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README.md

@@ -5,7 +5,7 @@ emoji: 🔥
 colorFrom: indigo
 colorTo: indigo
 sdk: gradio
-sdk_version: 6.17.3
+sdk_version: 6.18.0
 app_file: run.py
 pinned: false
 hf_oauth: true

run.py

@@ -1,377 +1,55 @@
-"""Interactive walkthrough for gr.Workflow, built on gr.Walkthrough + gr.WorkflowCanvas.
-
-Drop into a Space to replace the bare Workflow demo at gradio/workflow with a
-guided tour. Each step ships its own pre-loaded canvas so users see the
-concept immediately and can play with it.
-"""
-
-import json
 import os
-import tempfile
+import random
 
 import gradio as gr
-from gradio.workflow import (
-    call_model,
-    call_space,
-    fetch_dataset,
-    get_dataset_schema,
-    get_token,
-    search_datasets,
-    search_models,
-    search_spaces,
-)
-
-SERVER_FUNCTIONS = [
-    get_token,
-    call_space,
-    call_model,
-    fetch_dataset,
-    search_spaces,
-    search_models,
-    search_datasets,
-    get_dataset_schema,
-]
-
-BASE = {"schema_version": "2", "runtime": {"default": "client"}, "view": {"default": "canvas"}}
-
-WELCOME = json.dumps(
-    {
-        **BASE,
-        "name": "Welcome",
-        "references": [
-            {
-                "id": "ref_text",
-                "role": "reference",
-                "asset_type": "text",
-                "label": "Your prompt",
-                "inputs": [{"id": "in", "label": "Text", "type": "text"}],
-                "outputs": [{"id": "out", "label": "Text", "type": "text"}],
-                "x": 80, "y": 120, "width": 240, "height": 120,
-                "data": {"in": "a corgi wearing sunglasses"},
-            }
-        ],
-        "operators": [],
-        "subjects": [
-            {
-                "id": "out_text",
-                "role": "subject",
-                "asset_type": "text",
-                "label": "Output",
-                "inputs": [{"id": "in", "label": "Text", "type": "text"}],
-                "outputs": [{"id": "out", "label": "Text", "type": "text"}],
-                "x": 420, "y": 120, "width": 240, "height": 120,
-                "data": {},
-            }
-        ],
-        "edges": [
-            {"id": "e1", "from_node_id": "ref_text", "from_port_id": "out",
-             "to_node_id": "out_text", "to_port_id": "in", "type": "text"}
-        ],
-    }
-)
-
-USE_SPACE = json.dumps(
-    {
-        **BASE,
-        "name": "Use a Space",
-        "references": [
-            {
-                "id": "ref_prompt",
-                "role": "reference", "asset_type": "text", "label": "Prompt",
-                "inputs": [{"id": "in", "label": "Text", "type": "text"}],
-                "outputs": [{"id": "out", "label": "Text", "type": "text"}],
-                "x": 80, "y": 120, "width": 240, "height": 120,
-                "data": {"in": "a corgi wearing sunglasses, studio lighting"},
-            }
-        ],
-        "operators": [
-            {
-                "id": "op_flux",
-                "role": "operator", "kind": "space",
-                "source": "hf://spaces/black-forest-labs/FLUX.1-schnell",
-                "space_id": "black-forest-labs/FLUX.1-schnell",
-                "label": "FLUX.1-schnell",
-                "inputs": [{"id": "in_0", "label": "Prompt", "type": "text", "required": True}],
-                "outputs": [{"id": "out_0", "label": "Image", "type": "image"}],
-                "x": 380, "y": 120, "width": 260, "height": 124,
-                "data": {},
-            }
-        ],
-        "subjects": [
-            {
-                "id": "out_image",
-                "role": "subject", "asset_type": "image", "label": "Result",
-                "inputs": [{"id": "in", "label": "Image", "type": "image"}],
-                "outputs": [{"id": "out", "label": "Image", "type": "image"}],
-                "x": 700, "y": 120, "width": 240, "height": 124,
-                "data": {},
-            }
-        ],
-        "edges": [
-            {"id": "e1", "from_node_id": "ref_prompt", "from_port_id": "out",
-             "to_node_id": "op_flux", "to_port_id": "in_0", "type": "text"},
-            {"id": "e2", "from_node_id": "op_flux", "from_port_id": "out_0",
-             "to_node_id": "out_image", "to_port_id": "in", "type": "image"},
-        ],
-    }
-)
-
-INFERENCE_PROVIDERS = json.dumps(
-    {
-        **BASE,
-        "name": "Inference Providers model",
-        "references": [
-            {
-                "id": "ref_prompt",
-                "role": "reference", "asset_type": "text", "label": "Prompt",
-                "inputs": [{"id": "in", "label": "Text", "type": "text"}],
-                "outputs": [{"id": "out", "label": "Text", "type": "text"}],
-                "x": 80, "y": 120, "width": 240, "height": 120,
-                "data": {"in": "a corgi wearing sunglasses, studio lighting"},
-            }
-        ],
-        "operators": [
-            {
-                "id": "op_flux_dev",
-                "role": "operator", "kind": "model", "source": "model",
-                "model_id": "black-forest-labs/FLUX.1-dev",
-                "pipeline_tag": "text-to-image",
-                "label": "FLUX.1-dev",
-                "inputs": [{"id": "prompt", "label": "Prompt", "type": "text", "required": True}],
-                "outputs": [{"id": "image", "label": "Image", "type": "image"}],
-                "x": 380, "y": 120, "width": 260, "height": 124,
-                "data": {},
-            }
-        ],
-        "subjects": [
-            {
-                "id": "out_image",
-                "role": "subject", "asset_type": "image", "label": "Result",
-                "inputs": [{"id": "in", "label": "Image", "type": "image"}],
-                "outputs": [{"id": "out", "label": "Image", "type": "image"}],
-                "x": 700, "y": 120, "width": 240, "height": 124,
-                "data": {},
-            }
-        ],
-        "edges": [
-            {"id": "e1", "from_node_id": "ref_prompt", "from_port_id": "out",
-             "to_node_id": "op_flux_dev", "to_port_id": "prompt", "type": "text"},
-            {"id": "e2", "from_node_id": "op_flux_dev", "from_port_id": "image",
-             "to_node_id": "out_image", "to_port_id": "in", "type": "image"},
-        ],
-    }
-)
-
-BRANCHING = json.dumps(
-    {
-        **BASE,
-        "name": "Branch into multiple outputs",
-        "references": [
-            {
-                "id": "ref_prompt",
-                "role": "reference", "asset_type": "text", "label": "Prompt",
-                "inputs": [{"id": "in", "label": "Text", "type": "text"}],
-                "outputs": [{"id": "out", "label": "Text", "type": "text"}],
-                "x": 80, "y": 200, "width": 240, "height": 120,
-                "data": {"in": "a tiny astronaut hatching from an egg on the moon"},
-            }
-        ],
-        "operators": [
-            {
-                "id": "op_flux_schnell",
-                "role": "operator", "kind": "model", "source": "model",
-                "model_id": "black-forest-labs/FLUX.1-schnell",
-                "pipeline_tag": "text-to-image",
-                "label": "FLUX.1-schnell",
-                "inputs": [{"id": "prompt", "label": "Prompt", "type": "text", "required": True}],
-                "outputs": [{"id": "image", "label": "Image", "type": "image"}],
-                "x": 380, "y": 80, "width": 260, "height": 124,
-                "data": {},
-            },
-            {
-                "id": "op_sd35",
-                "role": "operator", "kind": "model", "source": "model",
-                "model_id": "stabilityai/stable-diffusion-3.5-large-turbo",
-                "pipeline_tag": "text-to-image",
-                "label": "SD 3.5 Large Turbo",
-                "inputs": [{"id": "prompt", "label": "Prompt", "type": "text", "required": True}],
-                "outputs": [{"id": "image", "label": "Image", "type": "image"}],
-                "x": 380, "y": 320, "width": 260, "height": 124,
-                "data": {},
-            },
-        ],
-        "subjects": [
-            {
-                "id": "out_a",
-                "role": "subject", "asset_type": "image", "label": "FLUX result",
-                "inputs": [{"id": "in", "label": "Image", "type": "image"}],
-                "outputs": [{"id": "out", "label": "Image", "type": "image"}],
-                "x": 700, "y": 80, "width": 240, "height": 124,
-                "data": {},
-            },
-            {
-                "id": "out_b",
-                "role": "subject", "asset_type": "image", "label": "SD result",
-                "inputs": [{"id": "in", "label": "Image", "type": "image"}],
-                "outputs": [{"id": "out", "label": "Image", "type": "image"}],
-                "x": 700, "y": 320, "width": 240, "height": 124,
-                "data": {},
-            },
-        ],
-        "edges": [
-            {"id": "e1", "from_node_id": "ref_prompt", "from_port_id": "out",
-             "to_node_id": "op_flux_schnell", "to_port_id": "prompt", "type": "text"},
-            {"id": "e2", "from_node_id": "ref_prompt", "from_port_id": "out",
-             "to_node_id": "op_sd35", "to_port_id": "prompt", "type": "text"},
-            {"id": "e3", "from_node_id": "op_flux_schnell", "from_port_id": "image",
-             "to_node_id": "out_a", "to_port_id": "in", "type": "image"},
-            {"id": "e4", "from_node_id": "op_sd35", "from_port_id": "image",
-             "to_node_id": "out_b", "to_port_id": "in", "type": "image"},
-        ],
-    }
-)
-
-CHAIN = json.dumps(
-    {
-        **BASE,
-        "name": "Chain models",
-        "references": [
-            {
-                "id": "ref_image",
-                "role": "reference", "asset_type": "image", "label": "Source image",
-                "inputs": [{"id": "in", "label": "Image", "type": "image"}],
-                "outputs": [{"id": "out", "label": "Image", "type": "image"}],
-                "x": 80, "y": 120, "width": 240, "height": 124,
-                "data": {},
-            }
-        ],
-        "operators": [
-            {
-                "id": "op_caption",
-                "role": "operator", "kind": "space",
-                "source": "hf://spaces/vikhyatk/moondream2",
-                "space_id": "vikhyatk/moondream2",
-                "endpoint": "/answer_question",
-                "label": "Caption (Moondream)",
-                "inputs": [
-                    {"id": "in_0", "label": "Image", "type": "image", "required": True},
-                    {"id": "in_1", "label": "Question", "type": "text", "required": False,
-                     "default_value": "Describe this image briefly."},
-                ],
-                "outputs": [{"id": "out_0", "label": "Caption", "type": "text"}],
-                "x": 380, "y": 120, "width": 280, "height": 144,
-                "data": {},
-            },
-            {
-                "id": "op_gen",
-                "role": "operator", "kind": "model", "source": "model",
-                "model_id": "black-forest-labs/FLUX.1-schnell",
-                "pipeline_tag": "text-to-image",
-                "label": "FLUX.1-schnell",
-                "inputs": [{"id": "prompt", "label": "Prompt", "type": "text", "required": True}],
-                "outputs": [{"id": "image", "label": "Image", "type": "image"}],
-                "x": 720, "y": 120, "width": 260, "height": 124,
-                "data": {},
-            },
-        ],
-        "subjects": [
-            {
-                "id": "out_image",
-                "role": "subject", "asset_type": "image", "label": "Reimagined",
-                "inputs": [{"id": "in", "label": "Image", "type": "image"}],
-                "outputs": [{"id": "out", "label": "Image", "type": "image"}],
-                "x": 1040, "y": 120, "width": 240, "height": 124,
-                "data": {},
-            }
-        ],
-        "edges": [
-            {"id": "e1", "from_node_id": "ref_image", "from_port_id": "out",
-             "to_node_id": "op_caption", "to_port_id": "in_0", "type": "image"},
-            {"id": "e2", "from_node_id": "op_caption", "from_port_id": "out_0",
-             "to_node_id": "op_gen", "to_port_id": "prompt", "type": "text"},
-            {"id": "e3", "from_node_id": "op_gen", "from_port_id": "image",
-             "to_node_id": "out_image", "to_port_id": "in", "type": "image"},
-        ],
-    }
-)
-
-SHIP_IT_CODE = """\
-import gradio as gr
-
-# Build it once in the visual editor, export the JSON, then ship it as code:
-demo = gr.Workflow(value=open("my_workflow.json").read())
-demo.launch()
-"""
-
-
-with gr.Blocks(
-    title="Gradio Workflow walkthrough",
-    fill_height=True,
-    fill_width=True,
-) as demo:
-    if os.getenv("OAUTH_CLIENT_ID"):
-        gr.LoginButton(visible=False)
-
-    with gr.Walkthrough(selected=1):
-        with gr.Step("Welcome", id=1):
-            gr.Markdown(
-                "## What is a Workflow?\n"
-                "A **Workflow** is a visual pipeline of AI models. Each box is a "
-                "node — a piece of data or an operation — and edges between them "
-                "carry values. Click **Run** to execute end-to-end.\n\n"
-                "Use the steps above to see what you can do."
-            )
-            gr.WorkflowCanvas(value=WELCOME, server_functions=SERVER_FUNCTIONS)
-
-        with gr.Step("Use a Space", id=2):
-            gr.Markdown(
-                "## Drop in any Gradio Space\n"
-                "Workflow inspects a Space's API on the fly and exposes each "
-                "parameter as a port you can wire up. Here we route a prompt "
-                "into [FLUX.1-schnell](https://huggingface.co/spaces/black-forest-labs/FLUX.1-schnell)."
-            )
-            gr.WorkflowCanvas(value=USE_SPACE, server_functions=SERVER_FUNCTIONS)
-
-        with gr.Step("Inference Providers", id=3):
-            gr.Markdown(
-                "## Skip the GPU quota with Inference Providers\n"
-                "Pick a model directly from the Hub (instead of a Space) and "
-                "Workflow routes it through HF Inference Providers — generally "
-                "faster, no ZeroGPU quota, and load-balanced across providers."
-            )
-            gr.WorkflowCanvas(value=INFERENCE_PROVIDERS, server_functions=SERVER_FUNCTIONS)
-
-        with gr.Step("Branch", id=4):
-            gr.Markdown(
-                "## Fan out, compare, combine\n"
-                "An output port can feed multiple downstream nodes. Here a "
-                "single prompt drives two different image generators so you "
-                "can compare them side by side."
-            )
-            gr.WorkflowCanvas(value=BRANCHING, server_functions=SERVER_FUNCTIONS)
-
-        with gr.Step("Chain", id=5):
-            gr.Markdown(
-                "## Chain models together\n"
-                "The output of one model becomes the input to the next. This "
-                "pipeline captions an image with Moondream, then sends the "
-                "caption to FLUX to re-imagine it."
-            )
-            gr.WorkflowCanvas(value=CHAIN, server_functions=SERVER_FUNCTIONS)
 
-        with gr.Step("Ship it", id=6):
-            gr.Markdown(
-                "## From canvas to app\n"
-                "When you're happy, export the workflow JSON and drop it into "
-                "a `gr.Workflow(...)` in Python — same canvas, embeddable "
-                "anywhere a Gradio app runs."
-            )
-            gr.Code(value=SHIP_IT_CODE, language="python", interactive=False)
-            gr.Markdown(
-                "📚 [Workflow docs](https://gradio.app/docs/gradio/workflow)  ·  "
-                "💻 [Source on GitHub](https://github.com/gradio-app/gradio)"
-            )
 
+def describe_product(image) -> str:
+    if image is None:
+        return "a premium product"
+    img_path = image.get("path") if isinstance(image, dict) else str(image)
+    img_url = image.get("url", "") if isinstance(image, dict) else str(image)
+    try:
+        from gradio_client import Client, handle_file
+        source = img_path if (img_path and os.path.exists(img_path)) else img_url
+        if not source:
+            return "a premium product"
+        client = Client("vikhyatk/moondream2", verbose=False)
+        result = client.predict(
+            handle_file(source),
+            "Describe this product briefly and concisely. What is it?",
+            api_name="/answer_question",
+        )
+        return str(result).strip() if result else "a premium product"
+    except Exception:
+        return "a premium product"
+
+
+def craft_marketing_prompt(caption: str) -> str:
+    if not caption:
+        caption = "a premium product"
+    caption = caption.strip().rstrip(".")
+    styles = [
+        (
+            f"Professional product advertisement photograph of {caption}, "
+            "studio lighting, clean white background, commercial photography, "
+            "ultra-sharp, 8K quality"
+        ),
+        (
+            f"Cinematic product shot of {caption}, dramatic lighting, "
+            "aspirational lifestyle context, premium brand aesthetic, "
+            "shot on Hasselblad, magazine cover quality"
+        ),
+        (
+            f"Bold marketing campaign visual of {caption}, vibrant colors, "
+            "dynamic composition, modern editorial style, "
+            "award-winning commercial photography"
+        ),
+    ]
+    return random.choice(styles)
+
+
+demo = gr.Workflow(bind=[describe_product, craft_marketing_prompt])
 
 if __name__ == "__main__":
-    demo.launch(allowed_paths=[tempfile.gettempdir()])
+    demo.launch()