AI Assistant — Flask + Groq (LLaMA 3)🚀🚀✨✨✨

A simple, lightweight AI chatbot built with Flask and the Groq LLaMA models. This project demonstrates how to integrate an LLM API into a small web app, design prompts, and build a minimal feedback loop.

Features🚀🚀✨✨

• Ask questions and get AI-powered responses
• Uses Groq's fast LLaMA models
• Clean Flask UI with background & favicon
• Feedback system (yes/no saved to feedback.txt)
• Secure-by-design: API key is NOT stored in the repository

Project structure

AIASSISTANT/ ├── app.py
├── assistant_logic.py (NOT included in GitHub — contains API key)
├── prompts.py
├── requirements.txt
├── feedback.txt (ignored by Git)
├── static/
│ ├── bg.png
│ └── favicon.png
└── templates/
└── index.html
└── .gitignore

Installation & Setup🔥🔥🔥🔥🔥🔥🔥

1. Clone the repository

   git clone https://github.com/YOUR_USERNAME/AIASSISTANT.git
   cd AIASSISTANT
   

2. Create a virtual environment (recommended)

   python -m venv venv
   source venv/bin/activate   # macOS / Linux
   venv\Scripts\activate      # Windows
   

3. Install dependencies

   pip install -r requirements.txt
   

4. Create assistant_logic.py

   assistant_logic.py is intentionally excluded from the repository to protect your API key. Create it manually in the project folder. Example:

   from groq import Groq
   
   client = Groq(api_key="YOUR_API_KEY_HERE")
   
   def answer_question(user_input):
       response = client.chat.completions.create(
           messages=[{"role": "user", "content": user_input}],
           model="llama-3.2-1b-preview"
       )
       return response.choices[0].message["content"]

   Replace "YOUR_API_KEY_HERE" with your Groq API key.

   Optional — use environment variables instead:

   import os
   from groq import Groq
   
   client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
   
   def answer_question(user_input):
       response = client.chat.completions.create(
           messages=[{"role": "user", "content": user_input}],
           model="llama-3.2-1b-preview"
       )
       return response.choices[0].message["content"]

Run the project

Start the Flask app:

python app.py

Open the app in your browser: http://127.0.0.1:5000/

Feedback system

After each AI response the UI asks: Was this helpful? (yes / no)

Responses are appended to feedback.txt (this file is ignored by Git to avoid storing user data in the repo).

Security notes

• assistant_logic.py is intentionally excluded using .gitignore — do not commit your API keys.
• Prefer storing secrets in environment variables or a secrets manager.
• Do NOT publish your Groq API key in public repos.

Requirements

• Python 3.8+
• Packages in requirements.txt (example)
  • flask
  • groq

Install them with:

pip install -r requirements.txt

Customization & Extensions

• Replace the LLM model or tweak prompts in prompts.py.
• Add authentication if exposing to the public internet.
• Expand feedback handling (store in a DB, add analytics).
• Add UI/UX improvements and screenshots in this README.

Author

Priyadharshini V
B.E CSE