{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-streamlit","slug":"pypi-streamlit","name":"streamlit","type":"framework","url":"https://pypi.org/project/streamlit/","page_url":"https://unfragile.ai/pypi-streamlit","categories":["app-builders"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-streamlit__cap_0","uri":"capability://automation.workflow.reactive.python.to.web.ui.compilation.with.automatic.reruns","name":"reactive python-to-web ui compilation with automatic reruns","description":"Streamlit compiles Python scripts into interactive web UIs by executing the entire script top-to-bottom on every state change, using a reactive execution model where widget interactions trigger full reruns with cached intermediate results. This differs from traditional web frameworks by eliminating explicit request-response routing—developers write imperative Python code that Streamlit automatically converts to reactive components, managing session state and rerun cycles internally through a delta-based protocol that only sends UI changes to the browser.","intents":["Build interactive data dashboards without learning HTML/CSS/JavaScript","Prototype data science workflows with minimal boilerplate","Share analysis scripts as shareable web apps without backend infrastructure","Iterate rapidly on data visualizations with hot-reload feedback"],"best_for":["Data scientists and analysts building internal tools","Solo developers prototyping data-driven applications","Teams migrating from Jupyter notebooks to production-ready apps"],"limitations":["Full script reruns on every interaction can cause performance degradation with large datasets (>100MB in memory)","No built-in multi-page routing until v1.18; requires workarounds for complex navigation","Session state persists only in browser memory; no native database persistence layer","Difficult to implement complex stateful workflows requiring fine-grained control over component lifecycle"],"requires":["Python 3.7+","pip or conda package manager","No external web server required for local development"],"input_types":["Python code (scripts)","Data frames (pandas, polars)","Numeric arrays (numpy)","File uploads (CSV, JSON, images)"],"output_types":["Interactive HTML web UI","Rendered charts and visualizations","Downloadable artifacts (CSV, images)"],"categories":["automation-workflow","web-framework"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-streamlit__cap_1","uri":"capability://automation.workflow.declarative.widget.binding.with.automatic.state.synchronization","name":"declarative widget binding with automatic state synchronization","description":"Streamlit provides a library of widgets (sliders, text inputs, dropdowns, file uploaders) that automatically bind to Python variables and synchronize state bidirectionally. When a user interacts with a widget, Streamlit captures the new value, updates the corresponding Python variable, and triggers a rerun of the script with the new state. This is implemented through a widget registry that maps UI component IDs to Python variable names, with state stored in a session object that persists across reruns within a single browser session.","intents":["Capture user input without writing event handlers or form submission logic","Build parameter exploration interfaces where changing inputs instantly updates outputs","Create multi-step workflows where widget values flow through computation pipelines","Implement conditional UI rendering based on widget state"],"best_for":["Developers unfamiliar with web frameworks seeking rapid prototyping","Data scientists building exploratory analysis tools","Teams building internal dashboards with simple to moderate interactivity"],"limitations":["Widget state is session-scoped; no cross-session persistence without external storage","Complex interdependent widget logic can become difficult to reason about due to full-script reruns","No native support for debouncing or throttling widget updates, leading to excessive reruns on rapid input changes","Limited customization of widget appearance; styling is constrained to Streamlit's design system"],"requires":["Python 3.7+","Streamlit library imported in script"],"input_types":["User interactions (clicks, text input, slider movement, file selection)"],"output_types":["Python variables with updated values","Conditional UI elements based on widget state"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-streamlit__cap_10","uri":"capability://data.processing.analysis.dataframe.display.and.interaction.with.st.dataframe","name":"dataframe display and interaction with st.dataframe","description":"Streamlit provides st.dataframe widget that renders pandas/polars DataFrames as interactive HTML tables with built-in sorting, filtering, and column selection. The widget uses a virtualized rendering approach to handle large DataFrames (100k+ rows) efficiently by only rendering visible rows. Users can click column headers to sort, use search boxes to filter, and resize columns. The implementation uses a custom JavaScript table component that communicates with the Streamlit backend to handle sorting and filtering operations.","intents":["Display tabular data in interactive tables without custom HTML/CSS","Allow users to sort and filter DataFrames without writing code","Render large DataFrames efficiently without browser performance issues","Export DataFrames as CSV or JSON from the UI"],"best_for":["Data scientists exploring datasets interactively","Teams building data exploration dashboards","Developers displaying query results or database tables"],"limitations":["Sorting and filtering are client-side only; large DataFrames (>100k rows) can cause browser lag","No built-in support for custom cell rendering or conditional formatting","Column selection is limited to show/hide; no reordering or pinning","Export functionality is limited to CSV; no Excel or Parquet export"],"requires":["Python 3.7+","Streamlit library","pandas or polars DataFrame"],"input_types":["pandas DataFrame","polars DataFrame","numpy array"],"output_types":["Interactive HTML table with sorting/filtering"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-streamlit__cap_2","uri":"capability://image.visual.built.in.data.visualization.with.matplotlib.plotly.altair.integration","name":"built-in data visualization with matplotlib/plotly/altair integration","description":"Streamlit provides native rendering functions for popular visualization libraries (st.pyplot, st.plotly_chart, st.altair_chart) that automatically embed charts into the web UI without requiring explicit HTML/JavaScript configuration. These functions accept library-native objects (matplotlib Figure, plotly Figure, altair Chart) and handle serialization, responsive sizing, and interactivity. The integration is shallow—Streamlit acts as a renderer rather than a wrapper, allowing developers to use the full feature set of each library while Streamlit manages display and caching.","intents":["Display matplotlib/plotly/altair charts in web apps without learning web rendering","Build interactive dashboards with multiple chart types","Cache expensive chart computations to avoid recomputation on reruns","Export visualizations as static images or interactive HTML"],"best_for":["Data scientists familiar with matplotlib/plotly/altair","Teams building analytics dashboards with standard chart types","Developers seeking quick visualization without D3.js or custom JavaScript"],"limitations":["Chart interactivity is limited to library-native features; no cross-chart brushing or linked selections without custom JavaScript","Large datasets (>10k points) can cause browser performance issues with plotly due to full DOM rendering","No native support for real-time streaming updates; requires manual refresh or polling","Altair charts are limited to Vega-Lite specification; complex custom visualizations require matplotlib or plotly"],"requires":["Python 3.7+","matplotlib, plotly, or altair library installed","Streamlit library"],"input_types":["matplotlib Figure objects","plotly Figure objects","altair Chart objects","pandas DataFrames (for altair)"],"output_types":["Rendered HTML canvas (matplotlib)","Interactive HTML (plotly, altair)","PNG/SVG static images (via export)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-streamlit__cap_3","uri":"capability://data.processing.analysis.session.scoped.caching.with.dependency.tracking","name":"session-scoped caching with dependency tracking","description":"Streamlit provides @st.cache_data and @st.cache_resource decorators that memoize function results across script reruns within a single session, using function arguments as cache keys. The caching layer tracks dependencies implicitly—if a function's arguments change, the cache is invalidated and the function reexecutes. This is implemented through a decorator that wraps function calls, serializes arguments to create cache keys, and stores results in a session-scoped dictionary. Developers can also manually clear cache or set TTL (time-to-live) for cached values.","intents":["Avoid recomputing expensive data transformations (database queries, API calls) on every script rerun","Cache large DataFrames in memory to improve responsiveness","Persist ML model objects across reruns without retraining","Optimize dashboard performance by caching intermediate computation results"],"best_for":["Data scientists building dashboards with expensive computations","Teams with large datasets requiring fast iteration","Developers optimizing Streamlit app performance"],"limitations":["Cache is session-scoped; cleared when user closes browser or session expires, requiring recomputation on next visit","Argument serialization for cache keys can fail with non-serializable objects (e.g., database connections, file handles)","No distributed caching; each Streamlit instance maintains its own cache, leading to redundant computation in multi-instance deployments","Cache invalidation is argument-based only; changes to external state (database records, API responses) are not detected automatically"],"requires":["Python 3.7+","Streamlit library","Serializable function arguments (for cache key generation)"],"input_types":["Python functions with serializable arguments"],"output_types":["Cached function results (DataFrames, models, computed values)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-streamlit__cap_4","uri":"capability://data.processing.analysis.file.upload.and.download.handling.with.in.memory.processing","name":"file upload and download handling with in-memory processing","description":"Streamlit provides st.file_uploader and st.download_button widgets that handle file I/O without requiring explicit form submission or server-side file storage. File uploads are streamed into memory as file-like objects (BytesIO), allowing developers to process them directly in Python (e.g., read CSV into DataFrame, parse JSON). Downloads are generated on-demand by serializing Python objects (DataFrames, images, text) into bytes and triggering browser downloads. This is implemented through multipart form handling on the backend and blob generation on the frontend.","intents":["Allow users to upload CSV/JSON/image files for processing without backend file storage","Generate downloadable reports, processed datasets, or visualizations from app state","Build data transformation pipelines where users upload raw data and download processed results","Enable file-based workflows without requiring users to manage local files"],"best_for":["Data scientists building data transformation tools","Teams building lightweight ETL interfaces","Developers prototyping file processing workflows"],"limitations":["File uploads are stored in memory; large files (>500MB) can cause memory exhaustion or timeouts","No built-in progress indicators for large file uploads or downloads","Downloaded files are not persisted on server; each download regenerates the file, causing latency for large outputs","No support for resumable uploads or multipart downloads"],"requires":["Python 3.7+","Streamlit library","Sufficient server memory for in-memory file processing"],"input_types":["File uploads (CSV, JSON, images, text, binary)","Python objects (DataFrames, images, text)"],"output_types":["File-like objects (BytesIO) for processing","Downloaded files (CSV, JSON, images, text)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-streamlit__cap_5","uri":"capability://automation.workflow.multi.page.app.routing.with.sidebar.navigation","name":"multi-page app routing with sidebar navigation","description":"Streamlit (v1.18+) provides st.navigation and st.Page APIs for building multi-page applications where each page is a separate Python file. The framework automatically generates a sidebar navigation menu and routes user clicks to the corresponding page file, executing that file's script in a new session context. Pages share a global session state object, allowing data to flow between pages. This is implemented through a page registry that maps page names to file paths and a routing layer that executes the appropriate page script on navigation.","intents":["Build multi-section dashboards with separate pages for different analyses","Create wizard-style workflows where users progress through sequential pages","Organize large apps into logical sections without monolithic scripts","Share state and data across multiple pages in a single app"],"best_for":["Teams building complex dashboards with multiple sections","Developers creating multi-step workflows or wizards","Data science teams organizing large analytical applications"],"limitations":["Page state is not automatically persisted across page navigation; developers must manually manage shared state","No built-in URL routing; page navigation doesn't update browser URL, limiting bookmarking and sharing","Each page navigation triggers a full script rerun, potentially causing performance issues with expensive computations","Limited control over page layout and sidebar appearance; styling is constrained to Streamlit's design system"],"requires":["Python 3.7+","Streamlit 1.18+","Multiple Python files organized in a pages/ directory"],"input_types":["Python page files (scripts)"],"output_types":["Multi-page web UI with sidebar navigation"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-streamlit__cap_6","uri":"capability://automation.workflow.real.time.data.streaming.with.st.write.and.container.updates","name":"real-time data streaming with st.write and container updates","description":"Streamlit provides mechanisms for updating UI elements in-place without full script reruns through container objects (st.container, st.columns, st.expander) and the st.write function, which intelligently renders different data types. For streaming scenarios, developers can use st.empty() to create placeholder containers and update them with new content, or use st.session_state to track state across reruns. This enables pseudo-real-time updates where new data is appended to existing containers without clearing the entire UI, though true streaming requires polling or WebSocket integration via custom components.","intents":["Display live-updating metrics or status indicators","Stream log output or progress updates to users","Build real-time dashboards that refresh specific sections without full page reloads","Append new data to existing visualizations without clearing previous results"],"best_for":["Teams building monitoring dashboards with live metrics","Developers creating progress indicators for long-running tasks","Data scientists building exploratory tools with incremental results"],"limitations":["No true WebSocket support; streaming requires polling or custom components, adding latency","Full script reruns on every update can cause flickering or performance issues with large UIs","st.empty() placeholders are cleared on rerun; maintaining state requires manual session_state management","No built-in support for server-sent events (SSE) or WebSocket connections"],"requires":["Python 3.7+","Streamlit library","Custom components or polling for true real-time updates"],"input_types":["Python objects (DataFrames, metrics, text, images)"],"output_types":["Updated UI containers with new data"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-streamlit__cap_7","uri":"capability://tool.use.integration.custom.component.integration.via.streamlit.component.api","name":"custom component integration via streamlit component api","description":"Streamlit provides a Component API that allows developers to create custom React/HTML components and embed them in Streamlit apps through a Python wrapper. Components are packaged as npm modules and communicate with the Streamlit backend via a bidirectional message protocol, allowing custom components to send data back to Python and receive updates from Python. This enables extending Streamlit with custom visualizations, interactive widgets, or third-party libraries not natively supported. Components are registered in Python via st.components.v1.declare_component and rendered like built-in widgets.","intents":["Embed custom React components or third-party JavaScript libraries in Streamlit apps","Build specialized visualizations not available in built-in charting libraries","Create custom interactive widgets with complex behavior","Integrate with external web-based tools or services"],"best_for":["Teams with React/JavaScript expertise extending Streamlit","Developers building specialized visualizations","Organizations integrating third-party web tools into Streamlit apps"],"limitations":["Component development requires JavaScript/React knowledge, raising the barrier for Python-only developers","Bidirectional communication adds latency compared to native Streamlit widgets","Component state is not automatically persisted; developers must manage state manually","Debugging component issues requires familiarity with both Python and JavaScript toolchains"],"requires":["Python 3.7+","Streamlit library","Node.js and npm for component development","React or vanilla JavaScript knowledge"],"input_types":["React components or HTML/JavaScript code"],"output_types":["Custom interactive widgets with bidirectional communication"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-streamlit__cap_8","uri":"capability://automation.workflow.deployment.and.sharing.via.streamlit.cloud.with.github.integration","name":"deployment and sharing via streamlit cloud with github integration","description":"Streamlit Cloud is a managed hosting platform that deploys Streamlit apps directly from GitHub repositories with zero configuration. Developers push code to GitHub, and Streamlit Cloud automatically detects the repository, installs dependencies from requirements.txt, and deploys the app with a public URL. The platform handles scaling, SSL certificates, and app lifecycle management. Apps are deployed as stateless instances that execute the full script on every user interaction, with session state isolated per user. This is implemented through GitHub OAuth integration, container orchestration, and a reverse proxy that routes requests to app instances.","intents":["Deploy Streamlit apps to production without managing servers or containers","Share interactive dashboards with stakeholders via public URLs","Automate deployment from GitHub without CI/CD configuration","Scale apps to handle multiple concurrent users"],"best_for":["Solo developers and small teams sharing internal tools","Data scientists deploying dashboards for stakeholder consumption","Teams seeking managed hosting without DevOps overhead"],"limitations":["Free tier has resource limits (1GB RAM, limited concurrent users); production workloads require paid plans","No built-in database or persistent storage; apps must use external services for data persistence","Cold start latency (~5-10 seconds) on free tier due to container initialization","Limited customization of deployment environment; no direct SSH access or custom runtime configuration"],"requires":["GitHub account and repository","Streamlit app code in repository root or pages/ directory","requirements.txt with dependencies","Streamlit Cloud account (free or paid)"],"input_types":["GitHub repository with Streamlit code"],"output_types":["Deployed web app with public URL"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-streamlit__cap_9","uri":"capability://safety.moderation.secrets.management.via.environment.variables.and.streamlit.secrets.toml","name":"secrets management via environment variables and streamlit secrets.toml","description":"Streamlit provides a secrets management system that stores sensitive credentials (API keys, database passwords) in a .streamlit/secrets.toml file (local development) or Streamlit Cloud's secrets UI (production). Secrets are accessed in Python via st.secrets dictionary, which loads values from the TOML file or environment variables. This prevents hardcoding credentials in source code and enables safe sharing of apps without exposing secrets. The implementation uses a TOML parser to load secrets on app startup and makes them available as a dictionary-like object.","intents":["Store API keys and database credentials without hardcoding them in source","Access secrets in Python code via st.secrets dictionary","Manage different secrets for development and production environments","Share Streamlit apps publicly without exposing sensitive credentials"],"best_for":["Teams deploying apps with external API dependencies","Developers managing multiple environments (dev, staging, prod)","Organizations with security requirements for credential management"],"limitations":["Secrets are loaded into memory on app startup; no runtime secret rotation","No audit logging of secret access; difficult to track which code accessed which secrets","TOML format is simple; no support for complex secret structures or versioning","Local .streamlit/secrets.toml must be manually created and managed; easy to accidentally commit to Git"],"requires":["Python 3.7+","Streamlit library",".streamlit/secrets.toml file (local) or Streamlit Cloud secrets UI (production)"],"input_types":["TOML configuration file or environment variables"],"output_types":["Dictionary-like access to secrets via st.secrets"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","pip or conda package manager","No external web server required for local development","Streamlit library imported in script","Streamlit library","pandas or polars DataFrame","matplotlib, plotly, or altair library installed","Serializable function arguments (for cache key generation)","Sufficient server memory for in-memory file processing","Streamlit 1.18+"],"failure_modes":["Full script reruns on every interaction can cause performance degradation with large datasets (>100MB in memory)","No built-in multi-page routing until v1.18; requires workarounds for complex navigation","Session state persists only in browser memory; no native database persistence layer","Difficult to implement complex stateful workflows requiring fine-grained control over component lifecycle","Widget state is session-scoped; no cross-session persistence without external storage","Complex interdependent widget logic can become difficult to reason about due to full-script reruns","No native support for debouncing or throttling widget updates, leading to excessive reruns on rapid input changes","Limited customization of widget appearance; styling is constrained to Streamlit's design system","Sorting and filtering are client-side only; large DataFrames (>100k rows) can cause browser lag","No built-in support for custom cell rendering or conditional formatting","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"ecosystem":0.3,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:24.098Z","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=pypi-streamlit","compare_url":"https://unfragile.ai/compare?artifact=pypi-streamlit"}},"signature":"BmCgAJ7qQsMsN0rVQfr4aYTkAIhJ3LeedRU2AKME2nIqAwkzEMcCybURjPcsz9qeK3qQTG8fAvPN+iUJxvuIDQ==","signedAt":"2026-06-21T13:11:49.241Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-streamlit","artifact":"https://unfragile.ai/pypi-streamlit","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-streamlit","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"}}