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.

Edit on GitHub

Streamlit AI technology page Hackathon projects

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

MenuTaste AI Agent

MenuTaste is a web-based enterprise AI agent for food entrepreneurs, cafes, restaurants, and small food businesses. It helps users turn a food or drink idea into a structured product-quality, nutrition, risk, market-fit, and launch-readiness report. The user enters a product name, ingredients, business type, location, customer segment, dietary focus, target price, and preparation complexity. MenuTaste then runs a multi-step workflow: it performs local ingredient and nutrition-signal analysis, detects allergens and dietary conflicts, scores the product across nutrition, quality, market fit, and operations, and sends a structured prompt to a Featherless open-source model for deeper business reasoning. The final output includes an executive summary, nutrition estimate, risk review, ingredient notes, Featherless AI reasoning, recommended actions, launch checklist, and downloadable Markdown and JSON reports. This makes MenuTaste useful for early-stage restaurants and food entrepreneurs who need to validate a menu item before piloting it with customers. The system combines deterministic local scoring with Featherless AI reasoning. This hybrid design keeps core food-risk checks explainable while using open-source LLM inference for strategy, recommendations, customer positioning, and launch planning. The app is designed as a production-style Streamlit web application and deployed on Vultr for the hackathon demo.

PROMETHEUS

PROMETHEUS is an autonomous competitive decision agent built for enterprises that need more than a report — they need a verdict. The system runs four agents in sequence, each with a distinct adversarial role: 1. SCOUT AGENT — deploys Gemini 2.5 Flash with live Google Search grounding to build a real-time evidence base: market data, competitor filings, analyst reports, news signals. Outputs a structured brief with citations. 2. CHALLENGER AGENT — reads Scout's findings and is explicitly instructed to find every reason the opportunity is overrated, the data is misleading, or the thesis is flawed. Real devil's advocacy — not polite hedging. 3. STRATEGIST AGENT — reads both Scout and Challenger. Produces an integrated strategic brief that identifies the core disagreements, weights them, and constructs actionable scenarios. Returns a DisagreementMatrix. 4. DECISION GATE — synthesises all three prior agents and produces the final verdict: GO, NO-GO, or MONITOR, with a calibrated confidence score (0–100%), key conditions, and explicit rationale. Three live scenarios are preloaded for instant demo: - OpenAI Market Entry → NO-GO at 65% confidence - Salesforce vs HubSpot Italy → MONITOR at 62% confidence - Tesla Competitive Threat Europe → GO at 88% confidence The UI populates each agent panel in sequence so judges can watch the reasoning unfold in real time — not just see the final answer. Tech stack: Gemini 2.5 Flash (all agents), Google Search grounding (Scout), FastAPI + Python backend deployed on Vultr Cloud Compute, Streamlit frontend on Streamlit Community Cloud, aiosqlite for analysis history. PROMETHEUS does what no existing AI tool does: it ends with a decision.

AgentOps-360: Autonomous Contract Risk Auditor

AgentOps-360 transitions corporate legal workflows from passive chatbot "copilots" into a proactive, fully autonomous decision-making engine built for real enterprise utility. The Problem: Enterprise procurement managers waste up to 15 hours manually verifying dense, multi-page vendor agreements. Under heavy deadline pressures, it is incredibly easy to miss hidden auto-renewal traps, ambiguous service level agreements (SLAs), and severe data privacy liabilities. Our Solution: AgentOps-360 provides a clean, single-file drop interface that autonomously extracts raw text, maps legal risks into prioritized structured data tables, and delivers an explicit, validated Go/No-Go directive with concrete risk mitigation advice. The Stack & Architecture: Deployed natively on isolated, high-performance Vultr Cloud Compute VMs for low latency and fixed hosting costs. To bypass open-source token limits under heavy enterprise traffic, we designed a parallel Map-Reduce workflow. The system chunks a 10-page PDF locally into 2-page text matrices, streaming them through the Featherless.ai gateway using an optimized open-source Mistral-7B-Instruct model layer. The platform enforces a Zero-Data-Retention posture in volatile RAM buffers, ensuring full compliance with GDPR and the EU AI Act.

PausePoint

The project, titled PausePoint, introduces a sophisticated technological intervention designed to mitigate digital procrastination and doomscrolling. Rather than deploying traditional application-blocking strategies that induce user frustration and subsequent circumvention, PausePoint implements a calculated layer of cognitive friction at the precise threshold of a distraction trigger. The system functions as an emotionally intelligent interceptor that halts an attempt to access a disruptive application and reroutes the user's behavioral trajectory through a structured psychological framework. The application leverages the ultra-low-latency capabilities of the Gemini 2.5 Flash model to process user inputs in real-time. When a individual attempts to transition into a state of distraction, PausePoint prompts them to identify the primary emotional catalyst driving their avoidance behavior, such as acute anxiety, cognitive fatigue, or boredom. The integrated artificial intelligence agent analyzes this emotional state, delivers a non-judgmental validation statement to address the root psychological discomfort, and immediately decomposes the overarching, avoided task into an exceptionally small micro-step that can be completed within two minutes with minimal cognitive overhead. Architecturally, the platform is engineered as a responsive web application utilizing Python and the Streamlit framework. This design ensures rapid state management, allowing the system to transition smoothly across the user lifecycle from an active distraction state to an emotional interception phase, a cognitive reframing view, and ultimately an unlocked focus state. By aligning advanced artificial intelligence with principles of behavioral science, PausePoint transforms an instinctive habit loop into an intentional moment of reflection, converting digital stagnation into immediate productivity momentum.

VoxCall Oracle: Live Audio Trading Agent

The latency gap in modern finance is a multi-million dollar problem. Traditional algorithmic trading relies on text-based transcripts that humans or bots read minutes after words are actually spoken on an earnings call. By the time the data is digested, the market has already moved. VoxCall Oracle is an end-to-end, production-ready pipeline designed to bridge the gap between raw audio and instant market execution. Our fully autonomous agent listens to live earnings calls and executes trades in real-time without human intervention. We utilized four core technologies to build this stack: 1. Speechmatics: We use their best-in-class speech-to-text API for real-time transcription and speaker diarization. This allows our agent to know exactly *what* was said and *who* said it (e.g., separating a confident CEO from a cautious reporter). 2. Featherless AI: To eliminate the risk of AI hallucinations, we route the transcript chunks into a 3-model financial ensemble via Featherless (Llama-Open-Finance, Fin-o1, and finance-chat). These specialized models vote on the market sentiment to ensure pinpoint accuracy. 3. LangGraph: In automated trading, safety is critical. We built a LangGraph state-machine that acts as a risk-gating firewall. Trades are only approved if the AI ensemble's confidence score exceeds a strict user-defined threshold (e.g., >75%). 4. Kraken CLI: Approved signals are instantly routed to the Kraken API for ultra-low-latency order execution. Our entire platform is wrapped in a premium, glassmorphism UI deployed on Streamlit Cloud, featuring a live Paper PnL tracker and execution logs. VoxCall Oracle isn't just a script—it's a fully functional, cloud-native hedge fund analyst.