# WinForm_Ollama_Copilot

**Repository Path**: HesenjanJava/WinForm_Ollama_Copilot

## Basic Information

- **Project Name**: WinForm_Ollama_Copilot
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-04-01
- **Last Updated**: 2024-04-01

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Ollama Copilot

`Ollama Copilot` is a UI for Ollama on Windows that uses Windows Forms.

Copilot responses can be automatically forward to other applications just like other paid copilots.

The `Ollama Copilot` has other features like speech to text, text to speech, and OCR all using free open-source software.

Check out [Releases](https://github.com/tgraupmann/WinForm_Ollama_Copilot/releases) for the latest installer.

## Screenshots

![image_1](images/image_1.png)

## Videos

[Playlist](https://www.youtube.com/playlist?list=PL4mjXeDqRBMTj8hd6OOv5u0qhJVZ32upw)

**Overview of Ollama Copilot**

<a target="_blank" href="https://www.youtube.com/watch?v=4mKgcgBTwCo"><img src="https://img.youtube.com/vi/4mKgcgBTwCo/0.jpg"/></a>

**Ollama Copilot v1.0.0**

<a target="_blank" href="https://www.youtube.com/watch?v=Jh6jCRSlclk"><img src="https://img.youtube.com/vi/Jh6jCRSlclk/0.jpg"/></a>

**Youtube Transcripts v1.0.1**

<a target="_blank" href="https://www.youtube.com/watch?v=lY-6ZdsuHS8"><img src="https://img.youtube.com/vi/lY-6ZdsuHS8/0.jpg"/></a>

**Speech to Text v1.0.2**

<a target="_blank" href="https://www.youtube.com/watch?v=BBn0__pgHIA"><img src="https://img.youtube.com/vi/BBn0__pgHIA/0.jpg"/></a>

**Text to Speech v1.0.3**

<a target="_blank" href="https://www.youtube.com/watch?v=rVadgr7rnVk"><img src="https://img.youtube.com/vi/rVadgr7rnVk/0.jpg"/></a>

**Optical Character Recognition v1.0.4**

<a target="_blank" href="https://www.youtube.com/watch?v=WHM-Bc9k04E"><img src="https://img.youtube.com/vi/WHM-Bc9k04E/0.jpg"/></a>

## Dependencies

* [Ollama AI](https://ollama.ai)

* Ollama Copilot Installer (Windows) - [Ollama Copilot v1.0.4](https://github.com/tgraupmann/WinForm_Ollama_Copilot/releases/tag/v1.0.4)

### Visual Studio Build Dependencies

* Note you can skip the Visual Studio Build dependencies if you used the `Ollama Copilot Installer`.

* [InnoSetup](https://jrsoftware.org/isinfo.php)

* [Microsoft Visual Studio Installer Projects 2022](https://marketplace.visualstudio.com/items?itemName=VisualStudioClient.MicrosoftVisualStudio2022InstallerProjects)

* Open `WinForm_Ollama_Copilot.sln` in [Visual Studio 2022](https://visualstudio.microsoft.com/).

![image_5](/images/image_5.png)

* The project uses Newtonsoft JSON so right-click the solution in solution explorer to select `Restore NuGet Packages`

![image_2](images/image_2.png)

* Build and run the application

![image_3](images/image_3.png)

* [Html Agility Pack](https://html-agility-pack.net/)

* [youtube-transcript-api-sharp](https://github.com/BobLd/youtube-transcript-api-sharp)

### Feature Dependencies

* [Whisper](https://github.com/openai/whisper.git)

* [Pyttsx3](https://pypi.org/project/pyttsx3/)

* [Teseract-OCR](https://tesseract-ocr.github.io/tessdoc/Installation.html)

### Ollama with Windows preview

* Install the [Ollama Windows preview](https://github.com/ollama/ollama)

* Install the `llama2` model to enable the Chat API

```shell
ollama run llama2
```

* Install the `llava` model

```shell
ollama run llava
```

* Install the `gemma` model (7B default)

```shell
ollama run gemma
```

* You can remove the `gemma` model (7B)

```shell
ollama rm gemma
```

* to install the smaller `gemma` 2B model

```shell
ollama run gemma:2b
```

### Ollama with Docker

* Install [Docker Desktop](https://www.docker.com/products/docker-desktop/)

![image_4](images/image_4.png)

```shell
docker run -d --gpus=all -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
```

* Install the `llama2` model to enable the Chat API.

```shell
docker exec -it ollama ollama run llama2
```

* Install the `llava` model

```shell
docker exec -it ollama ollama run llava
```

* Install the `gemma` model

```shell
docker exec -it ollama ollama run gemma
```

* Install the `mixtral` model (requires 48GB of VRAM)

```shell
docker exec -it ollama ollama run mixtral
```

## Launch Whisper Server to enable local dictation

### WSL2

* Install Ubuntu 22.04.3 LTS with WSL2

* Setup Ubuntu for hosting the local [Whisper](https://openai.com/research/whisper) server

```shell
sudo apt-get update
sudo apt install python3-pip
sudo apt install uvicorn
pip3 install FastAPI[all]
pip3 install uvloop
pip3 install numpy
sudo apt-get install curl
sudo apt-get install ffmpeg
pip3 install ffmpeg
pip3 install scipy
pip3 install git+https://github.com/openai/whisper.git
```

* Run the server

```shell
python3 -m uvicorn WhisperServer:app --reload --port 11437
```

![image_6](images/image_6.png)

# Useful information on Whisper model sizes.

* Source: [Whisper](https://github.com/openai/whisper)

## Available models and languages

There are five model sizes, four with English-only versions, offering speed and accuracy tradeoffs. Below are the names of the available models and their approximate memory requirements and inference speed relative to the large model; actual speed may vary depending on many factors including the available hardware.

|  Size  | Parameters | English-only model | Multilingual model | Required VRAM | Relative speed |
|:------:|:----------:|:------------------:|:------------------:|:-------------:|:--------------:|
|  tiny  |    39 M    |     `tiny.en`      |       `tiny`       |     ~1 GB     |      ~32x      |
|  base  |    74 M    |     `base.en`      |       `base`       |     ~1 GB     |      ~16x      |
| small  |   244 M    |     `small.en`     |      `small`       |     ~2 GB     |      ~6x       |
| medium |   769 M    |    `medium.en`     |      `medium`      |     ~5 GB     |      ~2x       |
| large  |   1550 M   |        N/A         |      `large`       |    ~10 GB     |       1x       |

## Test the Whisper mode conversion MP3 to text

```shell
python3 WhisperTest.py audio.mp3
```

## Launch Pyttsx3 Server to enable text to speech

### Windows

* Install [Python](https://apps.microsoft.com/detail/9NRWMJP3717K?hl=en-us&gl=US) from the Microsoft Store app on the Windows host machine which has access to the sound card.

![image_7](images/image_7.png)

* Open the Windows command prompt to install dependencies

```cmd
pip3 install uvicorn
pip3 install FastAPI[all]
pip3 install pyttsx3
```

* Launch the Pyttsx3 Server in the Windows command prompt

```cmd
python3 -m uvicorn Pyttsx3Server:app --reload --port 11438
```

### Speech Commands

* "Prompt clear" - Clears the prompt text area

* "Prompt submit" - Submits the prompt

* "Response play" - Speaks the response

- "Response clear" - Clears the response text area

## Launch Tesseract-OCR server for real-time OCR

### Windows

* Install `pytesseract`

```shell
pip install pytesseract
```

* Install Tesseract-OCR - [Installation](https://tesseract-ocr.github.io/tessdoc/Installation.html)

* Windows Installer - [Tesseract at UB Mannheim](https://github.com/UB-Mannheim/tesseract/wiki)

* Add Tesseract to your path: `C:\Program Files\Tesseract-OCR`

* Run the server

```shell
python3 -m uvicorn TesseractOCRServer:app --reload --port 11439 --log-level error
```