Project Name: Pravahini (प्रवाहिनी) - Where Data Flows, Models Grow, and AI Glows!
Project Track: Builder
Team Name: Team Pravahini
Team Member(s): @victorious @im_31 @oggyyy
Devpost Project Link: Will be updated.
Project Goal:
Our goal is to create a user-friendly and secure AI marketplace that facilitates the buying and selling of datasets and pre-trained ML models. Our platform also offers coding capabilities for the users and empowers decentralized computation to process their machine-learning models. We aim to establish a vibrant community of data providers, model developers, and users, offering a diverse range of high-quality resources for various domains using blockchain technology.
We are adding more features like Data Visualization, Commenting Features in individual datasets and Models, Email notification for completion of Computation Job and Plagiarism Checker to avoid duplicity of the dataset. These features will enhance the user experience.
Through Pravahini, we aim to foster collaboration, innovation, and knowledge-sharing among data enthusiasts and researchers, driving ground-breaking advancements in AI and data-driven applications.
Project Value:
Benefits of Pravahini on Blockchain:
Secure Marketplace Functionality: Blockchain ensures a secure and transparent marketplace for buying and selling datasets and pre-trained ML models. Transactions are tamper-proof, enhancing trust.
Efficient Decentralized Computation: Leveraging blockchain, Pravahini optimizes ML model processing through the Bacalhau Protocol. Users harness the power of distributed systems for resource-intensive tasks.
Granular Access Control: The Lighthouse Protocol enforces secure access control, allowing users to define permissions. This protects sensitive data and resources from unauthorized access.
Transparent Revenue Sharing: Smart contracts facilitate transparent revenue sharing with data providers and model creators. This creates a fair and collaborative ecosystem, incentivizing quality contributions.
Project Info:
In Sanskrit, the term “प्रवाहिनी” (Pravāhinī) embodies the essence of a “flow” or “stream.” Within our platform, it symbolizes the continuous flow of data, models, and innovations powering the realm of artificial intelligence. Pravahini is not just a marketplace; it’s a dynamic ecosystem fostering the evolution of AI resources.
Pitch deck: Will be updated
Project Website: Pravahini
Project Test Instructions:
These test instructions are of previous version, new one will be updated soon
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Connect your wallet:
Connecting your wallet is the first step to using our Platform. -
Get Started:
Click on the ‘Get Started’ button to be redirected to the sign-up page. -
User Dashboard:
The user dashboard will provide you with access to all your datasets and machine learning models. Additionally, it will display the purchased models and datasets for your convenience. -
Create Dataset & ML Model:
To create a dataset or ML Model, upload a file from your local device in the required format. Fill in all the necessary details and click the submit button. -
Dataset Marketplace & ML Model Marketplace:
Here you can find the list of datasets and ML Models that you can buy and use. There will be free as well as paid Datasets and ML Models. -
Decentralized Computation:
Here you can use decentralized computing for your ML model computations. To facilitate this, we provide two robust containers tailored to meet your specific requirements.
Project Details:
Problem Statement
Existing marketplaces for AI resources lack a unified platform that seamlessly integrates dataset and pre-trained model trading, coding capabilities, and decentralized computation. This fragmentation hinders efficient collaboration and resource utilization within the AI community.
Solution -
Pravahini is an innovative platform that offers an expansive AI marketplace encompassing datasets, pre-trained machine learning models, and decentralized computation. We empower users to unlock the full potential of AI through our comprehensive suite of features:
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AI Marketplace:
Explore a diverse array of datasets and pre-trained models tailored for machine learning and artificial intelligence applications. From image recognition to natural language processing, Pravahini provides a rich repository of resources to fuel your projects. -
Decentralized Computation:
Harness the power of distributed systems with our decentralized computation feature. Seamlessly process machine learning tasks across multiple nodes, maximizing efficiency and scalability while minimizing resource constraints.
New Features Enhancing User Experience -
In our continuous pursuit of excellence, we are excited to introduce several new features designed to enrich user’s Pravahini experience:
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Data Visualization:
Gain deeper insights from your datasets through advanced data visualization tools. Uncover patterns, trends, and relationships with interactive charts, graphs, and visualizations, enhancing your understanding. -
Commenting Feature:
Foster collaboration and feedback within the community with our commenting feature. Share your experiences, insights, and concerns directly on individual datasets and models, facilitating knowledge exchange and quality assurance. -
Computation Completion Notification Feature:
Email notifications for computation completion. Now, when your machine learning tasks are processed on our platform Pravahini, you’ll receive instant updates right in your inbox. Stay informed about your task progress, and track completion status. -
Plagiarism Checker for Datasets:
Safeguard data integrity and encourage original contributions with our plagiarism checker. Detect and prevent data duplication, ensuring the authenticity and credibility of datasets within the marketplace.
Smart Contract links: Will be updated
Project Milestones:
Milestone 1: (01/04/2024 to 10/04/2024) [Completed 
]
Aim: Integration of Data Visualization Tools
Research and Tool Selection
- Research various data visualization tools and libraries.
Evaluate compatibility and integration with the existing tech stack.
Select the most suitable tool for integration.
Integration of Visualization Features
- Implement interactive charts and graphs based on uploaded datasets.
Refine the user interface for a seamless visualization experience.
Conduct thorough testing to ensure functionality.
Documentation and Deployment
- Document integration process with setup instructions and usage guidelines.
Deploy updated platform with data visualization tools to the production environment.
Milestone 2: (10/04/2024 to 20/04/2024)
Aim: Commenting Feature in individual Dataset and Model
Research and Planning
- Research existing comment systems and define requirements.
- Define user flow for adding, viewing, and managing comments.
Database Schema and Backend Setup
- Design database schema for storing comments.
- Implement backend APIs for comment CRUD operations.
Comment UI Design, Integration, and Testing
- Design UI components for adding and displaying comments.
- Integrate comment UI into dataset and model pages.
- Test comment functionality thoroughly.
Documentation and Deployment
- Document usage guidelines and moderation policies for comments.
- Deploy updated platform with comment feature enabled.
Milestone 3: (21/04/2024 to 30/04/2024)
Aim: Email Notifications for Computation Completion
Research and Email Service Setup
- Research email notification services compatible with the current tech stack.
- Set up an email service provider or configure SMTP settings.
- Create email templates for computation completion notifications.
Backend Integration and Testing
- Develop APIs to trigger email notifications on computation completion.
- Implement logic to retrieve user email addresses and customize notification content.
- Conduct thorough testing to ensure emails are sent correctly.
Documentation and Deployment
- Document email notification feature with setup instructions and usage guidelines.
- Deploy updated platform with email notification functionality enabled.
Milestone 4: (01/05/2024 to 15/05/2024)
Aim: Deployment of Plagiarism Checker
Research and Planning
- Research plagiarism checker tools suitable for the platform’s needs.
- Identify integration points within the platform for the plagiarism checker.
Dataset Submission Flow
- Design submission flow to include a plagiarism checker feature.
- Implement automatic plagiarism check during dataset submission.
- Develop mechanisms to handle detection results and notify users.
Testing and Optimization
- Conduct extensive testing of the plagiarism checker feature.
- Optimize plagiarism detection algorithms for improved accuracy.
Documentation and Deployment
- Document usage guidelines and policies for plagiarism checkers.
- Deploy updated platform with plagiarism checker enabled.
Project Screenshots:
Some of the screenshots are from the previous version, but as we complete our milestone, we are updating the images.
