Project Info
This project did not submit a demo video on Devpost.
Inspiration
We wanted to create an easy way for users to fact-check news articles directly within their browser. With the rise of misinformation, Refact aims to provide a tool for quick, accessible fact-checking.
What it does
Refact allows users to input an article's text, and it verifies the information using the Perplexity API. It delivers fact-check results in real-time through a user-friendly interface, helping users verify claims easily.
How we built it
The project uses a Flask backend to connect with the Perplexity API, which performs the fact-checking. We built the frontend using HTML, CSS, and JavaScript, designed for a smooth experience inside a browser extension. The extension communicates with the backend to process requests and display the results.
Challenges we ran into
We had some struggles in connecting the Flask backend to the frontend, particularly in setting up a reliable flow of information between the browser extension and the server. Ensuring seamless communication was crucial to avoid delays in fact-checking.
Accomplishments we're proud of
We successfully integrated a functional fact-checking system using the Perplexity API. The browser extension works smoothly, and we’ve achieved a user-friendly interface.
What we learned
We learned the complexities of building a backend that interfaces with browser extensions, especially regarding API integration and managing cross-domain communication between the frontend and backend.
What's next
We plan to expand Refact by integrating additional APIs for more comprehensive fact-checking. We’re also exploring adding other tools like article summarization to further enhance the user experience.
FactCheckAI
Overview Fact Checker AI is a tool designed to verify the accuracy of written statements in articles and reports. The AI scans articles, checks the factual accuracy of key claims, and provides a summary of how truthful the content is. It is aimed at offering quick and reliable fact-checking for articles, making it easier to discern credible information from misleading content.
This version focuses solely on article fact-checking due to time constraints, with plans for future expansion to video-based fact-checking.
Our project addresses a critical need in today's information landscape: helping people make more informed decisions, particularly during high-stakes events like presidential elections. In an age where political discourse is often flooded with misinformation, our solution empowers voters to access accurate, unbiased information in real-time. By providing instant fact-checking during debates and political speeches, we aim to reduce the influence of misleading or false statements, allowing the electorate to focus on the actual issues at hand.
Our system integrates natural language processing (NLP) with advanced AI-driven fact-checking algorithms. It begins by analyzing live or pre-recorded YouTube videos of debates or speeches. Using the YouTube API, our application extracts video transcriptions and processes them through spaCy, a robust NLP library, to identify key claims and statements. These are then cross-referenced with fact-checking databases through our AI agent to determine their accuracy. The result is an easily accessible, real-time report on whether a statement is factually true, incorrect, or an opinion.
From a technical perspective, our project employs a variety of cutting-edge tools. We use Python as the core programming language, integrating with the YouTube API for transcription extraction and spaCy for linguistic processing. This combination allows our system to accurately extract and evaluate sentences from complex speech patterns. On the front end, we developed a browser extension using JavaScript and background.js to automatically provide fact-checking feedback as users browse YouTube videos or articles. This seamless integration enables users to view content without needing to manually input links.
Additionally, by leveraging pre-trained models and sophisticated algorithms, our system achieves real-time fact-checking without the need for extensive re-training, making it both scalable and efficient. This project highlights the power of NLP, AI, and real-world data integration, resulting in a technically complex yet user-friendly solution for promoting informed voting in presidential elections.
Analysis
View
Metric
- 9
- 5
- 3
- 2
Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.
Technology
- CIn code
- CSSIn code
- HTMLIn code
- JavaScriptIn code
- PythonIn code
- FlaskClaimed
5 of 6 appear in the indexed code. 1 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.
AI coding agents
No AI coding agent signals were found in this repository.
Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.
Codebase size
Source size
21 MB
Source files
2,092
Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.
Repository
hirdey795/FactCheckAI
2,747 files · 73.6 MB · @ e7a9aa1
Structure
Interface
6 files · 0%Screens, components and styles rendered to the user.
API & routing
85 files · 3%Request entry points: routes, handlers and controllers.
Application logic
2,449 files · 89%Domain rules, services and shared utilities.
Data & schema
32 files · 1%Schema definitions, migrations and data access.
Supporting
Layers are inferred from where files sit in the tree, not from reading the code. A project that names its directories unconventionally will read oddly here — open the file browser to check anything the diagram implies.
Languages
- Python100%
- JavaScript0%
- C0%
- CSS0%
- Markdown0%
- HTML0%
- Other (1)0%
Share of indexed source by file size. Binary and vendored files are excluded.
This project’s features have not been analysed yet.
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