Wire up real model inference in app.py

app.py

@@ -1,14 +1,46 @@
+import os
+import uuid
+import subprocess
+import shlex
 import gradio as gr
 
-def generate_motion(prompt):
-    return f"This will generate motion for: '{prompt}'"
+MODEL_DIR = "motion-diffusion-model"   # where you copied the repo
+OUTPUT_DIR = "outputs"
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+def generate_motion(prompt: str):
+    # Unique filename
+    run_id = uuid.uuid4().hex[:8]
+    out_file = f"{run_id}.mp4"
+    out_path = os.path.join(OUTPUT_DIR, out_file)
+
+    # Command-line call to the sample script
+    # Adjust flags if needed; this is the typical pattern
+    cmd = f"python {MODEL_DIR}/sample/predict.py " \
+          f"--prompt \"{prompt}\" " \
+          f"--results_dir {OUTPUT_DIR} " \
+          f"--num_samples 1"
+
+    # Run the model
+    subprocess.run(shlex.split(cmd), check=True)
+
+    # The script will dump something like outputs/sample_0.mp4
+    # Rename it to our unique filename
+    default_out = os.path.join(OUTPUT_DIR, "sample_0.mp4")
+    if os.path.exists(default_out):
+        os.replace(default_out, out_path)
+    else:
+        raise FileNotFoundError(default_out)
+
+    return out_path
 
 demo = gr.Interface(
     fn=generate_motion,
     inputs=gr.Textbox(label="Enter a text prompt"),
-    outputs="text",
-    title="Motion Diffusion Model Demo",
-    description="Generate 3D human motion from text."
+    outputs=gr.Video(label="Generated Motion"),
+    title="Motion Diffusion Model",
+    description="Type some text and watch the 3D human motion!"
 )
 
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -1 +1,9 @@
 gradio
+torch
+transformers
+omegaconf
+einops
+scipy
+numpy
+opencv-python
+imageio