Create app.py

app.py

@@ -0,0 +1,91 @@
+from daggr import FnNode, GradioNode, InferenceNode, Graph
+from daggr.state import get_daggr_files_dir
+
+import gradio as gr
+import numpy as np
+from PIL import Image
+from typing import Any
+import uuid
+
+def downscale_image_to_file(image: Any, scale: float = 0.25) -> str | None:
+
+   pil_img = Image.open(image)
+   scale_f = max(0.05, min(1.0, float(scale)))
+   w, h = pil_img.size
+   new_w = max(1, int(w * scale_f))
+   new_h = max(1, int(h * scale_f))
+   resized = pil_img.resize((new_w, new_h), resample=Image.LANCZOS)
+   out_path = get_daggr_files_dir() / f"{uuid.uuid4()}.png"
+   resized.save(out_path)
+   return str(out_path)
+
+background_remover = GradioNode(
+   "merve/background-removal",
+   api_name="/image",
+   run_locally=True, 
+   inputs={
+       "image": gr.Image(),
+   },
+   outputs={
+       "original_image": None,
+       "final_image": gr.Image(
+           label="Final Image"
+       ),
+   },
+)
+
+downscaler = FnNode(
+   downscale_image_to_file,
+   name="Downscale image for Inference",
+   inputs={
+       "image": background_remover.final_image,
+       "scale": gr.Slider(
+           label="Downscale factor",
+           minimum=0.25,
+           maximum=0.75,
+           step=0.05,
+           value=0.25,
+       ),
+   },
+   outputs={
+       "image": gr.Image(label="Downscaled Image", type="filepath"),
+   },
+)
+
+flux_enhancer = InferenceNode(
+   model="black-forest-labs/FLUX.2-klein-4B:fal-ai",
+   inputs={
+       "image": downscaler.image,
+       "prompt": gr.Textbox(
+           label="prompt",
+           value=("Transform this into a clean 3D asset render"),
+           lines=3,
+       ),
+   },
+   outputs={
+       "image": gr.Image(label="3D-Ready Enhanced Image"),
+   },
+)
+
+
+trellis_3d = GradioNode(
+   "microsoft/TRELLIS.2",
+   api_name="/image_to_3d",
+   inputs={
+       "image": flux_enhancer.image,
+       "ss_guidance_strength": 7.5,   
+       "ss_sampling_steps": 12,     
+   },
+   outputs={
+       "glb": gr.HTML(label="3D Asset (GLB preview)"),
+   },
+)
+
+graph = Graph(
+   name="Image to 3D Asset Pipeline",
+   nodes=[background_remover, downscaler, flux_enhancer, trellis_3d],
+)
+
+
+if __name__ == "__main__":
+   graph.launch()