CS student into AI/ML, building cool things, exploring bold ideas, and growing one project at a time.

github

Dhawal Lamba  • Peerlist

<h3>AI-powered browser extension that detects scam and phishing messages using NLP.</h3>
This project, Scamnet.ai, is an AI-powered browser extension designed to detect scam and phishing messages using Natural Language Processing (NLP). It analyzes suspicious messages and provides warnings to users before they interact with them, enhancing online safety.
Key Features:
<ul>
<li>AI-powered scam and phishing detection.</li>
<li>Utilizes NLP for message analysis.</li>
<li>Browser extension for real-time protection.</li>
<li>Backend API built with FastAPI.</li>
<li>Includes model training and prediction scripts.</li>
</ul>
Setup Instructions:
To set up Scamnet.ai, follow these steps:
<ol>
<li>Install Dependencies: Ensure Python is installed. Create a virtual environment and install the required packages using pip install -r requirements.txt.</li>
<li>Train the Model: Run the python train.py script to generate the machine learning model (model.pkl) and vectorizer (vectorizer.pkl).</li>
<li>Run the Backend API: Start the FastAPI server using uvicorn predict:app --reload. The API will be accessible at http://localhost:8000.</li>
<li>Install the Browser Extension: Open your browser (Chrome/Edge), navigate to the extensions page, enable developer mode, and load the unpacked browser_extension folder.</li>
</ol>
Future Improvements:
<ul>
<li>Implement more advanced NLP preprocessing techniques.</li>
<li>Integrate transformer models like BERT.</li>
<li>Add URL scanning capabilities.</li>
</ul>

Scamnet.ai

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PyLearn — Learn Python the Fun Way

Developed an interactive Pokémon Gallery web app using Streamlit and GitHub Pages integration. The app allows users to explore Pokémon in a clean, responsive gallery interface with a custom-themed design and Pokéball loading animation. Deployed the project for public access via Streamlit Cloud and integrated a custom landing page using GitHub Pages for seamless sharing. ✨ Key Highlights: Built with Python (Streamlit framework) Frontend landing page styled with HTML, CSS Added custom Pokémon-themed loading animation 🎮 Deployed live at: https://pokemon-gallery-web.streamlit.app/