Building End-to-End QA Chatbot Application using LLM  

I'm thrilled to share my latest project: an end-to-end QA chatbot application using Large Language Models (LLMs). This application leverages advanced AI to provide real-time, conversational responses to user queries, combining the power of natural language processing and interactive web interfaces.
Key Features:
Generative AI Integration: Utilizes Google's "gemini-1.5-flash" model for generating high-quality responses.
Streamlit Interface: Provides a clean, user-friendly interface for seamless interactions.
Dynamic Chat History: Maintains and displays a comprehensive chat history to enhance user experience.
Technical Stack:
Python: Core programming language for the backend.
Libraries: google-generative, Streamlit, python-dotenv.
Environment Management: Uses dotenv for secure API key handling.
Implementation Highlights:
Model Configuration: Securely loading and configuring the LLM using environment variables.
Real-Time Interaction: Handling user inputs and displaying bot responses dynamically.
State Management: Utilizing Streamlit's session state to maintain chat history across interactions.