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BravoTrevor/Online-store-chatbot

Domain:

natural language processing

Record type:

software
Creator:
Bra
Host:
AI-powered chatbot for an African e-commerce platform (inspired by Jumia/Kilimall). # Online-store-chatbot AI-powered chatbot for an African e-commerce platform (inspired by Jumia/Kilimall). This chatbot will: Help users search for products. Provide product recommendations. Answer FAQs (e.g., shipping, returns). Track orders (mock data). This is a portfolio project to showcase your AI and full-stack development skills, with a focus on African markets. Sections of the Project 1. Kenyan Product Dataset What it is: A realistic mock catalog of products sold in Kenya. Details: 50-100 products with names, prices, descriptions, ratings, reviews, and images. Includes Swahili-English mix for authenticity. Products have variants (e.g., colors, sizes) and mock ratings/reviews. How it fits: This dataset is the brain of the chatbot. It’s where the chatbot gets product info to answer user queries. 2. Chatbot Backend (Python) What it is: The logic that powers the chatbot. Details: Built with Flask (a Python web framework). Handles user messages, processes them, and generates responses. Uses rule-based logic (e.g., if the user says “search,” it looks up products). Can be extended with NLP (e.g., to understand Swahili/English mix). How it fits: The backend is the engine that connects the user interface (UI) to the product dataset. 3. Chatbot Frontend (Web UI) What it is: The user interface where users interact with the chatbot. Details: A floating chat button on the bottom-right of the webpage. A chat window with: Message bubbles (user messages on the right, bot replies on the left). A typing box and Send button. Built with HTML/CSS/JavaScript. How it fits: This is the face of the chatbot. Users type here, and the chatbot’s replies appear here. 4. Integration (Frontend + Backend) What it is: The connection between the web UI and the Python backend. Details: The frontend sends user messages to the backend via API calls (using fetch in JavaScript). The backend processes the message, queries the dataset, and sends a reply back to the fron …