Projects
MCQ Generator Web Application!
🚀Project Overview: I developed a Streamlit-based web application that allows users to input any text and generate MCQs using the Gemini model.
🌟 Features:
User Input: A text area for users to input the text for which they want to generate MCQs.
MCQ Quantity: An option to choose the number of MCQs (5, 10, 15 or 20).
Real-time Generation: Instant MCQ generation using the Gemini model.
🛠️ Tools & Technologies Used:
Streamlit: For creating an interactive and user-friendly web application.
Gemini Model: For generating the MCQs based on the provided text.
Querying CSVs and Plotting Graphs with LLMs!
🔧 Project Overview: I developed a Streamlit web application that uses Large Language Models (LLMs) to help you query your CSV files and generate visualizations effortlessly. 📊
🔥 Key Features:
- Upload your CSV file and view the data directly in the app.
- Query data using natural language for tasks like statistical analysis and summarization.
- Generate visualizations such as histograms, bar charts, scatter plots, and more — all from a simple query.
- Automated code execution: LLM-generated Python code is executed to visualize the data, making it a seamless experience for non-technical users.
💡 How It Works:
1. Upload your CSV 📄
2. Ask natural language questions like "What’s the average age?" or "Plot a bar chart for the department column."
3. Get instant responses and visualizations! 📈
This project is built using:
- Streamlit for the web interface
- LangChain and Google Generative AI (Gemini-1.5-pro) for LLM capabilities
- Pandas for data manipulation
- Matplotlib for visualizations
Multi Language Invoice Extractor!
Project Overview: I recently completed building a Multi Language Invoice Extractor using Streamlit and Google Gemini LLM for handling invoice data. The application allows users to upload invoice images and interact with an AI chatbot to extract, analyze, and answer queries about the invoice details.
Tech Stack & Features:
Streamlit: Used to create an interactive and user-friendly interface.
Google Gemini LLM: Leveraged the power of large language models to understand and extract information from multilingual invoices.
File Upload Functionality: Allows uploading of invoices in image formats (JPG, PNG) and dynamically displays the uploaded image.
AI-driven Responses: Users can ask questions about the invoice, and the app provides relevant responses based on the image and prompts.
💡 Use Case: Imagine having invoices in different languages and formats this app simplifies the task of analyzing them by making the extraction process smooth and efficient through conversational AI.
