{"id":30939202,"url":"https://github.com/alokthedataguy/nlp_projects","last_synced_at":"2026-05-18T03:32:07.838Z","repository":{"id":288614103,"uuid":"965648044","full_name":"AlokTheDataGuy/nlp_projects","owner":"AlokTheDataGuy","description":"Multiple chatbots and NLP-based projects recreated which were completed during my internship. 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Each project demonstrates different aspects of AI application development, from text summarization to multilingual chatbots.\n\n## Projects Overview\n\n### 1. Extractive Summarization Tool\nAn intelligent text summarization tool powered by BERT that automatically extracts the most important sentences from documents to create concise summaries.\n\n![Summarization Tool](1.%20extractive-summarization_tool/screenshot.png)\n\n**Key Technologies**: Flask, React, PyTorch, Hugging Face Transformers, NetworkX, PyPDF2, pdfplumber, TailwindCSS, Vite\n\n### 2. Chatbot Analytics Dashboard\nA comprehensive system for chatbot interactions with an analytics dashboard to track user engagement, satisfaction ratings, and conversation topics.\n\n![Analytics Dashboard](2.%20chatbot-analytics-dashboard/screenshots/dashboard_2.png)\n\n**Key Technologies**: Python, Ollama (Cogito:8b), Plotly, Dash, Pandas, SQLite, Chart.js, Bootstrap\n\n### 3. Article Generator with 3 Different LLMs\nA tool for generating articles using different open-source LLMs (Mistral, Qwen2.5, Llama3.1) and comparing their performance.\n\n![Article Generator](3.%20article-generator-with-3-different-llms/screenshots/web_interface.png)\n\n**Key Technologies**: Python, Ollama, Flask, NLTK, spaCy, Matplotlib, scikit-learn, TextBlob, Jinja2\n\n### 4. Multi-Modal Chatbot\nA chatbot that can understand both text and image inputs, providing seamless multi-modal interactions.\n\n![Multi-Modal Chatbot](4.%20multi-modal-chatbot/Screenshots/Image_Understanding.png)\n\n**Key Technologies**: FastAPI, React, Ollama (llama3.2-vision:11b, qwen2.5:7b), Pillow, Material-UI, Axios\n\n### 5. Medical Q\u0026A Chatbot\nA medical question-answering chatbot that provides appropriate and relevant medical information using retrieval-based methods and LLMs.\n\n![Medical Chatbot](5.%20medical-qa-chatbot/screenshot.png)\n\n**Key Technologies**: FastAPI, React, Ollama (Meditron 7B, Llama 3.1 8B), MedQuAD Dataset, FAISS, Pandas, NumPy, Tailwind CSS, TypeScript\n\n### 6. Dynamic News Chatbot\nAn intelligent news chatbot that automatically updates its knowledge base with fresh articles and provides conversational interactions powered by Llama 3.1 8B.\n\n![Dynamic News Chatbot](6.%20dynamic-news-chatbot/screenshots/chat.png)\n\n**Key Technologies**: Streamlit, Python, Ollama (Llama 3.1 8B), NewsAPI, newspaper3k, BeautifulSoup, ChromaDB, SentenceTransformers\n\n### 7. Chatbot Sentiment Analysis\nA sophisticated chatbot with sentiment analysis capabilities to recognize and respond appropriately to customer emotions.\n\n![Sentiment Analysis](7.%20chatbot-sentiment-analysis/screeenshots/chatbot.png)\n\n**Key Technologies**: FastAPI, React, Hugging Face Transformers (RoBERTa), Ollama (Llama3.1:8b), Material-UI, Chart.js, Pydantic, WebSockets\n\n### 8. arXiv-CS Expert Chatbot \nCS Expert Chatbot powered by an open-source foundation LLM (Llama 3) + Retrieval Augmented Generation (RAG).\n\n![CS Expert Chatbot](8.%20arvix-cs-expert-chatbot/screenshots/Chat_Interface.png)\n\n**Key Technologies**: Python, LLama3, ChromaDB, Streamlit, LangChain, Sentence-Transformers, Plotly, NetworkX, seaborn, wordcloud \n\n### 9. Multi-Lingual Chatbot\nA multilingual chatbot supporting English, Hindi, Bengali, and Marathi with automatic language detection, translation, and transliteration capabilities.\n\n![Multi-Lingual Chatbot](9.%20multi-lingual%20chatbot/screenshot9.png)\n\n**Key Technologies**: FastAPI, React, fastText, Ollama (LLaMA3.1-8B), IndicBART, IndicXlit, styled-components, Pydantic, Uvicorn\n\n\n## Skills Demonstrated\n\nThrough these projects, I've demonstrated proficiency in:\n\n- **Large Language Model Integration**: Implementing and fine-tuning various LLMs for specific tasks\n- **Retrieval-Augmented Generation (RAG)**: Creating systems that combine knowledge bases with generative AI\n- **Multi-Modal AI**: Building applications that process both text and image inputs\n- **Natural Language Processing**: Implementing text summarization, sentiment analysis, and language detection\n- **Full-Stack Development**: Creating end-to-end applications with modern frontend and backend technologies\n- **API Development**: Designing and implementing RESTful APIs\n- **Database Design**: Working with both traditional and vector databases\n- **Data Processing**: Extracting, transforming, and analyzing structured and unstructured data\n- **UI/UX Design**: Creating intuitive user interfaces for complex AI applications\n- **System Architecture**: Designing scalable, modular systems with clear separation of concerns\n\n## Learning Outcomes\n\nThrough this internship, I've gained valuable experience and insights:\n\n- **Practical AI Application**: Moving beyond theoretical knowledge to build real-world AI applications\n- **Technical Integration**: Combining multiple technologies and frameworks into cohesive systems\n- **Problem-Solving**: Addressing challenges in AI implementation, from model selection to deployment\n- **Performance Optimization**: Balancing model accuracy with computational efficiency\n- **User-Centered Design**: Creating AI systems that are accessible and useful to end-users\n- **Ethical AI Development**: Considering privacy, bias, and ethical implications in AI applications\n- **Project Management**: Planning, executing, and documenting complex technical projects\n- **Continuous Learning**: Adapting to rapidly evolving AI technologies and methodologies\n\n## Getting Started\n\nEach project has its own README with specific setup instructions. Generally, the projects follow this pattern:\n\n1. Set up a Python environment\n2. Install dependencies from requirements.txt\n3. Install Ollama and pull required models (for LLM-based projects)\n4. Set up the backend server\n5. Set up the frontend application\n\n## License\n\nThis repository is for demonstration purposes. All projects are provided under the MIT License.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falokthedataguy%2Fnlp_projects","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Falokthedataguy%2Fnlp_projects","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falokthedataguy%2Fnlp_projects/lists"}