https://github.com/alokthedataguy/internship_projects
Multiple chatbots and NLP-based projects completed during my internship. Each project demonstrates different aspects of AI application development, from text summarization to multilingual chatbots.
https://github.com/alokthedataguy/internship_projects
ai chatbot-analytics chatbots fullstack-development internship-project internship-task internships llm ml multi-modal multilingual-nlp nlp rag sentiment-analysis
Last synced: 2 months ago
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Multiple chatbots and NLP-based projects completed during my internship. Each project demonstrates different aspects of AI application development, from text summarization to multilingual chatbots.
- Host: GitHub
- URL: https://github.com/alokthedataguy/internship_projects
- Owner: AlokTheDataGuy
- Created: 2025-04-13T16:11:32.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-07-22T08:44:51.000Z (3 months ago)
- Last Synced: 2025-07-22T10:39:53.567Z (3 months ago)
- Topics: ai, chatbot-analytics, chatbots, fullstack-development, internship-project, internship-task, internships, llm, ml, multi-modal, multilingual-nlp, nlp, rag, sentiment-analysis
- Language: Python
- Homepage:
- Size: 10.7 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# 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.