End-to-End GENAI App Using DeepSeek R1 Model

Project Description

This project demonstrates an end-to-end generative AI application using the DeepSeek R1 model. The app utilizes Ollama to run the model locally, Langchain for chaining and processing the model's responses, and Streamlit for creating an interactive web interface.

Technologies Used

• DeepSeek R1 Model (Deepseek R1-1.5B)
• Ollama: Used to run the DeepSeek model locally
• Langchain: To manage and chain the language model prompts
• Streamlit: To create an interactive user interface
• Python 3.10

Installation Instructions

Before using this repo, ensure that Ollama is already installed on your system. Here are the installation steps for the repo:

1. Install Ollama:

   • You need to install Ollama and download the respective model (in this case, Deepseek R1-1.5B). Follow the instructions on the Ollama website to get set up: Ollama Installation.

2. Create Virtual Environment:

   • Once Ollama and the model are installed, you can create a virtual environment and activate it in the terminal.

     conda create -n my_env python=3.10
     conda activate my_env

3. Install Dependencies:

   • In the virtual environment, you can install the necessary Python libraries using the following command:

     pip install -r requirements.txt

4. Run the App:

   • After installation, start the application by running the following command:

     streamlit run app.py

   • This will launch the application on your local server.

Code Explanation

This project consists of several components:

• app.py: The main file that uses Streamlit to create a user interface where you can interact with the DeepSeek model.
• requirements.txt: A file listing all the necessary libraries needed to run the app.
• Ollama Integration: The DeepSeek R1-1.5B model is loaded and processed using Ollama.
• Langchain: Manages the model's input-output chains to enhance user interaction.

Features

• Run DeepSeek R1-1.5B model locally.
• Use Streamlit to build a simple web app.
• Seamless integration of Langchain for better chaining and processing of responses from the model.

Acknowledgements

This project was inspired by a tutorial on YouTube that helped in understanding the integration of Ollama and Streamlit.

Contributing

Feel free to fork this project, open issues, or submit pull requests. Contributions are welcome!