Add backend and Enhance features (RAG ChatBot, Darkmode, Embedded Map, Login Page, Admin Dashboard, Dynamic Product View, Mongodb Dtatabase)

[PasinduGunathilake]

This pull request introduces several new backend utilities for environment configuration, chatbot integration, and database management, as well as improvements to product data and frontend settings. The most significant changes include the addition of a Gemini-powered chatbot backend, scripts for MongoDB user management, a new environment variable template, and updates to product categorization and images.

Backend and Environment Setup:

• Added .env.example as a template for environment variables, including configuration for Gemini API, Flask, MongoDB, and SMTP, with clear setup instructions.
• Introduced chatbot_backend.py, integrating Google Gemini via LangChain for a retrieval-augmented chatbot with session-based history and a structured JSON checkout flow.

Database Utilities:

• Added check_users.py for checking MongoDB connection and listing users, including guidance for creating an admin user.
• Added create_admin.py to create or update an admin user in MongoDB, securely hashing the password.

Product Data and Categorization:

• Updated data/products.json to standardize image paths, correct product categories (e.g., "Mobile phone", "Headphone", "Laptops"), and add new laptop products.
• Added data/mobile_phones.csv with detailed specs and pricing for a wide range of mobile phones.

Frontend and Configuration:

• Removed .vscode/settings.json to clean up project-specific editor settings.
• Removed all code from js/index.js, possibly for a future rewrite or deprecation.

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[PasinduGunathilake]

This pull request introduces several significant improvements and additions across the backend and data layers of the project, focusing on chatbot integration, database management utilities, data consistency, and environment configuration. The most important changes are summarized below:

Backend & Chatbot Integration:

• Added a new chatbot_backend.py module implementing a conversational retrieval-augmented generation (RAG) chatbot using Google Gemini via the LangChain framework. The chatbot is context-aware, supports multi-turn conversations, and enforces structured purchase flows with JSON output. It is configurable via environment variables and includes robust error handling.

Database Utilities:

• Introduced create_admin.py, a script to create or update an admin user in MongoDB, ensuring secure password handling and idempotent user creation.
• Added check_users.py, a utility script to verify MongoDB connectivity and list users in the database, providing clear diagnostics and guidance for setup.

Data & Static Assets Consistency:

• Updated data/products.json to fix image paths (now under ./static/images/...) and standardized product categories (e.g., "Mobile phone", "Headphone", "Laptops"). Also added several new laptop products.
• Added a comprehensive data/mobile_phones.csv file listing detailed specs and prices for a wide range of mobile phones, to support chatbot context and other features.

Environment & Project Configuration:

• Added a detailed .env.example template with documentation for all required environment variables, including API keys, Flask, MongoDB, and SMTP settings, to streamline local development and deployment.
• Removed the .vscode/settings.json file, likely to avoid editor-specific configuration in version control.

Frontend JavaScript:

• Removed the entire contents of js/index.js, which included navigation, popup, and UI interaction logic. This may be in preparation for a refactor or migration to a new frontend framework.

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These changes collectively enhance the backend chatbot capabilities, improve developer experience with better environment and database management, ensure data consistency, and prepare the codebase for further frontend or backend evolution.