Professional Internship Projects

This repository contains a collection of AI and NLP projects developed during my professional internship. Each project demonstrates different aspects of AI application development, from text summarization to multilingual chatbots.

Projects Overview

1. Extractive Summarization Tool

An intelligent text summarization tool powered by BERT that automatically extracts the most important sentences from documents to create concise summaries.

Key Technologies: Flask, React, PyTorch, Hugging Face Transformers, NetworkX, PyPDF2, pdfplumber, TailwindCSS, Vite

2. Chatbot Analytics Dashboard

A comprehensive system for chatbot interactions with an analytics dashboard to track user engagement, satisfaction ratings, and conversation topics.

Key Technologies: Python, Ollama (Cogito:8b), Plotly, Dash, Pandas, SQLite, Chart.js, Bootstrap

3. Article Generator with 3 Different LLMs

A tool for generating articles using different open-source LLMs (Mistral, Qwen2.5, Llama3.1) and comparing their performance.

Key Technologies: Python, Ollama, Flask, NLTK, spaCy, Matplotlib, scikit-learn, TextBlob, Jinja2

4. Multi-Modal Chatbot

A chatbot that can understand both text and image inputs, providing seamless multi-modal interactions.

Key Technologies: FastAPI, React, Ollama (llama3.2-vision:11b, qwen2.5:7b), Pillow, Material-UI, Axios

5. Medical Q&A Chatbot

A medical question-answering chatbot that provides appropriate and relevant medical information using retrieval-based methods and LLMs.

Key Technologies: FastAPI, React, Ollama (Meditron 7B, Llama 3.1 8B), MedQuAD Dataset, FAISS, Pandas, NumPy, Tailwind CSS, TypeScript

6. Dynamic News Chatbot

An intelligent news chatbot that automatically updates its knowledge base with fresh articles and provides conversational interactions powered by Llama 3.1 8B.

Key Technologies: Streamlit, Python, Ollama (Llama 3.1 8B), NewsAPI, newspaper3k, BeautifulSoup, ChromaDB, SentenceTransformers

7. Chatbot Sentiment Analysis

A sophisticated chatbot with sentiment analysis capabilities to recognize and respond appropriately to customer emotions.

Key Technologies: FastAPI, React, Hugging Face Transformers (RoBERTa), Ollama (Llama3.1:8b), Material-UI, Chart.js, Pydantic, WebSockets

8. arXiv-CS Expert Chatbot

CS Expert Chatbot powered by an open-source foundation LLM (Llama 3) + Retrieval Augmented Generation (RAG).

Key Technologies: Python, LLama3, ChromaDB, Streamlit, LangChain, Sentence-Transformers, Plotly, NetworkX, seaborn, wordcloud

9. Multi-Lingual Chatbot

A multilingual chatbot supporting English, Hindi, Bengali, and Marathi with automatic language detection, translation, and transliteration capabilities.

Key Technologies: FastAPI, React, fastText, Ollama (LLaMA3.1-8B), IndicBART, IndicXlit, styled-components, Pydantic, Uvicorn

Skills Demonstrated

Through these projects, I've demonstrated proficiency in:

• Large Language Model Integration: Implementing and fine-tuning various LLMs for specific tasks
• Retrieval-Augmented Generation (RAG): Creating systems that combine knowledge bases with generative AI
• Multi-Modal AI: Building applications that process both text and image inputs
• Natural Language Processing: Implementing text summarization, sentiment analysis, and language detection
• Full-Stack Development: Creating end-to-end applications with modern frontend and backend technologies
• API Development: Designing and implementing RESTful APIs
• Database Design: Working with both traditional and vector databases
• Data Processing: Extracting, transforming, and analyzing structured and unstructured data
• UI/UX Design: Creating intuitive user interfaces for complex AI applications
• System Architecture: Designing scalable, modular systems with clear separation of concerns

Learning Outcomes

Through this internship, I've gained valuable experience and insights:

• Practical AI Application: Moving beyond theoretical knowledge to build real-world AI applications
• Technical Integration: Combining multiple technologies and frameworks into cohesive systems
• Problem-Solving: Addressing challenges in AI implementation, from model selection to deployment
• Performance Optimization: Balancing model accuracy with computational efficiency
• User-Centered Design: Creating AI systems that are accessible and useful to end-users
• Ethical AI Development: Considering privacy, bias, and ethical implications in AI applications
• Project Management: Planning, executing, and documenting complex technical projects
• Continuous Learning: Adapting to rapidly evolving AI technologies and methodologies

Getting Started

Each project has its own README with specific setup instructions. Generally, the projects follow this pattern:

1. Set up a Python environment
2. Install dependencies from requirements.txt
3. Install Ollama and pull required models (for LLM-based projects)
4. Set up the backend server
5. Set up the frontend application

License

This repository is for demonstration purposes. All projects are provided under the MIT License.