Saiprasaad Kalyanaraman

Repo Vision

Data & ML web app · 2024

Forecasts GitHub activity for popular open-source repositories with three different models.

5 repositories tracked3 forecasting models3 microservices7 activity series

A dashboard that pulls a year of issues, pull requests, commits, branches, releases and contributions for five well-known repositories, then forecasts what comes next with an LSTM, Prophet and SARIMAX so the three models can be compared side by side.

How it works

1. Pick a repository in the React dashboard
2. The Flask service collects a year of activity from the GitHub API
3. pandas groups it by day, week and month
4. The forecasting service trains an LSTM and fits Prophet and SARIMAX
5. Charts are saved to Google Cloud Storage and shown in the dashboard

Features

• Three models, side by side. An LSTM built with TensorFlow and Keras, Prophet, and a SARIMAX model from statsmodels each forecast the same history.
• Seven activity series. Issues created and closed, pull requests, commits, branches, releases and contributions.
• Peak-activity insights. For each model it reports the weekday with the most issues created and closed, and the month with the most issues closed.
• Cross-repository charts. Highcharts views compare issues, stars and forks across all five repositories, with monthly and weekly issue counts.
• Well-known repositories. Elasticsearch, Pymilvus, Angular Google Maps, OpenAI Python and the OpenAI Cookbook.
• Containerized services. The React app, the Flask API and the forecasting service each ship with a Dockerfile for Google Cloud Run.

• Python
• Flask
• React
• TFTensorFlow
• TSTime-series forecasting
• GCGoogle Cloud
• Docker
• Elasticsearch

GitHub repo

AI-Powered Log Summarizer

AI tool

Summarizes application logs with an LLM and posts the summary to Microsoft Teams.

Turns noisy application logs into short summaries with a language model and sends them to Microsoft Teams, so anomalies surface in real time and incidents get a faster response.

How it works

1. Application logs reach a Python and Flask service
2. An LLM served through Ollama summarizes them and flags anomalies
3. The summary is posted to Microsoft Teams

Features

• Real-time summaries. Condenses log activity into a few readable lines instead of pages of raw output.
• Anomaly detection. Calls out unusual patterns so they are noticed as they happen.
• Local LLM through Ollama. The model runs through Ollama and is called from the Flask service.
• Alerts in Teams. Summaries arrive in Microsoft Teams, where the team already works.

• Python
• Flask
• OlOllama
• LMLLMs
• TmTeams API
• AIGenerative AI

YouTube Translator

AI web app · 2024

Paste a YouTube link, pick Spanish or German, and get the video back with a translated voice-over.

6 pipeline steps2 target languages

A Streamlit app that dubs a YouTube video into another language. It chains speech recognition, machine translation and text-to-speech, then puts the new voice back into the original video and plays it in the browser.

How it works

1. Download the video with pytube
2. Extract the audio track with MoviePy
3. Transcribe the speech with OpenAI Whisper
4. Translate the transcript with the OpenAI API
5. Generate the new voice with gTTS
6. Merge the translated audio into the video

Features

• One-click dubbing. A single Translate button runs the whole pipeline and plays the finished video on the page.
• Whisper transcription. Runs OpenAI's open-source Whisper model to turn the speech into text.
• LLM translation. Translates the transcript with the OpenAI API (gpt-3.5-turbo-instruct).
• Progress at every step. Shows each stage as it runs: downloading, extracting audio, translating, generating speech and rendering.

• Python
• Streamlit
• WhWhisper
• OAOpenAI APIs
• TTSgTTS
• MPMoviePy
• NLNLP
• AIGenerative AI

GitHub repo

JSON Explorer

Web app · 2025 · Live · updated April 2026

Turn JSON into an interactive graph you can search, walk through and compare.

2 modes: explore and compare200 nodes rendered per batch

A web app for making sense of large JSON documents. It draws JSON as a graph of connected nodes you can pan, zoom, search and click through, pairs it with a full code editor, and adds a side-by-side diff for comparing two files. It is installable, works offline and can be embedded in other pages.

How it works

1. Paste, type or upload JSON in the Monaco editor
2. The app parses it into nodes and edges
3. React Flow draws the graph with color-coded values
4. Click, search or play a walkthrough to explore it

Features

• Graph view. Objects and arrays become connected nodes, with values color-coded by type.
• Path highlighting. Click any node to highlight its path from the root and open its full details.
• Search. Find keys and values, step through the matches and jump the view to each one.
• Walkthrough mode. Plays through the structure node by node, so large documents are easier to follow.
• JSON Compare. A Monaco diff editor with added and removed counts, swap, format-both and next/previous change navigation.
• Embeddable and offline. An embed mode takes JSON from a URL or postMessage, and a Workbox service worker makes it installable and usable offline.
• Fast on big files. Renders 200 nodes at a time and loads more on request, so large documents stay responsive.

• React
• RFReact Flow
• MoMonaco Editor
• MUIMaterial UI
• JavaScript
• PWAPWA
• NfNetlify

Live demoGitHub repo

Campus Cooks

Mobile app · 2024

Snap a photo of your ingredients and get recipes you can make with them.

10 recipes per photo2 food APIs

A Flutter app built with Koodos Labs during the Open Avenues internship. Students photograph the ingredients they have; the app recognizes them with the LogMeal food-recognition API and suggests recipes from Spoonacular that use as many of them as possible, with nutrition facts for each.

How it works

1. Take a photo with the in-app camera
2. LogMeal segments the image and names each ingredient
3. Spoonacular finds recipes that use the most of them
4. Each recipe card shows its nutrition and cooking time

Features

• Camera capture. A built-in camera screen that switches between the front and rear cameras.
• Ingredient recognition. Sends the photo to LogMeal's segmentation API and lists every ingredient it finds.
• Recipes from what you have. Asks Spoonacular for up to 10 recipes, ranked to use the most detected ingredients.
• Nutrition at a glance. Calories, protein, fiber, sugar and ready-in time for every recipe.
• Firebase and Figma. Firebase handles data storage and sign-in; the interface was designed in Figma.

• Flutter
• Dart
• Firebase
• LMLogMeal API
• SpSpoonacular API
• Figma
• UXUI/UX design

GitHub repo

Chicago Streets Harmony

Mobile app · 2024 · Built with Nagarajan Sivakumar

Find upcoming Chicago parades, or help someone experiencing homelessness get support.

A community app for Chicago. It lists upcoming parades along with the marching bands, floats and performers in each, and lets anyone report a person who needs help by sending a photo with their location so city services can respond.

How it works

1. A Spring Boot REST API serves parade and event data
2. The Flutter app lists upcoming parades, the next one first
3. Tap a parade to swipe through its units
4. Report someone in need with a photo and your GPS location

Features

• Parade guide. Upcoming parades with dates and descriptions, with the next one highlighted at the top.
• Swipe through units. A card deck for the marching bands, floats and performers in each parade.
• Homeless helper. Captures a photo and the GPS location (with its street address) to alert city services.
• Spring Boot back end. A REST API that serves the event data as JSON to the app.

• Flutter
• Dart
• Spring Boot
• Java
• GeoGeolocation
• APIREST APIs

GitHub repo

Asana Task Automation

Automation

Creates the day's tickets in Asana and keeps them in sync with back-end dashboards.

Automates the daily routine of creating tickets in Asana and syncs their data with the SQL behind the back-end dashboards, so the task board and the dashboards stay consistent without manual updates.

How it works

1. A Python and Flask job creates the day's tickets through the Asana API
2. Ticket data is synced with the dashboards' MySQL database

Features

• Daily tickets. Recurring tickets are created automatically instead of by hand every morning.
• Dashboard sync. Keeps the MySQL data behind the dashboards in step with Asana.

• AsAsana API
• Python
• Flask
• RdRedis
• MySQL
• SQLSQL

Encryption & Decryption Module

Security

AES-CBC encryption shared by a React front end and a Flask back end.

Implements AES-CBC encryption and decryption on both sides of an app, in the React front end and in the Flask back end, so sensitive data is protected end to end.

How it works

1. Data is encrypted with AES-CBC in the React app
2. Only the ciphertext travels to the Flask service
3. The Flask service decrypts it with the same scheme

Features

• One scheme, two languages. Matching AES-CBC implementations in JavaScript and Python, so either side can read what the other wrote.
• End-to-end protection. Sensitive fields stay encrypted between the browser and the server.

• React
• Flask
• AESAES-CBC encryption
• JavaScript
• Python

Battleships

Mobile game · 2023

Multiplayer Battleships in Flutter: place five ships, then play people or one of three AI opponents.

5 ships to place3 AI opponents

A Flutter client for a multiplayer Battleships game built on a REST API for authentication and gameplay. Players sign in, start a game against another person or an AI, place five ships and trade shots turn by turn.

How it works

1. Log in or register; the session token is saved on the device
2. Start a game against a person or one of three AIs
3. Place five ships on the board
4. Take turns firing until every ship is sunk

Features

• Accounts and sessions. Login and registration, with the session token kept on the device so players stay signed in.
• Three AI opponents. Random, Perfect and One-ship modes, alongside matchmaking with other players.
• Game list. Active and finished games with their state (your turn, opponent's turn, matchmaking, won or lost), and swipe to forfeit.
• Clear feedback. Ships, misses, hits and wrecks are marked on the board, with messages for sunk ships and repeated shots.

• Flutter
• Dart
• Flask
• MySQL
• APIREST APIs

GitHub repo

Wordle Clone

Mobile game · 2024

Cross-platform Wordle with a new word every game.

6 tries per word2 word APIs

A cross-platform Wordle clone written in Flutter. Every game fetches a fresh five-letter word, checks each guess against a dictionary before accepting it, and colors the tiles the way the original does.

How it works

1. Fetch a random five-letter word from the Wordnik API
2. Type a guess on the on-screen keyboard
3. Check it against a dictionary API
4. Color the tiles and move to the next row

Features

• A new word every game. Random five-letter words from the Wordnik API, fetched again until a valid one comes back.
• Real-word checks. Every guess is looked up in a dictionary API; made-up words are rejected.
• Classic scoring. Six tries, with green for a letter in the right spot and yellow for a letter in the wrong spot.
• Win and reveal. A custom keyboard sized to the screen, plus dialogs for a win or for revealing the word after the last try.

• Flutter
• Dart
• APIREST APIs

GitHub repo