Techietalks AI - Smart Chat Assistant (Enhanced)

Welcome to the Techietalks AI repository! This project is an enhanced version of a smart AI-powered chat assistant built using Streamlit, DeepSeek API, and Pydantic_AI. It allows users to interact with an AI assistant that can answer questions conversationally and concisely, while also maintaining a history of the conversation for context.

This repository contains all the necessary files to set up and run the chat assistant locally or in a Docker container. Below, you'll find a detailed explanation of the project and instructions to get started.

📁 Repository Structure

Here’s a breakdown of the files in this repository:

1. .DS_Store: A macOS-specific file that stores folder attributes (e.g., icon positions). You can ignore this file.
2. .env: A file to store environment variables, such as API keys. Important: Add your DEEPSEEK_API_KEY here to authenticate with the DeepSeek API.
3. .gitignore: Specifies files and folders that Git should ignore (e.g., .env to avoid exposing sensitive information).
4. Dockerfile: Contains instructions to build a Docker image for the application. It sets up a Python environment, installs dependencies, and runs the Streamlit app.
5. README.md: This file! It provides an overview of the project and instructions for setup.
6. app.py: The main application file. It contains the code for the Streamlit-based chat interface and integrates with the DeepSeek API. This version also includes conversation history for better context.
7. docker-compose.yml: A configuration file to run the application using Docker Compose. It maps ports, loads environment variables, and sets up volumes.
8. requirements.txt: Lists all Python dependencies required to run the application.
9. sree.txt: A placeholder text file (likely for personal notes or testing).

🚀 How It Works

This project uses the following technologies:

• Streamlit: A framework for building interactive web applications with Python. It powers the chat interface.
• DeepSeek API: An AI model that provides conversational responses. The app authenticates with the API using an API key stored in the .env file.
• Pydantic_AI: A library that helps structure and validate AI responses.
• Rich: A library used for pretty-printing debug information (e.g., conversation history).

When you type a question into the chat interface, the app sends it to the DeepSeek API, processes the response, and displays it in a conversational format. It also maintains a history of the conversation, allowing the AI to provide more context-aware answers.

🛠️ Setup Instructions

Follow these steps to set up and run the project:

Prerequisites

1. Python 3.11: Ensure Python is installed on your system.
2. Docker (optional): If you want to run the app in a container.
3. DeepSeek API Key: Obtain an API key from DeepSeek.

Steps

1. Clone the Repository: bash git clone https://github.com/schogini/techietalksai.git cd techietalksai/0003

2. Set Up Environment Variables:

   • Create a .env file in the project directory.
   • Add your DeepSeek API key: plaintext DEEPSEEK_API_KEY=your_api_key_here

3. Install Dependencies:

   • If running locally: bash pip install -r requirements.txt
   • If using Docker, skip this step.

4. Run the Application:

   • Locally: bash streamlit run app.py Open your browser and navigate to http://localhost:8501.
   • Using Docker: bash docker-compose up --build Open your browser and navigate to http://localhost:8502.

📄 License

This project is open-source and available under the MIT License. Feel free to use, modify, and distribute it as needed.

📧 Contact

For AI consultancy, training, and development, contact Schogini Systems Private Limited at https://www.schogini.com.

👨‍💻 Author

Sreeprakash Neelakantan
- Website: https://www.schogini.com
- GitHub: https://github.com/schogini/techietalksai.git

Enjoy using the Techietalks AI Smart Chat Assistant! If you have any questions or feedback, feel free to reach out. 😊