Passionate about smart Technologies and AI enthusiast

Krishna Varma  • Peerlist

This project focuses on classifying different species of iris flowers based on their physical features. Using logistic regression, a machine learning technique, the model predicts the species of an iris flower from measurements such as sepal length, sepal width, petal length, and petal width.

The project is deployed on Render, a cloud platform, and utilizes Flask, a lightweight web framework, to create a user-friendly web interface. Users can input flower measurements and receive instant predictions about the flower's species

Iris Flower Classification

Chat with PDF is an innovative web application that transforms user interaction with PDF documents. By leveraging advanced AI technologies, users can easily upload PDF files, ask questions, and receive accurate, context-aware answers in a conversational format. This project simplifies information retrieval, making it more intuitive and user-friendly.

Key Features:
PDF Upload: Seamlessly upload multiple PDF documents for interaction.
Text Extraction: Efficiently extract text from uploaded PDFs for engagement.
Text Chunking: Divide text into manageable chunks for improved processing.
Vector Storage: Create and store numerical representations (embeddings) in a FAISS vector database for quick retrieval.
Conversational AI: Ask questions via text or voice, generating answers using LangChain’s AI capabilities.
Resource Links: Access additional resources, such as YouTube videos and Wikipedia articles, to enhance understanding.
Summarization: Quickly summarize selected PDFs for faster content comprehension.
Technologies Used:
Streamlit: Framework for rapid development of interactive applications.
LangChain: Library for creating the conversational AI component.
Google Generative AI: For embedding and understanding PDF text.
FAISS: Efficient similarity search and clustering for quick retrieval.
Speech Recognition: Enables voice input for improved accessibility.
Why It Matters:
The Chat with PDF project enhances document management by making information retrieval more accessible and intuitive, benefiting students, researchers, and professionals by transforming how they interact with and extract knowledge from documents.