### Images inference with models trained by Intel® Geti™
+</details>
 
-1. Download code deployment for the model in OpenVINO format trained by Intel® Geti™. 
+<details>
+<summary>🤖 Images inference with models trained by Intel® Geti™ </summary>
+
+1. Download code deployment for the model in OpenVINO format trained by Intel® Geti™.
 
 <p align="left">
   <img src="./docs/geti_download.gif" width="500">
@@ -148,6 +135,29 @@ You can export LLM via `Export model` button.
 <p align="left">
   <img src="./docs/geti_batch.gif" width="500">
 </p>
+</details>
+
+## Installation
+
+Download the latest release from the [Releases repository](https://storage.openvinotoolkit.org/repositories/openvino_testdrive/).
+
+> [!NOTE]
+> To verify downloaded file integrity, you can generate a SHA-256 of the downloaded file and compare it to the SHA-256 from corresponding `.sha256` file published in Releases repository.
+
+### Installation on Windows
+
+> [!IMPORTANT]
+> For Intel® NPU, please use the Intel® NPU Driver latest available version.
+
+1. Downloading the zip archive [Releases repository](https://storage.openvinotoolkit.org/repositories/openvino_testdrive/) `Windows` folder .
+
+<p align="left">
+  <img src="./docs/win_inst.gif" width="500">
+</p>
+
+2. Extract zip archive double-click the MSIX installation package, click `Install` button and it will display the installation process
+
+3. Click on the application name on Windows app list to launch OpenVINO™ Test Drive.
 
 ## Build
 
@@ -160,7 +170,7 @@ Secondly, the bindings and its dependencies for your platform to be added to `./
 
 ## Ecosystem
 
-- [OpenVINO™](https://github.com/openvinotoolkit/openvino)  - software toolkit for optimizing and deploying deep learning models.
+- [OpenVINO™](https://github.com/openvinotoolkit/openvino) - software toolkit for optimizing and deploying deep learning models.
 - [GenAI Repository](https://github.com/openvinotoolkit/openvino.genai) and [OpenVINO Tokenizers](https://github.com/openvinotoolkit/openvino_tokenizers) - resources and tools for developing and optimizing Generative AI applications.
 - [Intel® Geti™](https://docs.geti.intel.com/) - software for building computer vision models.
 - [OpenVINO™ Vision ModelAPI](https://github.com/openvinotoolkit/model_api) - a set of wrapper classes for particular tasks and model architectures, simplifying data preprocess and postprocess as well as routine procedures.
@@ -174,4 +184,4 @@ For those who would like to contribute to the OpenVINO™ Test Drive, please che
 OpenVINO™ Test Drive repository is licensed under [Apache License Version 2.0](LICENSE).
 By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.
 
-FFmpeg is an open source project licensed under LGPL and GPL. See https://www.ffmpeg.org/legal.html. You are solely responsible for determining if your use of FFmpeg requires any additional licenses. Intel is not responsible for obtaining any such licenses, nor liable for any licensing fees due, in connection with your use of FFmpeg.
+FFmpeg is an open source project licensed under LGPL and GPL. See https://www.ffmpeg.org/legal.html. You are solely responsible for determining if your use of FFmpeg requires any additional licenses. Intel is not responsible for obtaining any such licenses, nor liable for any licensing fees due, in connection with your use of FFmpeg.