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https://github.com/kspviswa/local-packet-whisperer

A Fun project using Ollama, Streamlit & PyShark to chat with PCAP/PCAPNG files locally, privately!
https://github.com/kspviswa/local-packet-whisperer

llm-bot ollama-client pcap-analyzer python3 streamlit-webapp

Last synced: 2 days ago
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A Fun project using Ollama, Streamlit & PyShark to chat with PCAP/PCAPNG files locally, privately!

Host: GitHub
URL: https://github.com/kspviswa/local-packet-whisperer
Owner: kspviswa
License: mit
Created: 2024-02-23T20:29:42.000Z (9 months ago)
Default Branch: main
Last Pushed: 2024-10-06T19:58:46.000Z (about 1 month ago)
Last Synced: 2024-10-11T18:55:00.226Z (about 1 month ago)
Topics: llm-bot, ollama-client, pcap-analyzer, python3, streamlit-webapp
Language: Python
Homepage:
Size: 7.43 MB
Stars: 49
Watchers: 2
Forks: 19
Open Issues: 2
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        ![](gifs/lpw_logo_small.png)

# Local Packet Whisperer (LPW)

### A Fun project using [Ollama](https://github.com/ollama), [Streamlit](https://streamlit.io) & [PyShark](https://github.com/KimiNewt/pyshark) to chat with PCAP/PCAG NG files locally, privately!

[![Downloads](https://static.pepy.tech/badge/lpw)](https://pepy.tech/project/lpw) [![Downloads](https://static.pepy.tech/badge/lpw/month)](https://pepy.tech/project/lpw)

## Features & Background

1) 100% local, private PCAP assistant powered by range of local LLMs at your control, powered by Ollama

2) Purely based on promp engg without any fancy libraries & dependencies. 100% vanilla

3) Uses streamlit for the FE and pyshark for the pcap parsing needs

4) Available as a pip installable package. So just *pip it away!* 😎

5) As of **v0.2.3**, you can also connect LPW to a Ollama server running over a network.

![](gifs/lpw_latest_cover.png)

Refer [Release History](https://github.com/kspviswa/local-packet-whisperer/releases) for more details info on what each release contains.

## Requirements

1) Download & Install [Ollama](https://ollama.ai) by referring to instructions according to your OS [here](https://ollama.com/download)

2) Pull any Chat based LLM models to use with LPW.

```

ollama pull dolphin-mistral:latest

```

3) If not running the desktop application, Start Ollama Server (refer [here](https://github.com/ollama/ollama?tab=readme-ov-file#start-ollama))

4) You also need to install `tshark` executable. You could either install the [Wireshark Application](https://www.wireshark.org/download.html) or simply use `brew install tshark`. 

    

    ⚠️Warning⚠️ If you don't perform this step, you may see below error

    ```

    TSharkNotFoundException: TShark not found. Try adding its location to the configuration file.

    ```

     

## Usage

1) Install *LPW* using pip

```

pip install lpw

```

2) This will install `lpw` CLI in your machine. Now simply Start or Stop LPW as follows:

```

lpw {start or stop}

lpw -h #for help

```

3) LPW will automatically fetch the local models from Ollama local repo and populate the dropdown. Select a model to start your test. You can play with more than 1 model to compare the results 😎

![](gifs/select_models.gif)

4) Now upload a PCAP/PCAPNG file.

![](gifs/upload_pcap.gif)

5) You can now start to chat with LPW and ask questions about the packet. **Please Note: The performance of LPW depends on the underlying model. So feel free to download as many local LLMs from Ollama and try it.** It is fun to see different response 🤩🤩🤩.

![](gifs/packet_chat.gif)

*(This is a long gif. You will find LLM response at the end of the gif)*

6) By default *PyShark* parse the pcap till transport layer. If you want, you can help the LLM to parse application layer by selecting protocol filter in the analysis *(just like how we would do in wireshark)* .

![](gifs/chat_with_ntp.gif)

## Local Development

1) Clone this repo and install requirements

```

git clone https://github.com/kspviswa/local-packet-whisperer.git

python3 -m venv .venv

source .venv/bin/activate

pip install -r requirements.txt

```

2) Run streamlit app & point to `http://localhost:8501`

```

streamlit run bin/lpw_main.py

```

or simply

```

/bin/lpw {start or stop}

```

## Contributions

I just created this project based on inspiration from similar project called [Packet Buddy](https://github.com/automateyournetwork/packet_buddy) which used open AI. But if you find this useful and wanna contribute bug fixes, additional features feel free to do so by raising a PR or open issues for me to fix. I intend to work on this as a hobby unless there is some interest in the community.