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a working demo is a single function call.","Interface API is unique; migration to Streamlit or Dash requires rewriting the UI layer.","Simple Interface API works well; advanced layouts and custom components require more effort.","Fast for inference demos; built-in queuing handles concurrent requests efficiently.","Very widely used in the ML and HuggingFace community for model demos and Spaces.",[392,411,422,447],{"stack_id":393,"slug":394,"name":395,"tagline":396,"experience_level":397,"project_type":398,"stack_type_slug":399,"stack_type_icon_url":400,"score_popularity":35,"score_learning_curve":39,"catalog_display_order":32,"published_date":32,"last_updated_date":32,"core_tool_previews":401},95,"pytorch-ml-training","PyTorch ML Training","PyTorch deep learning training with scikit-learn baselines, Pandas, and Jupyter for research and experimentation.","intermediate","ml_project","project","https:\u002F\u002Fassets.tekyous.dev\u002Ficons\u002Fstack-types\u002Fproject.svg",[402,403,404,405,406],{"tool_id":35,"slug":201,"name":200,"logo_url":203,"logo_bg":204},{"tool_id":276,"slug":278,"name":277,"logo_url":280,"logo_bg":204},{"tool_id":307,"slug":308,"name":308,"logo_url":310,"logo_bg":204},{"tool_id":330,"slug":332,"name":331,"logo_url":334,"logo_bg":100},{"tool_id":407,"slug":408,"name":409,"logo_url":410,"logo_bg":204},114,"jupyter-notebook","Jupyter Notebook","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fjupyter-notebook.svg",{"stack_id":412,"slug":413,"name":414,"tagline":415,"experience_level":416,"project_type":398,"stack_type_slug":399,"stack_type_icon_url":400,"score_popularity":105,"score_learning_curve":37,"catalog_display_order":32,"published_date":32,"last_updated_date":32,"core_tool_previews":417},94,"ml-exploration-starter","ML Exploration Starter","scikit-learn and Pandas in Jupyter for hands-on classical machine learning exploration.","beginner",[418,419,420,421],{"tool_id":35,"slug":201,"name":200,"logo_url":203,"logo_bg":204},{"tool_id":307,"slug":308,"name":308,"logo_url":310,"logo_bg":204},{"tool_id":330,"slug":332,"name":331,"logo_url":334,"logo_bg":100},{"tool_id":407,"slug":408,"name":409,"logo_url":410,"logo_bg":204},{"stack_id":423,"slug":424,"name":425,"tagline":426,"experience_level":427,"project_type":398,"stack_type_slug":399,"stack_type_icon_url":400,"score_popularity":39,"score_learning_curve":105,"catalog_display_order":32,"published_date":32,"last_updated_date":32,"core_tool_previews":428},9,"mlops-pipeline","MLOps Pipeline","End-to-end ML pipelines from training to production monitoring.","advanced",[429,433,434,439,443],{"tool_id":284,"slug":430,"name":431,"logo_url":432,"logo_bg":204},"fastapi","FastAPI","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Ffastapi.svg",{"tool_id":35,"slug":201,"name":200,"logo_url":203,"logo_bg":204},{"tool_id":435,"slug":436,"name":437,"logo_url":438,"logo_bg":204},57,"snowflake","Snowflake","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fsnowflake.svg",{"tool_id":423,"slug":440,"name":441,"logo_url":442,"logo_bg":204},"apache-airflow","Apache Airflow","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fapache-airflow.svg",{"tool_id":444,"slug":445,"name":445,"logo_url":446,"logo_bg":204},74,"dbt","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fdbt.png",{"stack_id":35,"slug":448,"name":449,"tagline":450,"experience_level":416,"project_type":451,"stack_type_slug":399,"stack_type_icon_url":400,"score_popularity":105,"score_learning_curve":208,"catalog_display_order":32,"published_date":32,"last_updated_date":32,"core_tool_previews":452},"python-dashboard-starter","Python Dashboard Starter","Interactive Streamlit dashboard with pandas analytics and a PostgreSQL backend.","dashboard",[453,454,458,459],{"tool_id":35,"slug":201,"name":200,"logo_url":203,"logo_bg":204},{"tool_id":131,"slug":455,"name":456,"logo_url":457,"logo_bg":204},"postgresql","PostgreSQL","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fpostgresql.svg",{"tool_id":330,"slug":332,"name":331,"logo_url":334,"logo_bg":100},{"tool_id":105,"slug":460,"name":461,"logo_url":462,"logo_bg":204},"streamlit","Streamlit","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fstreamlit.svg",[464,467,470,473,476],{"question":465,"answer":466},"Does the Gradio app need a backend server?","No. Gradio's Python function runs directly behind the generated interface, so there's no separate API or database layer to build unless the model itself needs one.",{"question":468,"answer":469},"Free Hugging Face Spaces or a paid GPU Space?","Free CPU Spaces are enough for lightweight models or ones already exported to a fast runtime. A paid GPU Space is worth it once inference on CPU takes more than a couple of seconds per request.",{"question":471,"answer":472},"Gradio or Streamlit for sharing a model?","Gradio when the app is the model: an input goes in, a prediction comes out, and the interface exists so people can try it. Its components are built for that loop (image and audio inputs, chat windows, labelled outputs), every app gets an API for free, and Hugging Face Spaces hosts it with no setup. Streamlit, which the Python Dashboard Starter stack is built on, suits apps where the data is the point: dashboards, filters, charts over a dataset, multi-page tools. A model demo with a few supporting charts fits either; an analytics tool with a model inside it is Streamlit's territory.",{"question":474,"answer":475},"Why does the demo get slow when several people use it at once?","Because Gradio queues requests and, by default, runs each event handler for one request at a time, so a model that takes three seconds per prediction makes the tenth visitor wait about half a minute. If the model can safely handle parallel calls, raise the event's concurrency limit. If it runs on a GPU, batching is often the better fix: mark the function as batched with a maximum batch size, and Gradio groups waiting requests into one model call. On free CPU hardware neither change creates more compute, so a popular demo eventually needs faster hardware.",{"question":477,"answer":478},"Can I restrict who uses the demo?","Yes, in a few ways depending on who the audience is. A private Space is visible only to you or your organization on Hugging Face, which suits internal model reviews. Gradio's launch settings accept a username and password for simple shared access when hosting it yourself, and Spaces support signing in with Hugging Face accounts when you need to know who each user is. Keep in mind that the auto-generated API follows the same access rules as the interface, so a public Space means a public API too.",{"summary":480,"starting_cost_label":481,"has_free_tier":3,"line_items":482},"Gradio, PyTorch, scikit-learn, and Pandas are all free, open-source Python libraries with no usage costs. The only real cost is hosting the demo: Hugging Face Spaces' free CPU tier covers most lightweight demos, with paid GPU-backed Spaces available once a model needs more compute to run inference at a reasonable speed.","Free to start",[483,487],{"label":484,"cost":485,"note":486},"Gradio, PyTorch, scikit-learn, Pandas","Free (open source)","No licensing or usage cost for any of the core Python libraries.",{"label":488,"cost":489,"note":490},"Hugging Face Spaces hosting","Free-$9+\u002Fmo","The free CPU tier runs most demos; PRO ($9\u002Fmo) and paid GPU-backed Spaces are worth it once a model is too slow on CPU.",{"title":492,"description":493,"og_image":32,"canonical":494},"Gradio ML Showcase: Tools, Pricing & How to Deploy | Tekyous","Gradio Python interface for sharing ML models as interactive web demos instantly. Compare Gradio ML Showcase tools, pricing & how to deploy on Tekyous.","https:\u002F\u002Ftekyous.dev\u002Fstacks\u002Fgradio-ml-showcase",1790518845341]