Streamlit

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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.

SafetySignal Desk: Multi-Agent Recall Review

SafetySignal Desk is a multi-agent product-safety recall review room built on Band. When complaints arrive about a snack bar, seven specialist AI agents collaborate through Band as registered participants with distinct roles and identities to prepare a human-reviewed recall escalation decision. In the demo, Complaint Intake extracts structured facts from raw complaints. Pattern Detection finds a same-batch cluster. Label & Ingredient compares the consumer label against the supplier sheet and catches a suspected undeclared peanut allergen. Batch Trace quantifies market exposure. Recall Precedent pulls real records from the openFDA Food Enforcement API. Regulatory Risk scores the case on a transparent 100-point rubric. Customer & Retailer Response drafts safe communications, but every outbound message remains a draft until human approval. Band is the actual collaboration layer, not a notifier. Agents hand off work by mentioning each other, share structured findings as Band messages and events, and build a live evidence chain that the human recall manager can review. The final output is an audit-ready Safety Decision Packet plus the full Band transcript as the audit trail. The system is deliberately hybrid. Counts, scores, batch exposure, and allergen mismatch checks are deterministic Python for auditability. LLMs handle language tasks such as extraction, summarization, reasoning support, and drafting, each with deterministic guardrails. The AI never issues recalls, sends notices, or makes regulatory filings on its own. It prepares the evidence, blocks unsafe actions, and routes the final decision to a human recall manager.

Sangam : Polypharmacy Safety Council

Sangam is a multi-agent pharmaceutical safety system built on Band AI's infrastructure. Six specialist agents — Intake, PatientProfile, StructuralBio, PKPD, EvidenceRAG, and ComplianceGuard — coordinate via @mention routing inside a shared Band room to screen any combination of allopathic drugs and Ayurvedic herbs for dangerous interactions. The problem it solves is real and largely ignored. India has the world's highest rate of concurrent allopathic and traditional medicine use. Up to 70% of patients never disclose herbal supplement use to their doctor. No clinical tool exists to screen these combinations systematically — every major drug interaction checker screens against the Western pharmacopoeia only, with zero Ayurvedic herb coverage. Each agent handles a distinct analytical layer. The Intake Agent parses free-text patient queries and fetches compound data from PubChem. PatientProfile computes a personalized clearance modifier based on CYP2C9/3A4 genotype, renal function, and age. StructuralBio queries a curated molecular docking database of 26 drug-herb pairs across six enzyme targets — CYP1A2, CYP2C9, CYP2C19, CYP3A4, P-gp, and OCT — returning binding affinity in ΔG kcal/mol. The PKPD Agent runs a one-compartment pharmacokinetic model and computes AUC percentage change with a full 48-hour concentration curve. EvidenceRAG retrieves supporting findings from a curated corpus of 70 peer-reviewed studies and traditional pharmacology texts. ComplianceGuard synthesizes all five upstream reports and issues a RED, YELLOW, or GREEN verdict with confidence score, clinical action, and regulatory disclaimer. The system also includes a fast deterministic combination screener at /api/interactions/screen that returns pairwise risk verdicts in milliseconds without an LLM call — built for point-of-care use. The stack is FastAPI backend, React + Vite frontend with WebSocket streaming, ChromaDB vector index, DeepSeek LLM, Docker Compose deployment, and GitHub Actions CI.

Injury Risk Review

Injury Risk Review mimics how a sports science department actually debates injury risk: instead of one model announcing a verdict, four specialized agents argue it out. A Load Analyst flags concerning training-loads patterns from raw data. A Context Skeptic actively hunts for a non-injury explanation, position norms, fixture congestion, injury history, before accepting the flag, and can genuinely overturn it. A Conductor owns the room-level coordination, routing between specialists and tracking debate rounds. A Report Writer joins only once the debate resolves, producing a coaching note that preserves any unresolved disagreement rather than forcing a false consensus. All four collaborate through Band as a real coordination layer.

PaperBand AI

PaperBand AI is a multi-agent research paper review system built for academics, students, and conference organizers who need fast, structured feedback on academic work. Instead of waiting weeks for peer review, users simply upload any research PDF and three specialized AI agents go to work collaboratively. Agent 1 (Summarizer) reads the full paper and extracts the title, authors, research problem, methodology, key results, and conclusion into a clean structured format. Agent 2 (Critic) receives that summary through a shared Band Room message bus and performs a rigorous peer-review-style analysis — identifying strengths, weaknesses, missing experiments, and limitations. Agent 3 (Recommender) reads both the summary and critique, then scores the paper from 1 to 10 and issues a formal publication decision: Accept, Accept with Minor Revisions, Major Revisions Required, or Reject — with a written justification and future research suggestions. All three agents communicate through a custom BandRoom, a shared in-process message bus that simulates real agent collaboration. The entire pipeline runs on Groq's free LLaMA 3.3 70B model, making it blazing fast and completely free to use. The frontend is built in Streamlit with a custom dark academic UI, and the project is structured as a clean modular Python codebase with separate agent classes, a PDF reader utility, and secure API key management via dotenv. PaperBand AI was built in one day as a hackathon project by Team IDEA — a five-person team specializing in Data and Agentic AI.

GreenLight AI

GreenLight AI is a multi-agent due-diligence system for film investors. A producer pastes a project package — script, budget, crew list, deal memos — into a Band chat room. Seven specialized AI agents then collaboratively analyze the project: five specialists work in parallel (ScriptAnalyst, BudgetAuditor, MarketIntel, LegalEagle, TalentScout), then RedTeam cross-examines for cross-agent contradictions, and finally CRO synthesizes a weighted scorecard with a GREENLIGHT / CONDITIONAL / PASS verdict. The architecture uses Band's @mention routing as its native coordination protocol — no central orchestrator. Three framework adapters (Google ADK, LangGraph, CrewAI) and three Gemini model tiers (Flash-Lite, Flash, Pro) all collaborate through Band's chat-as-protocol. Each agent's prompt ends with an explicit @mention handoff to the next phase, so Phase 1 → Phase 2 → Phase 3 self-propagates through Band's mention router. The killer demo finding on our "Deep Horizon" sample project: a ~$71M VFX budget shortfall surfaced via cross-agent examination — ScriptAnalyst identified 60+ complex VFX shots requiring an $80M-$150M minimum budget tier, while BudgetAuditor flagged the actual $8.5M total as catastrophically inadequate. That insight only emerged when one agent compared its numbers against another. RedTeam's adversarial cross-examination then issued four formal challenges, all resolved, before CRO synthesized the final Conditional verdict with specific approval conditions. Built for Track 3 — Regulated & High-Stakes Workflows. Film financing is a $30B+/year market with no AI-native due-diligence tool. The architecture generalizes to any multi-document, multi-discipline due-diligence workflow: TV greenlighting, real-estate development, infrastructure financing, regulatory filings.

StudyBand — Multi-Agent AI Study System

StudyBand is a multi-agent educational platform built for Track 1 (Internal Enterprise Workflows). It replaces the slow, manual process students go through to study a topic — researching, rewriting notes simply, creating practice questions, and checking answers — with four specialized AI agents that hand off work to each other automatically through Band.ai. The Researcher agent gathers structured study notes on any topic. It passes these to the Simplifier agent, which rewrites them in clear, education-level-appropriate language. The Quiz Master agent then generates multiple-choice questions from the simplified notes. Finally, the Evaluator agent grades the student's answers, gives encouraging feedback, and — if the score is below 80% — automatically triggers the Quiz Master to generate a shorter remedial quiz on the weak topics, creating a real feedback loop between agents rather than a one-way pipeline. All agent-to-agent communication happens inside a shared Band.ai room using @mentions, the same way a human team would hand off tasks in Slack — Band is the actual coordination layer, not a wrapper around a single LLM call. Built with Band.ai, Groq (Llama 3.3 70B for low-latency inference), AI/ML API (for switching between GPT-4o, Claude, and DeepSeek), LangGraph, Python, and Streamlit. Deployed live on Render with both the UI and all 4 agents running together. Beyond the hackathon, StudyBand has a clear path to revenue: a low-cost monthly subscription for individual students, white-label licensing to coaching institutes, or direct adoption by universities as an internal learning tool.