{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"streamlit-cloud","slug":"streamlit-cloud","name":"Streamlit Cloud","type":"platform","url":"https://streamlit.io/cloud","page_url":"https://unfragile.ai/streamlit-cloud","categories":["deployment-infra"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"streamlit-cloud__cap_0","uri":"capability://automation.workflow.github.triggered.automatic.deployment.with.containerized.execution","name":"github-triggered automatic deployment with containerized execution","description":"Monitors GitHub repositories for commits and automatically builds isolated container environments with Python dependencies (from requirements.txt or pyproject.toml), then executes the Streamlit app without requiring manual deployment steps or infrastructure management. Uses webhook-based change detection and AWS-backed serverless execution to eliminate DevOps overhead for data science teams.","intents":["Deploy a Python data app to the web without managing servers or containers","Automatically update a live dashboard when I push code changes to GitHub","Share a working prototype with stakeholders without DevOps knowledge","Eliminate manual deployment steps and CI/CD pipeline configuration"],"best_for":["Data scientists prototyping dashboards and analyses","ML engineers demoing models with simple web interfaces","Teams needing quick internal tools without DevOps overhead","Researchers sharing interactive analyses without infrastructure expertise"],"limitations":["Requires GitHub repository (no GitLab, Bitbucket, or self-hosted Git support)","Full script re-execution on every interaction creates latency for compute-heavy apps (>10s per run becomes unusable)","No persistent state between deployments unless explicitly cached with @st.cache_data","Concurrent user limits unknown on free tier; likely throttled for shared infrastructure","No custom domain support documented; apps accessible only via Streamlit-assigned URLs"],"requires":["GitHub account with OAuth authorization","Python 3.7+ in repository","requirements.txt or pyproject.toml for dependency declaration","Public or private GitHub repository (Streamlit Cloud must have read access)"],"input_types":["Python source code (.py files)","Dependency manifests (requirements.txt, pyproject.toml, poetry.lock)","Secrets configuration (.streamlit/secrets.toml)"],"output_types":["Hosted HTTPS web application","Container logs and deployment status","Live app URL for sharing"],"categories":["automation-workflow","deployment-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"streamlit-cloud__cap_1","uri":"capability://automation.workflow.reactive.script.re.execution.with.widget.state.binding","name":"reactive script re-execution with widget state binding","description":"Implements a reactive programming model where the entire Python script re-executes top-to-bottom whenever a user interacts with a widget (button click, slider change, text input). Widget state is automatically captured and passed back into the script execution context, enabling interactive UIs without explicit event handlers or callback functions. This pattern eliminates the need for traditional request-response HTTP routing.","intents":["Build interactive dashboards where slider changes instantly update visualizations","Create data exploration tools where button clicks trigger new analyses","Implement multi-step workflows where each interaction advances the app state","Avoid writing explicit event handlers or callback logic for UI interactions"],"best_for":["Data scientists building exploratory dashboards","Researchers creating interactive papers and analyses","Prototypers who want rapid UI iteration without backend complexity","Teams building internal tools with simple, linear workflows"],"limitations":["Full script re-run on every interaction creates latency penalty; apps with >10 second computation per interaction become unusable","No persistent state between script executions unless explicitly cached with @st.cache_data or @st.cache_resource decorators","Memory overhead from re-executing entire script; complex data loading on every interaction is inefficient","No native support for background tasks or async processing; long-running operations block the UI","Debugging is harder because script re-runs on every widget interaction, making breakpoint-based debugging difficult"],"requires":["Python 3.7+","Streamlit library (pip install streamlit)","Understanding of Python control flow and function definitions","Familiarity with Streamlit widget API (st.button, st.slider, st.text_input, etc.)"],"input_types":["Widget interactions (button clicks, slider values, text input, file uploads)","User-provided data (uploaded CSV, JSON, images)","Query parameters in URL (for deep linking)"],"output_types":["Updated HTML/JavaScript UI components","Re-rendered visualizations and data tables","File downloads triggered by button clicks"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"streamlit-cloud__cap_10","uri":"capability://data.processing.analysis.file.upload.and.download.handling.with.st.file.uploader.and.st.download.button","name":"file upload and download handling with st.file_uploader and st.download_button","description":"Provides built-in widgets for handling file uploads (st.file_uploader) and downloads (st.download_button) without requiring form encoding or multipart request handling. Uploaded files are temporarily stored in memory and accessible as file-like objects; downloads are triggered by button clicks and streamed to the user's browser. Supports multiple file types and formats with automatic MIME type detection.","intents":["Allow users to upload CSV, Excel, or image files for processing","Enable users to download processed data or generated reports","Build data transformation tools that accept user-provided files","Create file conversion or analysis apps without backend file storage"],"best_for":["Data scientists building data processing tools","Teams creating file conversion or analysis apps","Researchers sharing interactive data analysis tools","Developers building simple ETL or data cleaning apps"],"limitations":["Uploaded files are stored in memory; large files (>100MB) may cause memory issues or timeouts (exact limits unknown)","No persistent file storage; uploaded files are deleted after script execution","No progress indicators for large file uploads or downloads","File upload size limits are unknown; may be restricted on free tier","No support for streaming uploads or resumable downloads"],"requires":["Python 3.7+","Streamlit library (pip install streamlit)","File handling libraries (pandas for CSV, openpyxl for Excel, PIL for images, etc.)"],"input_types":["File uploads (any file type)","File content (bytes or text)"],"output_types":["Downloaded files (any format)","File-like objects for processing"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"streamlit-cloud__cap_11","uri":"capability://text.generation.language.real.time.data.streaming.with.st.write.stream.and.st.chat.message","name":"real-time data streaming with st.write_stream and st.chat_message","description":"Provides streaming capabilities for displaying real-time data updates and LLM token streaming via st.write_stream (for iterative output) and st.chat_message (for chat-like interfaces). st.write_stream accepts iterables or generators and renders output incrementally as data arrives, enabling live updates without waiting for full computation. st.chat_message creates message containers for chat-style interactions with automatic styling and layout.","intents":["Display LLM responses token-by-token as they are generated","Build chat interfaces that mimic ChatGPT or similar conversational apps","Stream real-time data updates or live analysis results","Create interactive chatbots with streaming responses"],"best_for":["Developers building LLM-powered chat applications","Teams creating AI assistants or chatbots","Researchers building interactive AI demos","Projects requiring real-time data visualization or streaming output"],"limitations":["Streaming is limited to output display; no streaming input or bidirectional communication","st.write_stream requires iterables or generators; not compatible with all data sources","Chat message styling is fixed; limited customization of appearance","No built-in message persistence; chat history is lost on app reload unless explicitly cached","Streaming performance depends on network latency and client-side rendering speed"],"requires":["Python 3.7+","Streamlit library (pip install streamlit)","LLM API client (OpenAI, Anthropic, etc.) for token streaming","Understanding of Python generators or iterables"],"input_types":["Iterables or generators (for st.write_stream)","Chat messages (for st.chat_message)","LLM API responses (streamed tokens)"],"output_types":["Streamed text output (rendered incrementally)","Chat message containers with styling","Real-time data updates"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"streamlit-cloud__cap_12","uri":"capability://image.visual.data.visualization.integration.with.plotly.matplotlib.altair.and.bokeh","name":"data visualization integration with plotly, matplotlib, altair, and bokeh","description":"Provides native rendering support for popular Python visualization libraries through dedicated functions (st.plotly_chart, st.matplotlib_figure, st.altair_chart, st.bokeh_chart). Visualizations are embedded directly in the app without requiring manual HTML/JavaScript code. Supports interactive features like hover tooltips, zooming, and clicking (for Plotly and Altair), and automatically handles responsive sizing and browser compatibility.","intents":["Display interactive charts and graphs without writing HTML/JavaScript","Create dashboards with multiple visualizations","Build exploratory data analysis tools with rich visualizations","Embed interactive plots from Jupyter notebooks into Streamlit apps"],"best_for":["Data scientists building dashboards and visualizations","Teams creating exploratory data analysis tools","Researchers sharing interactive visualizations","Developers building data-driven applications"],"limitations":["Limited to supported libraries (Plotly, Matplotlib, Altair, Bokeh); custom visualization libraries may not work","Matplotlib figures are rendered as static images; interactivity is limited compared to Plotly or Altair","Large datasets may cause performance issues; visualization rendering can be slow for >100k data points","No built-in caching for visualization rendering; charts are re-rendered on every script execution unless explicitly cached","Responsive sizing is automatic but may not work well for all custom visualization configurations"],"requires":["Python 3.7+","Streamlit library (pip install streamlit)","Visualization library (plotly, matplotlib, altair, bokeh)","Data in format compatible with visualization library (Pandas DataFrame, NumPy array, etc.)"],"input_types":["Plotly Figure objects","Matplotlib Figure objects","Altair Chart objects","Bokeh Figure objects","Pandas DataFrames or NumPy arrays"],"output_types":["Rendered interactive charts","Hover tooltips and interactive features","Responsive visualizations"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"streamlit-cloud__cap_13","uri":"capability://data.processing.analysis.dataframe.rendering.and.interaction.with.st.dataframe","name":"dataframe rendering and interaction with st.dataframe","description":"Renders Pandas DataFrames as interactive HTML tables with built-in sorting, filtering, and column selection. Tables are responsive and support large datasets with virtual scrolling to avoid rendering performance issues. Supports conditional formatting, column width customization, and data type-specific rendering (dates, numbers, etc.). Users can interact with tables via sorting and filtering without triggering script re-execution.","intents":["Display tabular data in interactive tables without writing HTML","Allow users to sort and filter data without backend processing","Build data exploration tools with interactive tables","Render large datasets efficiently without performance degradation"],"best_for":["Data scientists exploring and presenting tabular data","Teams building data dashboards with interactive tables","Researchers sharing datasets in interactive format","Developers building data-driven applications"],"limitations":["Sorting and filtering are client-side only; large datasets (>100k rows) may have performance issues","No built-in pagination; virtual scrolling is used but may be slow for very large datasets","Limited customization of table appearance; no CSS override mechanism","No built-in export to CSV or Excel; users must implement download functionality separately","Column width customization is limited; automatic sizing may not work well for all data types"],"requires":["Python 3.7+","Streamlit library (pip install streamlit)","Pandas DataFrame"],"input_types":["Pandas DataFrame","Column configuration (width, format, etc.)"],"output_types":["Rendered interactive HTML table","Sorted and filtered data (client-side)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"streamlit-cloud__cap_2","uri":"capability://text.generation.language.python.to.html.ui.component.rendering.with.declarative.syntax","name":"python-to-html ui component rendering with declarative syntax","description":"Converts Python function calls (st.write(), st.button(), st.dataframe(), st.plotly_chart(), etc.) into interactive HTML/CSS/JavaScript UI components rendered in the browser. Uses a declarative API where developers specify what to display, and Streamlit handles the underlying DOM manipulation and browser communication. Supports native integration with popular visualization libraries (Plotly, Matplotlib, Altair, Bokeh) and data structures (Pandas DataFrames, NumPy arrays).","intents":["Display data visualizations and charts without writing HTML/CSS/JavaScript","Create interactive tables and data grids from Pandas DataFrames","Build forms with text inputs, dropdowns, and file uploads using Python syntax","Render markdown content, images, and media without HTML boilerplate"],"best_for":["Data scientists who know Python but not web development","Teams building internal dashboards and data exploration tools","Researchers creating interactive visualizations for papers","Prototypers who need rapid UI iteration without frontend expertise"],"limitations":["Limited customization of UI appearance; no CSS override mechanism documented","Widget library is smaller than React/Vue ecosystems; custom components require workarounds","No native support for complex layouts beyond columns and expanders; advanced grid systems not available","Visualization libraries must be compatible with Streamlit's rendering pipeline; some libraries may not work","Mobile responsiveness is limited; apps are optimized for desktop browsers"],"requires":["Python 3.7+","Streamlit library (pip install streamlit)","Visualization libraries (Plotly, Matplotlib, Altair, Bokeh) for charts","Pandas for DataFrame rendering"],"input_types":["Python objects (lists, dicts, DataFrames, NumPy arrays, Matplotlib figures, Plotly figures)","Markdown strings","Image files (PNG, JPG, SVG)","Audio and video files"],"output_types":["Rendered HTML/CSS/JavaScript UI components","Interactive charts and visualizations","Data tables with sorting and filtering","Forms and input widgets"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"streamlit-cloud__cap_3","uri":"capability://memory.knowledge.caching.and.memoization.with.st.cache.data.and.st.cache.resource.decorators","name":"caching and memoization with @st.cache_data and @st.cache_resource decorators","description":"Provides two decorator-based caching mechanisms to prevent redundant computation across script re-executions: @st.cache_data caches function results based on input parameters (suitable for data loading and transformations), while @st.cache_resource caches expensive objects like database connections or ML models that should persist across multiple script runs. Uses function signature hashing to determine cache validity and supports TTL-based expiration.","intents":["Load large datasets once and reuse across multiple script executions","Cache expensive ML model inference or database queries","Maintain persistent database connections across widget interactions","Avoid re-downloading or re-processing data on every user interaction"],"best_for":["Data scientists building dashboards with large datasets","ML engineers caching model inference results","Teams querying databases repeatedly in interactive apps","Developers optimizing app performance for latency-sensitive interactions"],"limitations":["Cache is per-app instance; no distributed caching across multiple deployments","Cache invalidation is based on function signature hashing; changes to function body do not invalidate cache unless input parameters change","No explicit cache eviction mechanism; relies on Streamlit's internal memory management","Cache size limits are unknown; large datasets may cause memory issues on free tier","@st.cache_resource objects must be thread-safe if accessed concurrently (documentation does not clarify concurrency model)"],"requires":["Python 3.7+","Streamlit library (pip install streamlit)","Understanding of Python decorators","Knowledge of which functions are expensive and should be cached"],"input_types":["Python functions (any function that takes parameters and returns a value)","Function parameters (used for cache key generation)"],"output_types":["Cached function results (returned without re-execution)","Cached resource objects (database connections, ML models, etc.)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"streamlit-cloud__cap_4","uri":"capability://safety.moderation.secure.credential.management.via.streamlit.secrets","name":"secure credential management via streamlit secrets","description":"Provides a built-in secrets management system that injects environment variables into the app execution context without exposing them in source code or logs. Secrets are stored in .streamlit/secrets.toml (local development) or via the Streamlit Cloud UI (production), and accessed in code via st.secrets['key_name']. Supports TOML syntax for nested configuration and integrates with external secret stores (documented as 'securely connect to all your data sources').","intents":["Store API keys and database credentials without committing them to GitHub","Access secrets in code without hardcoding sensitive values","Manage different secrets for development and production environments","Prevent accidental exposure of credentials in logs or error messages"],"best_for":["Teams deploying apps that require API keys or database credentials","Developers building apps that integrate with external services","Organizations with security requirements for credential management","Projects using multiple environments (dev, staging, production)"],"limitations":["Secrets are stored in plaintext in .streamlit/secrets.toml during local development; no encryption at rest","No audit logging for secret access; cannot track which app accessed which secret","No role-based access control; all secrets are accessible to all code in the app","Integration with external secret stores is documented as possible but not detailed; specific protocols unknown","No secret rotation mechanism; manual updates required"],"requires":["Python 3.7+","Streamlit library (pip install streamlit)",".streamlit/secrets.toml file (local development) or Streamlit Cloud UI (production)","Knowledge of TOML syntax for configuration"],"input_types":["TOML configuration files (.streamlit/secrets.toml)","Streamlit Cloud UI form inputs","Environment variables (for external secret stores)"],"output_types":["Python dictionary-like object (st.secrets)","Injected environment variables in app execution context"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"streamlit-cloud__cap_5","uri":"capability://safety.moderation.per.app.access.control.via.viewer.allow.lists","name":"per-app access control via viewer allow-lists","description":"Implements simple access control by allowing app owners to specify a list of email addresses that can view the deployed app. Uses GitHub OAuth or email-based authentication to verify viewer identity before granting access. Does not support role-based access control (RBAC), SAML, or fine-grained permissions; all authenticated viewers have identical access to the entire app.","intents":["Share a dashboard with a specific team without making it publicly accessible","Restrict access to a demo or prototype to invited users only","Prevent unauthorized access to apps containing sensitive data","Control who can view an app without implementing custom authentication logic"],"best_for":["Teams sharing internal dashboards with colleagues","Researchers sharing interactive papers with collaborators","Developers demoing prototypes to specific stakeholders","Small organizations with simple access control requirements"],"limitations":["No role-based access control (RBAC); all authenticated viewers have identical permissions","No fine-grained permissions (e.g., cannot restrict access to specific data or features within an app)","No SAML or enterprise SSO support; only GitHub OAuth or email-based authentication","Allow-list must be manually maintained; no group-based access management","No audit logging for viewer access; cannot track who accessed the app or when"],"requires":["Streamlit Cloud account","GitHub account (for OAuth authentication)","Email addresses of viewers to add to allow-list","App deployed on Streamlit Cloud"],"input_types":["Email addresses (for allow-list)","GitHub OAuth credentials (for authentication)"],"output_types":["Access granted or denied to app","Authenticated user identity (available in app via st.session_state)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"streamlit-cloud__cap_6","uri":"capability://automation.workflow.live.code.updates.with.automatic.app.reloading","name":"live code updates with automatic app reloading","description":"Detects changes to Python source files and automatically reloads the app in the browser without requiring manual restart or page refresh. Uses file system watchers (during local development with streamlit run) and webhook-based change detection (on Streamlit Cloud after git push) to trigger app reloads. Preserves widget state during reloads when possible, allowing developers to iterate on code without losing user input.","intents":["Iterate on app code and see changes reflected immediately in the browser","Debug code changes without manually restarting the app","Develop dashboards with fast feedback loops","Test UI changes without losing widget state or user input"],"best_for":["Data scientists prototyping dashboards iteratively","Developers building interactive tools with rapid feedback requirements","Teams collaborating on app development with frequent code changes","Researchers experimenting with different visualizations and analyses"],"limitations":["Widget state is not always preserved during reloads; complex state may be lost","Reloads can be slow for apps with large data loading or computation; may take 5-10+ seconds","File system watchers may miss rapid file changes or have race conditions","On Streamlit Cloud, reloads are triggered by git push, not real-time file changes; delay of seconds to minutes possible","No selective reloading; entire app reloads even if only one function changed"],"requires":["Python 3.7+","Streamlit library (pip install streamlit)","Text editor or IDE for editing Python files","For Streamlit Cloud: GitHub repository with automatic deployment enabled"],"input_types":["Python source code changes (.py files)","File system events (local development) or git commits (Streamlit Cloud)"],"output_types":["Reloaded app in browser","Updated UI components and visualizations"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"streamlit-cloud__cap_7","uri":"capability://search.retrieval.community.app.discovery.and.forking","name":"community app discovery and forking","description":"Provides a public gallery of Streamlit apps deployed on Streamlit Cloud, allowing users to discover, view, and fork (copy) other developers' apps. Forking creates a new GitHub repository with the original app's code, enabling users to modify and deploy their own version without starting from scratch. Includes app ratings, descriptions, and tags for discoverability.","intents":["Discover example Streamlit apps and learn from community projects","Find templates or starting points for building similar apps","Share apps with the broader Streamlit community","Build on top of existing apps by forking and modifying them"],"best_for":["Developers learning Streamlit by example","Teams looking for templates or reference implementations","Researchers sharing interactive analyses with the community","Hobbyists and students exploring data science projects"],"limitations":["Community gallery is opt-in; not all apps are discoverable","No quality control or vetting of community apps; security and correctness not guaranteed","Forking requires GitHub account and creates a new repository; not a simple copy mechanism","No built-in version control or update mechanism; forked apps are independent copies","Community ratings and reviews are not mentioned; discoverability relies on tags and descriptions"],"requires":["Streamlit Cloud account","GitHub account (for forking)","Internet access to browse community gallery"],"input_types":["App metadata (title, description, tags, ratings)","GitHub repository URL (for forking)"],"output_types":["Community gallery listing","Forked GitHub repository","New Streamlit Cloud deployment"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"streamlit-cloud__cap_8","uri":"capability://automation.workflow.multi.page.app.structure.with.st.navigation","name":"multi-page app structure with st.navigation","description":"Enables building multi-page applications by organizing Python files into a pages/ directory structure and using st.navigation() to define navigation between pages. Each page is a separate Python file that executes independently, allowing developers to split complex apps into logical sections. Navigation state is preserved across page transitions, and URL routing is automatic based on file names.","intents":["Build complex dashboards with multiple sections or tabs","Organize large apps into logical pages without monolithic code files","Create multi-step workflows where users navigate between pages","Implement sidebar navigation or tab-based interfaces"],"best_for":["Teams building complex dashboards with multiple features","Developers creating multi-step workflows or wizards","Researchers organizing interactive papers into sections","Projects requiring clear separation of concerns across pages"],"limitations":["Each page is a separate Python file; shared state between pages requires explicit session state management (st.session_state)","No automatic code splitting or lazy loading; all pages are loaded into memory","URL routing is based on file names; custom URL paths are not supported","Navigation UI must be manually implemented; no built-in sidebar or tab components for multi-page apps","Page transitions trigger full script re-execution; state is lost unless explicitly cached"],"requires":["Python 3.7+","Streamlit library (pip install streamlit)","pages/ directory structure in app root","Understanding of st.session_state for cross-page state management"],"input_types":["Python files in pages/ directory","st.navigation() configuration","URL parameters for deep linking"],"output_types":["Multi-page app with navigation","URL-based routing to different pages","Preserved state across page transitions"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"streamlit-cloud__cap_9","uri":"capability://memory.knowledge.session.state.management.with.st.session.state","name":"session state management with st.session_state","description":"Provides a dictionary-like object (st.session_state) that persists data across script re-executions within a single user session. Session state is tied to browser tabs and survives widget interactions, allowing developers to maintain state without explicit caching decorators. Supports initialization with default values and can be used to implement multi-step workflows or preserve user input across page transitions.","intents":["Maintain user input or application state across multiple widget interactions","Implement multi-step workflows where each step depends on previous state","Share state between multiple pages in a multi-page app","Preserve form data or user selections without re-fetching from external sources"],"best_for":["Developers building multi-step workflows or wizards","Teams implementing complex interactive apps with state dependencies","Researchers creating interactive analyses with multiple stages","Projects requiring state persistence across page transitions"],"limitations":["Session state is per-browser-tab; closing the tab loses all state","No persistence to disk or database; state is lost on app reload or server restart","No built-in synchronization across multiple browser tabs; state is isolated per tab","Debugging state mutations is difficult because state changes are implicit and scattered throughout the script","No type safety or validation; state values can be any Python object"],"requires":["Python 3.7+","Streamlit library (pip install streamlit)","Understanding of Python dictionaries and object references","Knowledge of when to initialize vs. update session state"],"input_types":["Python objects (any serializable type)","Widget values (automatically synced to session state via key parameter)"],"output_types":["Persisted state across script re-executions","State values accessible throughout the app"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"streamlit-cloud__headline","uri":"capability://deployment.infra.free.hosting.platform.for.streamlit.applications","name":"free hosting platform for streamlit applications","description":"Streamlit Cloud is a free hosting platform specifically designed for deploying interactive data science dashboards, machine learning demos, and AI tools built with Streamlit directly from GitHub repositories, enabling easy sharing and collaboration.","intents":["best free platform for Streamlit apps","Streamlit app hosting for data science","deploy ML demos with Streamlit","collaborative platform for Streamlit applications","Streamlit hosting solutions","free deployment for AI tools"],"best_for":["data scientists","ML developers","AI tool creators"],"limitations":[],"requires":["GitHub account"],"input_types":["Streamlit apps"],"output_types":["deployed web applications"],"categories":["deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["GitHub account with OAuth authorization","Python 3.7+ in repository","requirements.txt or pyproject.toml for dependency declaration","Public or private GitHub repository (Streamlit Cloud must have read access)","Python 3.7+","Streamlit library (pip install streamlit)","Understanding of Python control flow and function definitions","Familiarity with Streamlit widget API (st.button, st.slider, st.text_input, etc.)","File handling libraries (pandas for CSV, openpyxl for Excel, PIL for images, etc.)","LLM API client (OpenAI, Anthropic, etc.) for token streaming"],"failure_modes":["Requires GitHub repository (no GitLab, Bitbucket, or self-hosted Git support)","Full script re-execution on every interaction creates latency for compute-heavy apps (>10s per run becomes unusable)","No persistent state between deployments unless explicitly cached with @st.cache_data","Concurrent user limits unknown on free tier; likely throttled for shared infrastructure","No custom domain support documented; apps accessible only via Streamlit-assigned URLs","Full script re-run on every interaction creates latency penalty; apps with >10 second computation per interaction become unusable","No persistent state between script executions unless explicitly cached with @st.cache_data or @st.cache_resource decorators","Memory overhead from re-executing entire script; complex data loading on every interaction is inefficient","No native support for background tasks or async processing; long-running operations block the UI","Debugging is harder because script re-runs on every widget interaction, making breakpoint-based debugging difficult","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.3,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.15,"match_graph":0.25,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:28.695Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=streamlit-cloud","compare_url":"https://unfragile.ai/compare?artifact=streamlit-cloud"}},"signature":"4/fhyvAn/DQ8MRl/gNlhvd0D7kYeMkot9/g6Vem3HOyPGW4or1bZ9bGeFXYYFUmggig/WTgxhREWE0xTxwPvBQ==","signedAt":"2026-06-20T14:34:04.592Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/streamlit-cloud","artifact":"https://unfragile.ai/streamlit-cloud","verify":"https://unfragile.ai/api/v1/verify?slug=streamlit-cloud","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}