Freelancers using Streamlit in Kangra
Freelancers using Streamlit in Kangra
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Karan Singh
Kangra, India
AI/ML Engineer crafting intelligent systems & AI solutions.
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AI/ML Engineer crafting intelligent systems & AI solutions.
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In this project, I developed a Sentiment Analysis Web App using deep learning (CNN) and traditional models to classify text sentiment with high accuracy. The system includes a complete evaluation pipeline comparing CNN, LSTM, Logistic Regression, Random Forest, and Naive Bayes — analyzing performance across multiple iterations and datasets. Key Highlights: Built a Streamlit-based web app for real-time sentiment classification Developed and evaluated multiple models for accuracy and F1-score Created detailed analysis reports and prototype schematics Project here → GitHub Repository (https://github.com/Imkaran04/Sentiment_Analysis_Web_App/tree/main) Reports: Sentiment Analysis Report (PDF), Product Prototype Diagram Tech Stack: Python, Streamlit, TensorFlow/Keras, Scikit-learn, Matplotlib
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1. What it is: An advanced e-commerce web app that combines Graph Neural Networks (GNNs) and Generative AI to identify "toxic inventory"—products, sizes, or suppliers driving high customer returns. 2. GNN Predictive Modeling: Built a heterogeneous GNN using PyTorch Geometric to model relationships between customer demographics and product features to predict future return risks. 3. AI Attribute Enrichment: Automated a metadata extraction pipeline using the Gemini API with local caching to parse raw HTML product descriptions into structured product features (fabric, fit, pattern). 4. Interactive Dashboard & Reporting: Developed a Streamlit dashboard with Supabase Auth and integrated ReportLab to generate boardroom-ready PDF return audit reports. 5. Tech Stack: Python, PyTorch Geometric, Streamlit, Gemini API, Supabase, ReportLab.
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Scientific Image Forgery Detection — Kaggle Competition Participated in the ongoing Kaggle competition on Copy-Move Forgery Detection in Scientific Images, aimed at identifying manipulated biomedical figures that can compromise research integrity. For this challenge, I developed a ResNet50 + U-Net hybrid segmentation model using PyTorch, designed to detect and segment forged regions at the pixel level. My approach combines Dice and Focal losses for balanced training, WeightedRandomSampling to oversample forged images, and Test-Time Augmentation (TTA) to improve prediction robustness. Achieved an initial score of 0.303 on the public leaderboard. I’m continuing to experiment with architecture tuning, learning rate schedules, and other loss functions to further enhance performance and generalization.
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Introducing QuickSynopsis, a fully-featured AI-based summarization and text comparison web app designed for speed, simplicity, and scalability. This project lets users: Generate efficient, context-aware summaries for any text. Compare multiple Summaries to highlight key differences. Enjoy a responsive UI with user authentication. Built using Python (Flask), HTML/CSS/JS, and SQLite/MySQL, QuickSynopsis can easily be customized or deployed to your preferred cloud platform. Key Features: AI-powered summarization & text comparison Signup/login authentication Integrated payment gateway (customizable) Responsive, modern UI/UX Ready-to-deploy setup for Heroku, AWS, or local hosting Explore the repo: GitHub – QuickSynopsis-Version-Control (https://github.com/Imkaran04/QuickSynopsis-Version-control)
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