Developed a Python-based API that processes customer reviews, performs sentiment analysis using a Large Language Model, and returns structured results.

Scalable Django DRF backend for advanced NLP: Sentiment Analysis & Text Summarization via API. Features secure JWT auth, user management, history, Redis caching, and Docker. Integrates LLMs (Gemini) with future local AI support

Using Groq API to do sentiment analysis and flask for handling the application

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Task: Sentiment Analysis API with LLM Integration

Implemented advanced LLM applications using Ollama, leveraging CLI commands, REST API, Python integration, and Misty API to perform summarization, sentiment analysis, and image captioning with multimodal models.

AI-powered sentiment and emotion analysis tool using React, Flask, MongoDB, and BERT. Supports Reddit/Bugzilla input, LLM integration (ChatGPT/Gemini), emotion detection, and keyword tracking with a redesigned UI and optimized API handling.

This project uses advanced LLMs and financial tools to predict stock prices and automate trading. It integrates sentiment analysis, market trends, fraud detection, and real-time action insights to generate smart buy/sell signals, with future support for brokerage API trading.