Build a Product Recommendation Web App that uses AI to recommend furniture products and generate creative product descriptions. The application should be developed using FastAPI (backend) and React (frontend), integrating with a vector database. ( dataset will be provided) - The recommendation page will be a like back and forth conversation page. - The analytics page will be on a different route with analytics of all the items in the database. Project Requirements 1. Machine Learning (ML): Build a product recommendation model based on the given dataset. You may use any techniques you may find fit for this. 2. Natural Language Processing (NLP): Implement text analysis features to process products and group similar or related products. 3. Computer Vision (CV): Develop an image classification model that can identify product categories/types from the provided product images. 4. Generative AI(GenAI): Use a lightweight GenAI model(your choice)to generate creative product design descriptions to accompany recommended products on the frontend. 5. Vector Database: Store embeddings (text/image-based) in a vector database for semantic search and retrieval. You can use any vector database of your choice (Pinecone preferred). 6. Frontend (React): Create a simple yet functional frontend to display product recommendations once someone sends a prompt. These recommendations should come with generated descriptions and product images.. 7. Analytics Page: Add a page (using React routing) showing analytics on the current dataset. You may use a different database for analytics if you wish. Tech Stack Requirements - Backend: FastAPI - Frontend: React(any library/framework of your choice for UI) - VectorDB: Pinecone (preferred) or any other vector DB - ML Models: Any framework (e.g., scikit-learn, PyTorch, TensorFlow) - NLP: Any model/tool (e.g., spaCy, HuggingFace Transformers) - CV: CNN/ResNet/Vision Transformer/GenAI model (your choice) - GenAI: Any open-source or API-based lightweight model - Integration Framework: LangChain (must be used for GenAI or embedding-based tasks) Deliverables: You must submit a GitHub repository containing: 1. Frontend (React App) 2. Backend (FastAPI App) 3. Data Analytics Notebook (.ipynb) — must include: - Clear and descriptive comments explaining your reasoning for each major step 4. Model Training Notebook (.ipynb) — must include: - Clear and descriptive comments explaining your reasoning for each major step - Model performance evaluation (if applicable) 5. Instructions (README.md) — detailing setup, usage, and environment requirements.