https://github.com/ayushverma135/llama-custom-x
Llama Custom X is a Streamlit-based AI chatbot that interacts with CSV datasets using conversational AI. It offers data-driven insights, intelligent responses, and visualizations for enhanced data analysis. Perfect for cybersecurity data exploration.
https://github.com/ayushverma135/llama-custom-x
chatbot ctransformers gpt4all huggingface-transformers llama2 meta python
Last synced: 5 days ago
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Llama Custom X is a Streamlit-based AI chatbot that interacts with CSV datasets using conversational AI. It offers data-driven insights, intelligent responses, and visualizations for enhanced data analysis. Perfect for cybersecurity data exploration.
- Host: GitHub
- URL: https://github.com/ayushverma135/llama-custom-x
- Owner: Ayushverma135
- License: bsd-2-clause
- Created: 2024-09-04T18:47:08.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-09-04T19:11:56.000Z (about 1 year ago)
- Last Synced: 2025-01-13T09:39:23.799Z (9 months ago)
- Topics: chatbot, ctransformers, gpt4all, huggingface-transformers, llama2, meta, python
- Language: Python
- Homepage:
- Size: 6.88 MB
- Stars: 0
- Watchers: 2
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Security: securityserver.csv
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README
# Llama-Custom-X - Chatbot Assistant 🤖
This repository contains a Streamlit-based web application named **Llama-Custom-X**, a Cyber Assistant designed to interact with CSV datasets and provide data-driven insights through a chatbot interface. The app leverages machine learning models to facilitate conversational queries and offers robust data visualization features.
## Features
- **Chatbot Interface**: Conversational interaction with the first dataset (`CQS.csv`). Ask questions about the data, and the AI will respond with insightful answers.
- **Data Preview and Visualization**: Visualize key aspects of the datasets (`CQS.csv` and `LOG.csv`) using count plots, time series, ring charts, and more.
- **Customizable and Expandable**: Easy to add more visualizations or modify existing ones based on your dataset requirements.
## Installation
1. **Clone the repository**:
```bash
git clone https://github.com/Ayushverma135/Llama-Custom-X.git
cd Llama-Custom-X
```
2. **Install dependencies**:
Make sure you have Python 3.8+ installed. Then, install the required packages:
```bash
pip install -r requirements.txt
```
3. **Download the model**:
Ensure that the `llama-2-7b-chat.Q4_0.gguf` model is available in your system. You can download it from GPT4All or Hugging Face.
## Usage
1. **Run the application**:
```bash
streamlit run app.py
```
2. **Interact with the Cyber Assistant**:
- Use the chatbot to query your data in the `CQS.csv` file. The assistant provides intelligent answers based on the CSV content.
- Explore visualizations and statistics for both `CQS.csv` and `LOG.csv`.
## Dependencies
- `streamlit` - Web framework for creating the interactive app.
- `pandas` - Data manipulation and analysis library.
- `matplotlib` & `seaborn` - For data visualization.
- `langchain` - Framework for building language model-powered applications.
- `HuggingFaceEmbeddings` - To embed text for similarity search.
- `FAISS` - Facebook AI Similarity Search, used for fast similarity search.
## Installing `llama-2-7b-chat.Q4_0.gguf` Model from GPT4All and Hugging Face
### From GPT4All:
1. **Install GPT4All:**
- If you haven't already, you can install GPT4All with the following command:
```bash
pip install gpt4all
```
2. **Download the Model:**
- Visit the [GPT4All website](https://gpt4all.io/index.html) to download the `llama-2-7b-chat.Q4_0.gguf` model. Ensure you download the `.gguf` version.
- Alternatively, you can use a direct download link (if provided by GPT4All) to download the model.
3. **Load the Model:**
- Once the model is downloaded, you can load it using GPT4All in Python:
```python
from gpt4all import GPT4All
model_path = "path/to/llama-2-7b-chat.Q4_0.gguf"
gpt = GPT4All(model_path)
```
### From Hugging Face:
1. **Install Hugging Face Transformers:**
- Install the `transformers` library if you don't have it:
```bash
pip install transformers
```
2. **Install Hugging Face Hub (Optional):**
- If you want to interact with models directly from the Hugging Face Hub:
```bash
pip install huggingface_hub
```
3. **Download the Model:**
- Use the Hugging Face model page to download `llama-2-7b-chat.Q4_0.gguf`. You may need to specify the correct variant if it’s available.
- Alternatively, you can clone the model repository:
```bash
git lfs install
git clone https://huggingface.co/your-username/llama-2-7b-chat
```
4. **Load the Model:**
- Load the model using the `transformers` library:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "path/to/llama-2-7b-chat"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
```
## Acknowledgements
- **Streamlit** for providing an easy-to-use framework for building interactive web apps.
- **LangChain** for making it easier to build language model applications.
- **Hugging Face** for their comprehensive library of machine learning models and embeddings.
- **FAISS** for enabling efficient similarity searches over large datasets.
## Notes:
- The specific path or way to load the model might vary depending on updates from GPT4All or Hugging Face, so always refer to the latest documentation.
- Ensure your environment has sufficient resources to handle large models like `llama-2-7b-chat`.
