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SQL Assistant : Voice/Text to SQL Multilingual WebApp
INTRODUCTION - SQL Assistant is an AI-powered web application built with Streamlit that allows users to query any SQLite database using natural language — either by typing or speaking. The app uses Groq LLMs to convert plain English (or Hindi, Telugu, and 50+ other languages) into accurate SQL queries, executes them securely, and presents results with interactive charts, downloadable CSVs, and AI-generated summaries. Users can also bring their own database by uploading a .db file or CSV files directly from the sidebar. FEATURES - 1. Natural Language to SQL - Converts user questions into valid SQLite queries using Groq LLMs - Supports three models: Llama 3.3 70B Versatile, Llama 3.1 8B Instant, and Gemma 2 9B - Auto error correction with configurable reflection loop (0-10 retries) when the generated SQL is invalid 2. Voice Input with Multilingual Support - Built-in microphone recording using audio-recorder-streamlit - Speech-to-text powered by Groq Whisper (whisper-large-v3) - Automatically detects and transcribes 50+ languages including English, Hindi, and Telugu 3. Dynamic Database Management - Comes with a pre-built ecommerce SQLite database (customers, products, orders) - Upload any SQLite .db file from the sidebar to query it instantly - Upload one or more CSV files — each file automatically becomes a table in a new database - Schema is auto-detected from any database and passed dynamically to the LLM prompt - Sidebar schema viewer shows all tables, columns, types, and row counts 4. Interactive Plotly Visualizations - Four chart types: Bar Chart, Pie Chart, Line Chart, and Scatter Plot - Auto-selects appropriate columns (numeric vs categorical) for axes - Dark theme styled charts that match the app UI 5. SQL Security - SELECT-only enforcement — blocks INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, TRUNCATE, and EXECUTE queries before they reach the database - Validation runs on every query before execution 6. CSV Export - One-click download button to export any query result as a .csv file 7. Performance Dashboard - Real-time tracking of total queries, successful queries, and failed queries - Success rate percentage with visual progress bar in the sidebar 8. AI-Powered Summarization - After every successful query, the LLM generates a natural language summary of the results - Users can add custom context (e.g., "write it in Hindi", "explain it simply") to control the summary style TECH STACK - - Frontend: Streamlit - Database: SQLite - LLM API: Groq (Llama 3.3 70B, Llama 3.1 8B, Gemma 2 9B) - Voice Transcription: Groq Whisper (whisper-large-v3) - Visualizations: Plotly Express - Audio Recording: audio-recorder-streamlit - SQL Formatting: sqlparse - Data Processing: pandas --> Response : 0.2 seconds --> Accuracy : 98.2%
Student Performance Prediction
This project implements a student performance prediction system using a modular machine learning pipeline built in Python. Nine regression models are trained and evaluated - Random Forest, XGBoost, CatBoost, Gradient Boosting, AdaBoost, Decision Tree, K-Nearest Neighbors, Linear Regression, and Extreme Learning Machine (ELM). ELM, introduced as the primary novel addition, is a Single Hidden Layer Feedforward Neural Network that analytically solves output weights via Moore-Penrose pseudoinverse, eliminating iterative backpropagation and achieving competitive accuracy at significantly faster training speeds (Deo et al., IEEE Access, 2020). All models are optimized using GridSearchCV and the best model by R² score is automatically saved for inference. A Flask web application provides a clean, responsive UI for real-time predictions with grade classification.
Intelligent File Organizer - ML
Upload any mix of files or folders and get back a neatly organized ZIP - automatically. FileGenie uses a RandomForest ML model to classify files by actual content (magic bytes, entropy, keywords), not just extension, so even mislabeled or extension-less files land in the right place. Built with FastAPI, React, and JWT authentication. Accuracy : 95% Tech Stack - Backend : Python 3.10+ · FastAPI · Uvicorn SQLAlchemy 2.0 ORM · SQLite (dev) / PostgreSQL (prod) PyJWT (HS256) scikit-learn RandomForestClassifier (300 trees, 16-feature vector) python-multipart for file uploads Frontend : React 18 · Vite 5 · Tailwind CSS 3 React Router v6 (multi-page SPA) JWT stored in localStorage · AuthContext for global auth state
Drowsiness Detection
This project detects drowsiness using classical computer vision techniques instead of deep learning. It first identifies the face using HOG + SVM (via dlib), then extracts 68 facial landmarks using an Ensemble of Regression Trees (ERT) model. From these landmarks, it calculates the Eye Aspect Ratio (EAR) to measure eye openness. If the EAR drops below a threshold (0.25) for a continuous number of frames, the system concludes that the eyes are closed for too long and triggers a drowsiness alert. App Modes - --> Live Webcam - Click Start to begin real-time detection - Click Stop to release the camera - Runs at ~30 FPS --> Upload Video - Supports `.mp4`, `.avi`, `.mov`, `.mkv` - Click **Run Detection** to process frame by frame - Progress bar shows completion --> Upload Image - Supports `.jpg`, `.jpeg`, `.png`, `.bmp`, `.webp` - Instant single-frame analysis - Shows annotated image with EAR value and status
Violence Classification System
Video Anomaly Detection (VAD) is an AI-powered violence classification system that analyzes video clips in real-time. Built on a MobileNetV2 + BiLSTM + Temporal Attention architecture, it detects and classifies four types of violent actions — Kick, Punch, Slap, and Group Violence — with up to 98% validation accuracy. The system extracts spatial features per frame using MobileNetV2, computes temporal difference features to capture motion dynamics, then passes the combined sequence through two Bidirectional LSTM layers and a Temporal Attention mechanism before classifying. Key highlights: Multi-clip inference (5 stride-based clips averaged) for robust prediction Focal loss training to prevent class collapse on minority classes Two-phase training strategy: feature learning then fine-tuning Streamlit web UI - upload any MP4/AVI/MOV file and get instant results
TRAVELYS – Smart Travel Risk & Cost Analytics System
TRAVELYS – Smart Travel Risk & Cost Analytics System TRAVELYS is a data-driven travel analytics and decision-support platform built with Flutter Web that helps users evaluate international travel destinations using multiple socioeconomic and environmental indicators. Instead of relying only on basic travel blogs or reviews, TRAVELYS aggregates global travel indexes, real-time weather data, currency exchange rates, and tourist attraction information to generate meaningful travel insights. The platform analyzes metrics such as safety, cost of living, quality of life, pollution, climate, and healthcare availability to help travelers make informed decisions before visiting a country. The application provides an interactive dashboard where users can explore country-level analytics, visualize travel-related indicators, and generate downloadable travel reports for deeper insights. Key Highlights: Data-Driven Travel Insights: Combines multiple global indicators including Quality of Life, Safety, Cost of Living, Healthcare, Climate, and Pollution to provide a comprehensive overview of travel conditions in different countries. Smart Travel Recommendation System: Generates a weighted travel recommendation score based on multiple indexes to help users identify destinations that best match their safety, affordability, and lifestyle preferences. Interactive Country Analytics Dashboard: Users can explore detailed country profiles displaying travel-related metrics, environmental conditions, and economic indicators in a clean and intuitive interface. Real-Time Travel Information: Integrates external APIs to fetch current weather conditions, currency exchange rates, and tourist attraction data for selected destinations. Tourist Attractions Explorer: Displays popular tourist destinations, landmarks, and points of interest using geographic data APIs to enhance travel planning. Downloadable Travel Reports: Users can generate downloadable reports summarizing travel insights, making it easier to save or share travel analysis. Share Travel Insights: Allows users to share country travel analytics through built-in sharing functionality. Secure User Authentication: User accounts are protected using Firebase Authentication with email verification to ensure secure access to the platform. Modern Responsive UI: Built using Flutter Web to provide a responsive, modern interface that works smoothly across desktops and browsers. Technology Stack Frontend: Flutter Web Dart Backend / Services: Firebase Authentication APIs Used: Geoapify API – Tourist attractions data Weather API – Real-time weather information Currency Exchange API – Global currency rates Data Sources: Global travel and lifestyle index datasets including: Quality of Life Index Purchasing Power Index Safety Index Health Care Index Cost of Living Index Traffic Commute Time Index Pollution Index Climate Index
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