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https://github.com/natewu/marketsafe
https://github.com/natewu/marketsafe
Last synced: 7 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/natewu/marketsafe
- Owner: natewu
- Created: 2024-01-06T02:44:15.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-01-11T06:42:01.000Z (10 months ago)
- Last Synced: 2024-01-11T10:59:24.148Z (10 months ago)
- Language: JavaScript
- Size: 1.87 MB
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# MarketSafe
## Inspiration 💡
In the digital era, we are constantly bombarded with information. It is hard to tell what is real and what is fake. We are inspired to create a platform that can help people to identify fake content and make better decisions.
MarketSafe is a cutting edge platform using the latest machine learning technology to help people to identify fake content. Marketplaces such as Amazon, eBay, and Alibaba are filled with fake reviews. It is hard to tell which reviews are real and which are fake. MarketSafe is a platform that can help people to identify fake reviews and make better decisions.
Businesses use MarketSafe to identify fake reviews and improve their products and services. MarketSafe can help businesses to identify fake reviews and automatically flag them for review.
## What it does 🤔
MarketSafe is an innovative platform that integrates artificial intelligence to scrutinize product reviews in real-time. It identifies and flags fake reviews, harmful content, and misinformation, enabling businesses to maintain a high standard of credibility. For businesses, this means enhanced product integrity, improved consumer trust, and an overall healthier marketplace environment. Consumers benefit from more accurate product information and reliable reviews, assisting them in making better purchasing decisions.
## How we built it ⚙️
First and foremost, we've crafted MarketSafe with 💙. We have built a ML-enabled full-stack application that solves a real world problem. The whole process can be broken into the following points:
- Front-end development with React, styled using Tailwind CSS and Sass for a responsive and user-friendly interface.
- Back-end development with Flask and Python, utilizing SQLite3 for database management, and integrating Google Cloud and PyTorch for advanced computing needs.
- Implementation of OpenAI's GPT-4 and Google Cloud's Natural Language API for analyzing and processing review content.
- Use of Google Cloud BigQuery for handling large datasets and analytics.
**The application's architecture ensures robustness, scalability, and a seamless user experience.**
## Design 🎨
Our inspiration drew heavily from the reimagined Double Diamond design process, emphasizing a comprehensive research cycle that mandates problem discovery and definition before delving into the solution phase and eventual deployment. This approach ensured a holistic and effective design strategy for the development of MarketSafe.
> 1. **Discover**: a deep dive into the problem we are trying to solve.
> 2. **Define**: synthesizing the information from the discovery phase into a problem definition.
> 3. **Develop**: think up solutions to the problem.
> 4. **Deliver**: pick the best solution and build that.
In addition, we leveraged design tools such as Figma to craft prototypes before diving into the coding process. This approach allowed us to gather iterative feedback early in the development cycle, reducing the need for extensive code revisions. By adopting this strategy, we streamlined our workflow and optimized the efficiency of the development process, ensuring that our final product, MarketSafe, met the highest standards in both functionality and user experience.
## Challenges we ran into 😤
Developing the MarketSafe review analyzer using machine learning posed unique challenges for our diverse team spread across different time zones, including British Columbia, Alberta, and Georgia, USA.
## Accomplishments that we're proud of 💚
Our team achieved significant milestones that fill us with pride, including successfully implementing machine learning with GPT-4 for advanced analysis, ensuring the seamless functionality of the backend, mastering collaborative teamwork, crafting an exceptional frontend user experience, and achieving an aesthetically pleasing and intuitive UI design. These accomplishments collectively reflect our dedication and proficiency in creating a robust and user-friendly platform with MarketSafe.
## What we learned 🙌
The development of MarketSafe enriched our knowledge base in various domains. Integrating and optimizing GPT-4 for advanced content analysis enhanced our proficiency in machine learning. Overcoming the intricacies of backend development provided valuable insights into building a robust server infrastructure. Navigating challenges across different time zones showed us the importance of effective communication and collaborative teamwork. Crafting the frontend and UI design not only improved our user experience design skills but also emphasized the significance of creating intuitive and visually appealing interfaces.
## What's next? 🚀
As MarketSafe continues to evolve, our vision encompasses a broader scope and deeper integration into the digital world. Here’s what we have planned for the future:
1. Expansion to Social Media and Other Platforms:
We aim to extend our services beyond traditional e-commerce websites to social media platforms like Twitter, Instagram, and Facebook. With misinformation and fake endorsements prevalent on social media, MarketSafe's technology could be pivotal in identifying and flagging misleading content, thus protecting brands and consumers alike.
2. Integration with Larger E-commerce Platforms:
Partnering with giants like Amazon, eBay, and Shopify, we plan to integrate MarketSafe directly into these platforms. This integration will offer businesses and consumers an in-built, reliable tool for review verification, enhancing trust and transparency at a larger scale.
3. Advanced Analytics and Reporting Features:
Future updates will include more sophisticated analytics tools, giving businesses deeper insights into customer sentiment, trends, and feedback patterns. These analytics can drive strategic decisions and product improvements.
4. Customizable Filters and AI Models:
Understanding that different businesses have unique needs, we will provide customizable AI models and filters. Companies can adjust these models to best suit their industry standards and customer base.
5. Global Expansion and Multilingual Support:
To cater to the global market, MarketSafe will expand its linguistic capabilities, offering multilingual support to identify and analyze reviews in various languages. This expansion will make our platform more accessible to non-English speaking markets.
6. Collaborations and Community Building:
We plan to collaborate with industry experts, regulatory bodies, and consumer rights organizations to continually refine our algorithms and approaches. By fostering a community around ethical business practices, we can collectively combat the spread of misinformation.
7. API Development for Wider Integration:
We will develop an API that can be integrated into various platforms and systems, allowing businesses to seamlessly incorporate MarketSafe's technology into their existing digital infrastructure.
By realizing these goals, MarketSafe aspires to become a cornerstone of digital integrity, fostering a safer, more transparent, and trustworthy online environment for businesses and consumers worldwide.
# Installation instructions
## Backend (src/flask-server)
```
python -m pip install flask_migrate
python -m pip install flask_marshmallow
python -m pip install flask_cors
python -m pip install python-dotenv
python -m pip install google-api-python-client
python -m pip install google-cloud-bigquery
python -m pip install yake
python -m pip install nltk
python server.py
```
## Frontend (root folder)
```
npm i
npm start
```
# When you pull any database changes on backend
Delete the "database.db" file in src/flask-server and RERUN "python server.py".
This will re-migrate the database schema and initialize any default database values from models.py