"""
06 · Instruction Image Editor   (REAL AI — needs a Hugging Face token)   ·   one model node
===========================================================================================

Upload an image, type an edit instruction ("make it a snowy winter scene", "turn
the car red", "add sunglasses"), and get the edited photo back. The whole app is
a single `model` operator calling `Qwen/Qwen-Image-Edit` on
Hugging Face Inference Providers — no client code, no API wiring.

Graph:
    [Image]            ─┐
                        ├─▶ (model) Qwen/Qwen-Image-Edit (image_to_image) ─▶ 🖼️ Edited image
    [Edit instruction] ─┘

Why a plain `model` node works here: the `image_to_image` endpoint schema is
exactly {image, prompt} → image, so the canvas keeps the ports as authored.

SETUP:
  export HF_TOKEN=hf_xxxxx        # or: hf auth login   (Windows: setx HF_TOKEN ...)
  python apps/06_image_editor/app.py
On the hosted Space, click "Sign in with Hugging Face" first so the edit runs
under your own token.
"""

import os

import gradio as gr

WORKFLOW = os.path.join(os.path.dirname(os.path.abspath(__file__)), "workflow.json")
demo = gr.Workflow(WORKFLOW)   # no bind: the model node calls HF for you

if __name__ == "__main__":
    from huggingface_hub import get_token
    if not get_token() and not os.environ.get("HF_TOKEN"):
        print("\n  ⚠  No Hugging Face token found — the edit will fail until you set\n"
              "     HF_TOKEN (or `hf auth login`), or sign in inside the app.\n")
    demo.launch()