README.md

metadata

title: Agentic Space Factory
sdk: gradio
app_file: app.py
hf_oauth: true
hf_oauth_expiration_minutes: 480
hf_oauth_scopes:
  - read-repos
  - write-repos
  - manage-repos
  - gated-repos
  - inference-api
  - jobs
  - read-billing

Agentic Space Factory

Agentic Space Factory turns a Hugging Face model card into a private, testable Gradio Space using a Hugging Face Job, Pi, a coding model through Hugging Face Inference Providers, and a private Storage Bucket for run state, traces, reports, generated files, and validation artifacts.

It is designed as a Hugging Face-native version of the local agent workflow described in the ZeroGPU Spaces article:

model card → agentic job → generated private Space → hardware if available → live validation → report

Main workflow

1. Build from model card

Paste a Hugging Face model ID or model-card URL. The app launches an HF Job that:

• fetches model metadata and repo information;
• asks Pi + Qwen Coder to build a Gradio app;
• creates a private Space under your namespace;
• uploads the generated workspace;
• attempts ZeroGPU first, then fixed-GPU fallback if enabled;
• validates /health or Gradio API availability;
• classifies the result honestly;
• returns quick links to the Job page, generated Space, Space settings, and run artifacts.

2. Validate existing Space

If automatic hardware assignment fails, set the hardware manually in the generated Space settings, then run validation. The validation job:

• waits for the existing Space;
• calls the configured generation endpoint;
• checks the returned output type;
• stores returned artifacts in the Bucket;
• measures latency;
• recommends a conservative ZeroGPU duration;
• returns quick links to the validation Job, target Space, Space settings, and run artifacts.

Result statuses

• full_inference_success: a live generation smoke test returned the expected output type.
• manual_hardware_required: the Space was generated but automatic ZeroGPU/fixed-GPU assignment failed; set hardware manually, then validate.
• full_inference_candidate_health_passed: the Space boots and contains inference signals, but generation was not smoke-tested yet.
• health_only: the Space boots, but no real inference path was validated.
• technical_blocker: the agent found concrete blockers such as multi-GPU requirements, missing licenses, custom CUDA, or unclear usage.
• failed: the build, runtime, or validation job failed.

What this app guarantees

• Generated Spaces are private by default.
• Nothing is published automatically.
• Runs are persisted in the configured Storage Bucket.
• The app distinguishes bootability from real inference.
• Hardware assignment is treated as best effort, not as an assumed capability.

What this app does not guarantee

It does not guarantee that every model card becomes a working Space. Some models require multi-GPU execution, Docker, unavailable CUDA extensions, external services, accepted licenses, very large hardware, or manual fixes.

Required environment

Each signed-in user writes to their own private Storage Bucket by default. The app can now check and create this private bucket for the user from the Run storage section before launching Jobs. The default bucket is:

https://huggingface.co/buckets/<your-username>/space-factory-runs

For local dev or deployment, configure only the bucket name; the namespace is derived from the signed-in OAuth user:

SPACE_FACTORY_BUCKET_NAME=space-factory-runs
SPACE_FACTORY_BUCKET_MOUNT=/output

Do not configure a shared owner/name bucket for a public Space. The app uses HF OAuth; no global admin token should be stored in the Space.