Gemini-A-True-Gem

Welcome to my learning journey with Gemini Pro, Google's next-generation AI model. This repository serves as a documentation hub where I'll be sharing my experiences, key takeaways, and experiments as I explore the capabilities of Gemini Pro.

🚀 Project Overview

This repository aims to document my progress in learning and experimenting with Gemini Pro. I'll be focusing on understanding its architecture, potential use cases, and integrating it into various AI and NLP-based applications.

🧑‍💻 Learning Objectives

1. Deep Dive into Gemini Pro: Understand the unique features and architecture that make Gemini Pro a powerful AI model.
2. Comparison with Other Models: Explore how Gemini Pro stands out from other models like GPT, LLaMA, etc.
3. Hands-on Experiments: Implement real-world use cases, including chatbot development, content generation, and AI-driven solutions.
4. Documenting Challenges and Solutions: Capture the challenges faced during implementation, along with their solutions and best practices.

📂 Repository Structure

• apps/: Source code for various projects and use cases involving Gemini Pro.
• docs/: Contains detailed notes, explanations, and insights from different stages of the learning process.
• notebooks/: Jupyter Notebooks with code examples, experiments, and model integration tests.
• data/: Datasets used during experiments (where applicable).

📜 Key Topics Covered

• Introduction to Gemini Pro architecture
• Use case exploration: NLP, content generation, chatbots
• Model fine-tuning and optimization
• Real-world applications and challenges

💻 List of Projects

Simple Chatbot using Gemini Pro API

- Build a basic chatbot leveraging the Gemini Pro API for natural language understanding and response generation. - [Project Code](app1/1_app.py) - Folder: `app1/`

Gemini Pro Content Creator using Langchain Framework

• Implemented Gemini-pro API Chatbot using the Langchain framework.
• Learned how to monitor it through Langsmith.
• Project Code
• Folder: app2/

Context-Based Approach for Question Answering

• Implement a context-based model that can answer complex queries using Gemini Pro’s advanced NLP capabilities.
• Project Code
• Folder: app3/

Multi-Purpose Streamlit Web App

• Implement an app having 4 below targets-

  • Chatbot
  • Image Captioning
  • Text Embedding
  • Ask Any Query
  
  
  
  
  
  
  
  
  
  
  
  

• took help of yt video by Siddhardhan.

• [Project Code](multi purpose streamlit app/main.py)

• Folder: multi purpose streamlit app/

Fine-Tuning Gemini Pro for Custom Applications

• Fine-tune the Gemini Pro model on specific datasets to optimize performance for custom use cases like student support chatbots.

Sentiment Analysis with Gemini Pro

• Analyze and classify sentiments from text inputs using the pre-trained Gemini Pro model.

Content Generation Tool

• Develop a content generation tool using Gemini Pro’s API for creating articles, summaries, or creative writing pieces.

More as we go.....

💡 How to Use This Repository

Feel free to explore the notes, code, and documentation.