Chat with PDF Using Gemini - Streamlit App

This project enables users to upload PDF files, extract their content, and interact with the extracted data via a conversational interface powered by Google Generative AI (Gemini) and LangChain. Using Streamlit, this application allows you to ask questions and it will generate detailed answers

PDF Summarizer

This project is an AI-powered PDF summarizer developed to extract and summarize key elements from business documents. Using the Groq API and the LLaMA 3 (8B-8192) model, the tool focuses on identifying critical information. such as future growth prospects, key business changes.

LinkedIn Profile Summarizer

Developed an LLM-powered application using LangChain, called "Ice Breaker.The application captures user input (e.g., name) and scrapes LinkedIn profiles for relevant informatio and Generates personalized summaries based on designed standards for professional outreach. through NLP driven summary

sentimental model with TensorFlow and Transformers

Trained a transformer-based model to classify movie reviews as either positive or negative sentiments .By fine-tuning a pre-trained model from the transformers library, optimized the model’s performance on the sentimental analysis task and highlighted the ability to harness state-of-arts-models

PDF chatbot using OpenAI Assistants:

Have developed a conversational pdf chatbot using OpenAI's Assistants, integrating NLP for personalized, context-aware responses in real-time, handling complex multi-turn dialogues efficiently, and providing an interactive user experience."

Implementing Latent Dirichlet Allocation (LDA) for coherence score calculation

This project is dedicated to implementing Latent Dirichlet Allocation (LDA) for coherence score calculation, serving as the primary objective of the project. the implementation follows a systematic approach, starting with data preprocessing and moving on to LDA model training and evaluation.

Stock Price Prediction And Forecasting Using Stacked LSTM-

In this project i have utilized Stacked Long Short-Term Memory (LSTM) neural networks to forecast stock prices. Leveraging LSTM, a type of recurrent neural network (RNN), I developed a model capable of learning intricate patterns within historical stock price data