CodeGirlsInc/SMALDA

AI Summary: The issue requests the implementation of a Solidity smart contract, `DocumentVerification`, for a decentralized land registry. This contract will manage the storage, verification, and AI-powered risk assessment of land documents, integrating with an existing `ILandRegistry` and IPFS. Key features include role-based access control using OpenZeppelin, manual and AI-assisted verification workflows, granular document access management, and risk tracking.

AI Summary: This issue requires adding unit tests for the upload and chatbot UI components using Jest and React Testing Library. The tests should cover key user interactions such as button clicks, input handling, and file selection. Mock data will be utilized, and no API integration is involved, making it a focused UI testing task.

AI Summary: Develop a NestJS module for managing land ownership documents. This includes creating a module, controller, and service to handle file uploads (with MIME type validation), storing file paths and metadata in a PostgreSQL database via TypeORM, and enabling document retrieval.

AI Summary: Develop a risk analysis module for a land ownership document processing system. This involves creating a module, controller, and service to analyze uploaded documents for risk keywords (e.g., "dispute", "incomplete") and return a risk report with a risk level and summary. Initial implementation can use static rules or mocked AI.

AI Summary: Develop an audit logging module for a land ownership risk detection tool. This involves creating a module to record user actions such as document uploads, risk flagging, and review approvals, storing the user, action, and timestamp for each log entry.

AI Summary: Develop a notification module for a land ownership risk detection tool. This involves creating an email service within the module and triggering email or in-app notifications when a document is flagged as risky or a review is approved. The implementation should utilize a queue or background task to handle notifications asynchronously.

AI Summary: Develop an admin module for a land ownership risk detection tool. This module should include user management, access control (admin-only), and the ability to view all flagged risks and document statistics. Controllers and services for the admin module need to be created.

AI Summary: Develop a reporting module for an AI-powered land ownership risk analysis tool. This module should generate downloadable PDF and CSV reports containing document analysis results, risk assessments, status updates, and timestamps.

AI Summary: Implement a geo-tagging module for a land ownership risk detection tool. This involves creating a module to extract GPS coordinates from documents using regular expressions or AI, and then storing these coordinates in a database.

AI Summary: Develop a module that allows human reviewers to review AI-flagged risks in land ownership documents. This involves creating a reviewer-only endpoint for commenting and approving/rejecting risks, storing reviewer decisions and metadata, and updating the document status accordingly.

AI Summary: Develop a 'feedback' module allowing users to submit feedback on AI-flagged risks within land ownership documents. This feedback should be linked to the specific document and user, and stored for future model improvement.

AI Summary: Develop a 'document-history' module for an AI-powered land ownership risk detection tool. This module should track all versions of uploaded documents, maintain a changelog of identified risks and their status, and provide an endpoint for viewing the document's version history. Version numbers must be tracked, and the changelog should include risk details and status updates.

AI Summary: Integrate an OCR (Optical Character Recognition) module, such as Tesseract, into the land ownership risk detection tool. The module should extract clean text from uploaded image or PDF files and store the extracted text alongside the original document. A mock implementation can be used initially.

AI Summary: Develop a tagging module for a land ownership document risk analysis tool. This involves adding a tags field to the document entity, implementing Create, Read, Update, and Delete (CRUD) operations for tags, and enabling filtering documents by tag in queries.

AI Summary: Develop a 'property-owner' module for an AI-powered land ownership risk detection tool. This involves creating an owner entity with relationships to land documents, building CRUD (Create, Read, Update, Delete) endpoints for managing owner data, and ensuring support for both individual and corporate owner profiles.

AI Summary: Develop an activity tracker module that logs user actions (logins, uploads, reviews, downloads) with timestamps, stores this data in a PostgreSQL database, and allows querying by user ID.

AI Summary: Create a new module to manage user roles (Admin, Analyst, Reviewer, User) and implement role-based access control (RBAC) for routes within the land ownership risk detection application. This involves defining permissions for each role and ensuring only authorized users can access specific functionalities.

AI Summary: Develop an `ml-training` module to collect, manage, and export labeled data for improving the land ownership risk analysis AI. This involves creating a system to store user feedback, tags, and human reviews, allowing for data export in JSON format and implementing CRUD operations for dataset records.

AI Summary: Implement an access logging module for an AI-powered land ownership risk detection tool. This involves capturing API access details (route, IP, user, method), storing them in a dedicated database table (`access_logs`), and ensuring the logs can be filtered by user and time.

AI Summary: Develop a 'document-comments' module for an AI-powered land ownership risk detection tool. This module should enable internal users to add, edit, delete, and view comments on documents, ideally with threaded replies to facilitate discussion. Each comment must include author and timestamp information.

AI Summary: Develop a module that generates a chronological timeline of events for a given land ownership case ID. The timeline should incorporate data from uploaded documents, court hearings, and other actions, and be presented as an array of objects with type, date, and relevant details.

AI Summary: Develop a module for an AI-powered land ownership risk detection tool. This module should analyze structured land metadata to identify red flags such as non-original Starknet ownership, duplicate land claims, and expired documents.

AI Summary: Develop a module for the land ownership risk detection tool that analyzes mock court case data (JSON or database records) to generate statistical insights such as win rates by region and average time-to-resolution. The output should be aggregated statistics.

AI Summary: Develop a module for the SMALDA land ownership risk analysis tool that allows users to self-generate, manage (set expiration), and revoke their own API keys for accessing the API. This ensures secure access control for the application.

AI Summary: Develop a module that generates a thumbnail (JPG or PNG) from the first page of uploaded PDF documents. This thumbnail will be used as a preview image in the frontend of a land ownership risk analysis tool. The module should utilize a suitable library such as `pdf-lib`, `pdf-poppler`, or `ImageMagick`.

AI Summary: Develop a module that validates land coordinates provided in Nigerian land ownership documents. This involves checking if the coordinates are within Nigeria's legal boundaries, are correctly formatted, and fall within the Earth's geographical limits. The module should handle edge cases and various coordinate formats.

AI Summary: Develop a notification dispatcher module that sends alerts via email (and potentially SMS in the future). The module should accept a payload containing recipient details, subject, and body, and dispatch the notification appropriately. Email functionality should be mocked for now; no external dependencies are required.

AI Summary: Create a data faker/seeder module to generate realistic dummy data (land dispute cases, users, documents) for testing and development of an AI-powered land ownership risk detection tool. The module should allow for random data generation, manual overrides of specific fields, and JSON export/import functionality.

AI Summary: Develop a module for an AI-powered land ownership risk detection tool that tracks changes between versions of uploaded land documents. The module should store diffs between old and new versions and provide a changelog to show the changes made.

AI Summary: Design a Natural Language Processing (NLP) module that normalizes variations of legal terms found within land ownership documents. The module should standardize different spellings, capitalization, and abbreviations of legal terms to ensure consistent data processing for the risk detection system.

AI Summary: Develop a module that uses Nominatim or OpenStreetMap APIs to determine the administrative region (state/province/etc.) from provided GPS coordinates or an address. This information will be used to geographically tag land and case data within a land ownership risk detection tool.

AI Summary: Develop a module that generates unique, human-readable, tamper-proof case IDs (e.g., `SMLD-KD-2025-00X13`) for a land ownership risk detection system. The module should accept a region and year as input, avoid ID collisions, and use short UUIDs internally for efficient storage and retrieval.

AI Summary: Develop a simple scheduler/job queue module for an AI-powered land ownership risk detection tool. This module should handle background tasks such as deleting temporary files and sending scheduled reports, using a library like `bull` or `Agenda.js`.

AI Summary: Develop a module that extracts metadata (file size, MIME type, creation date) from various document files (provided as a Buffer or file path) and returns it as a JSON object. The module should not process the document content itself, only the file metadata.

AI Summary: Develop a reusable logging module for an AI-powered land ownership risk detection tool. The module should record all API calls and significant system actions, including timestamps, user information, and relevant metadata, and store these logs in a database. The module should expose a function with the signature `logAction(userId, actionType, metadata)`. This module will be used across all services within the application.

AI Summary: Design and implement a responsive landing page for SMALDA, an AI-powered land ownership risk detection tool. The page should include a hero section, sections highlighting core features, and a clear call to action. Placeholder content is acceptable.

AI Summary: Integrate an Optical Character Recognition (OCR) service into the land ownership risk detection tool. The service should accept image or PDF files as input, extract text content, and log key sections such as parties involved, location details, and signatures. The API endpoint for this service will be `POST /documents/ocr`. A mock data fallback is optional.

AI Summary: Implement Role-Based Access Control (RBAC) for a land ownership risk detection tool. This involves defining roles (Admin, User, Lawyer) within the user entity, using custom decorators and guards to protect sensitive endpoints like document deletion and user management, ensuring only Admins can delete documents.

AI Summary: Implement user authentication for the land ownership risk detection tool using JWT (JSON Web Tokens). This involves creating signup and login endpoints, hashing passwords using bcrypt, generating JWTs upon successful login, and securing protected API routes using guards. The API endpoints will be POST /auth/register and POST /auth/login.

AI Summary: Develop basic login and signup forms with email and password fields, including input validation (required fields and email format verification), and a toggle for password visibility. This is a frontend-only task using mock data; backend integration is not required at this stage.

AI Summary: Write unit tests using Jest and Supertest to verify the functionality of the risk evaluation and OCR extraction services within a land ownership risk detection tool. Tests should validate input and output data.

AI Summary: Create an API endpoint that accepts parsed land ownership document data (parties involved, land description, prior conflicts) and returns a structured JSON legal summary. Mock logic is permitted for this task.

AI Summary: Develop a geolocation service with a RESTful API to store and retrieve land coordinates associated with dispute statuses. The API should accept POST requests to save latitude/longitude pairs and their dispute status, and GET requests to retrieve all points within a specified region. This data will be used to power a frontend map view displaying dispute-prone areas.

AI Summary: Integrate an OpenAI or Claude API into a land ownership risk assessment tool to create a `/chat` endpoint that accepts user questions (and optionally a document ID) and returns AI-generated legal advice. The endpoint must securely handle API keys and log chat interactions to a database.