Streamlit

Top Builders

Explore the top contributors showcasing the highest number of app submissions within our community.

Streamlit: Effortless Front-Ends for Your Data Apps

Streamlit is a pioneering technology provider that specializes in turning data scripts into shareable web apps with minimal effort. Launched in 2018, Streamlit has gained popularity for its ease of use and efficiency, empowering data scientists and developers to create and deploy data-driven applications swiftly.

General
Author	Streamlit
Repository	https://github.com/streamlit/streamlit
Type	Framework for ML and data science apps

Key Features

Transforms Python scripts into interactive apps with simple annotations, dramatically reducing development time.
Facilitates real-time interactivity directly from Python code without requiring front-end expertise.
Supports hot-reloading, allowing instant app updates as the underlying code changes.
Provides built-in support for a wide array of widgets, enabling the addition of interactive features without additional coding.

Start building with Streamlit's products

Streamlit offers a range of features designed to simplify the process of app creation and deployment, enhancing productivity in data science and machine learning fields. Explore how you can leverage Streamlit to turn your data projects into interactive applications. Don’t forget to check out the innovative projects built with Streamlit at various tech meetups!

List of Streamlit's products

Streamlit Library

The Streamlit Library allows developers to quickly convert Python scripts into interactive web apps. This library is packed with easy-to-use functionalities that make it straightforward to add widgets, charts, maps, and media files, transforming complex data science projects into user-friendly applications.

Streamlit Sharing provides the hosting infrastructure to share Streamlit apps with the world. It simplifies deployment, enabling users to go from script to app in minutes on a secure and scalable platform.

Streamlit for Teams

Streamlit for Teams is designed for collaboration and enterprise usage, offering additional features like integration with existing databases, advanced security protocols, and customized control for managing user access and data privacy.

System Requirements

Streamlit is compatible with Linux, macOS, and Windows systems, requiring Python 3.6 or later. It typically runs with minimal hardware requirements, though performance scales with available resources. For optimal performance, a modern processor and sufficient RAM are recommended, with a stable internet connection for deploying apps using Streamlit Sharing. Modern browsers with JavaScript support are required to view and interact with the apps.

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Streamlit AI technology page Hackathon projects

Discover innovative solutions crafted with Streamlit AI technology page, developed by our community members during our engaging hackathons.

SovereignSwarm: Auto Multi-Tone Video Captioner

SovereignSwarm is an advanced, high-throughput video intelligence application designed to automate the process of localized multi-tone video captioning. Built purely on the fast Fireworks AI inference ecosystem, the system acts as a seamless bridge between computer vision and generative linguistics. The pipeline utilizes OpenCV to dynamically extract key chronological frames from any short video under 30 seconds. These frames are processed through a cutting-edge Vision-Language Model to generate a raw, highly factual description of the scene. The architecture then leverages an asynchronous text-generation layer to rewrite the core description into four completely distinct narrative styles: Formal, Sarcastic, Humorous Tech, and Everyday Humor. The final payload is formatted and returned as a strictly validated, production-ready JSON schema. This ensures seamless integration into enterprise content management workflows, video editing suites, or automated social media pipelines without requiring heavy local infrastructure or complex cloud configuration.

ReCodeX

ReCodeX is an AI-powered enterprise platform developed for the AMD Developer Hackathon: ACT II (Track 3 - Unicorn Track) designed to tackle the multi-billion dollar challenge of technical debt and legacy migration. Instead of a basic chatbot conversational layout, ReCodeX provides a complete engineering dashboard that automates the analysis, documentation, translation, and verification of aging infrastructure like legacy Java, COBOL, or unoptimized C++. The platform delivers comprehensive developer features, including: - Side-by-Side Modernization: Allows users to upload a legacy source file and view the original code side-by-side with refactored, modern, and fully test-covered code. - Automated Documentation: Provides deep semantic understanding of complex code logic to break down precisely what legacy components do. - Risk & Security Audits: Automatically generates a comprehensive Risk Report highlighting structural changes, security optimizations, and cloud-readiness metrics. The AMD Advantage & Tech Architecture: - To process heavy enterprise code demands reliably, ReCodeX relies entirely on high-performance cloud infrastructure backed by AMD hardware. - Core LLM orchestration is routed via the Fireworks AI API, utilizing high-performance AMD hardware endpoints running Google DeepMind's open-source Gemma models. - The compute layer is hosted on the AMD Developer Cloud, leveraging powerful cloud-based AMD GPUs to manage parallelized analytics, code scanning, and workflow pipeline tasks at sub-second latencies. - The entire system is packaged inside a Docker container for standardized deployment, with the frontend application built using Streamlit and React. Ultimately, ReCodeX targets tangible enterprise metrics for the Unicorn Track by reducing ongoing maintenance overhead, ensuring regulatory auditability through precise change logging, and accelerating cloud migration frictionlessly.

XYZ123

This project is an AI-powered video captioning solution designed to automatically generate high-quality captions and summaries for short video clips ranging from 30 seconds to 2 minutes. Instead of producing only a single description, the system creates four unique caption styles for every video: formal, sarcastic, humorous-tech, and humorous-non-tech. This allows the same content to be presented for different audiences, platforms, and use cases. The solution combines multimodal video understanding with modern language models to analyze visual scenes, actions, objects, temporal events, and contextual information before generating style-specific captions. Each generated caption preserves the core meaning of the video while adapting vocabulary, tone, and writing style according to the requested format. Formal captions focus on clarity and accuracy, sarcastic captions add witty commentary, humorous-tech captions incorporate software and engineering references, and humorous-non-tech captions provide lighthearted jokes that are easy for a general audience to understand. The system is designed to process multiple videos efficiently through an automated pipeline that accepts video inputs, performs inference, and returns structured outputs suitable for evaluation. Prompt engineering, video preprocessing, and output validation help maintain consistency across different caption styles while reducing hallucinations and preserving factual correctness. The architecture can also be extended with fine-tuned models or custom-trained captioning components using open datasets.

ReachIQ AI - Competitive Intelligence Engine

ReachIQ AI is a Generative Growth as a Service platform for YouTube creators. For this hackathon, we built a Video RAG-powered Competitive Intelligence Engine: it ingests competitor videos, transcribes them with Whisper, extracts on-screen text via OCR, generates embeddings, and stores everything in a Supabase pgvector knowledge base. Using semantic retrieval, the engine surfaces hook patterns, CTA strategies, thumbnail trends, and content gaps — then synthesizes them into a full executive intelligence report via Gemma 3 4B, deployed and validated on AMD Developer Cloud using ROCm 7.2 and vLLM. This turns hours of manual competitor research into seconds of AI-accelerated strategy.

Express Caption

Express Caption is a video-captioning agent built for AMD Express Track 2. Give it a short clip (30 seconds to 2 minutes) and it writes a caption in four requested styles: formal (precise and objective), sarcastic (dry and deadpan), humorous_tech (developer in-jokes), and humorous_non_tech (warm, jargon-free wit). Rather than describe the video and then restyle text, it captions in a single grounded pass: it samples frames densely across the clip (~1 per second) and, in one multimodal call, the model writes all four styles directly from what it sees, so nothing is lost in a handoff, and temporal coverage drives accuracy. A distinctness check retries if the four voices come back too similar, and every requested style is always returned. The scored deliverable is a headless Docker agent: it reads tasks from /input/tasks.json, streams frames straight from each video URL with ffmpeg (downloading only as a fallback), and writes /output/results.json with per-style fallbacks, an atomic write, and an adaptive time budget so the output is always complete and valid. Built with Fireworks AI (Kimi K2.6, a vision-language model) and ffmpeg for frame sampling.

Rebound: The Plan After the Plan Fails

A missed study session should not destroy an entire revision plan. But for many students, that is exactly what happens. Once the timetable slips, they are left with less time, more unfinished work, and no reliable way to decide what still matters. Rebound is built for that moment. When a student misses a session, Rebound does not simply move every task forward. It recalibrates the entire remaining plan around the time still available. A diagnostic test identifies strengths, weaknesses, and high-risk knowledge gaps. Rebound then combines those results with exam importance, topic dependencies, and remaining study time to determine which topics must be protected, which can be reduced, and what should be studied next. Its knowledge map and relationship graph reveal how concepts connect, highlighting foundational topics that unlock progress across the wider syllabus. Every adjustment is explained, so students understand not only what changed, but why. For students who have no time to rebuild their timetable manually, Rebound turns disruption into a clear recovery path. Most study tools help students make a plan. Rebound is designed for the moment that plan fails. It absorbs the disruption, recalculates the trade-offs, and gives students the confidence to focus on what can still make the biggest difference.