feat: lightweight non-LFS Movimento app deployment

README.md

@@ -1,15 +1,29 @@
 ---
 title: Movimento
-emoji: 🏢
-colorFrom: yellow
-colorTo: yellow
+emoji: 🎬
+colorFrom: blue
+colorTo: green
 sdk: gradio
 sdk_version: 6.14.0
 python_version: '3.12'
 app_file: app.py
-pinned: false
-license: mit
-short_description: Agentic multi-character motion scripts
+pinned: true
+license: apache-2.0
+short_description: Text-driven multi-character motion planning workspace
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Movimento is a hackathon Space for multi-character motion planning and orchestration.
+
+This Space currently runs a lightweight planner UI while full model assets are prepared.
+
+Implemented pipeline milestones:
+- Card 0: environment readiness gate
+- Card 1: scope lock
+- Card 2: service contracts
+- Card 3: shared state deterministic loop
+- Card 4: Qwen planner adapter
+- Card 5: BONES-SEED ingestion flow
+- Card 6: script-to-Kimodo mapping
+
+Next milestone:
+- Card 7: blend quality guardrails with constraint-aware multi-character transition tuning

app.py

@@ -1,7 +1,45 @@
+import os
+
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+def generate_plan(prompt: str, characters: int, transition: str) -> str:
+    cleaned = (prompt or "").strip() or "Two characters wave and then walk together"
+    return (
+        "Movimento deployment is live on Hugging Face Spaces.\n\n"
+        "Requested scene:\n"
+        f"- Prompt: {cleaned}\n"
+        f"- Characters: {characters}\n"
+        f"- Transition preference: {transition}\n\n"
+        "Card 0-6 status:\n"
+        "- Qwen planning adapter implemented\n"
+        "- Deterministic loop scheduler implemented\n"
+        "- BONES-SEED ingestion flow implemented\n"
+        "- Script-to-Kimodo mapping implemented\n\n"
+        "Next step: Card 7 blend quality guardrails with constraint-aware multi-character transitions."
+    )
+
+
+with gr.Blocks(title="Movimento") as demo:
+    gr.Markdown("# Movimento")
+    gr.Markdown("Text-driven multi-character motion planning for the lablab.ai AMD hackathon.")
+
+    with gr.Row():
+        prompt = gr.Textbox(label="Scene Prompt", lines=3, placeholder="Two characters greet and sit down")
+
+    with gr.Row():
+        characters = gr.Slider(label="Characters", minimum=1, maximum=6, step=1, value=2)
+        transition = gr.Dropdown(
+            label="Transition Policy",
+            choices=["smooth", "overlap", "hold", "cut"],
+            value="smooth",
+        )
+
+    run = gr.Button("Generate Plan")
+    output = gr.Textbox(label="Planner Output", lines=16)
+
+    run.click(generate_plan, inputs=[prompt, characters, transition], outputs=output)
+
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", "7860")))