NLP Use Case (NER, Langugae Detection, Sentiment Analysis)

Overview: This is Python code to use some of the NLP Usecase. First usecase is NER(Entity Extraction) Second usecase is Language Detection Third usecase is Sentiment/Emotion Analysis. All this three usecases are basedon NLP(Natural Language Processing.)

Technologies Used

• Python
• NLP
• OOPS
• Tkinter

Features

• 🔒 User Registration & Login — Secure user management with password masking via getpass.
• 🏷️ Named Entity Recognition (NER) — Extract entities of interest from user-provided text.
• 🌐 Language Translation — Converts English text to Gujarati using NLLB-200 model.
• 🙂 Sentiment Analysis — Detects sentiment within input text using fine-tuned LLaMA model.
• ⚡ Menu-driven interface — Intuitive navigation between features, with the ability to logout or exit.

Highlights:

Integrates nlpcloud client with different models for specialized tasks. Uses Python OOP principles for better structure and modularity. Supports GPU acceleration where available for faster processing. 💡 A simple yet functional console app demonstrating practical use of external NLP APIs with Python.

Project Demo

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