wip

app.py

@@ -7,7 +7,42 @@ import subprocess
 from threading import Timer
 from functools import partial
 import time
-from huggingface_hub import InferenceClient
+
+MODEL = "Qwen/Qwen2.5-Coder-32B-Instruct"
+
+if os.environ.get("HF_TOKEN"):
+    from huggingface_hub import InferenceClient
+
+    client = InferenceClient(
+        provider="hf-inference",
+        api_key=os.environ["HF_TOKEN"],
+    )
+
+    def generate(promt, history, code):
+        print(promt, history, code)
+        completion = client.chat.completions.create(
+            model=MODEL,
+            messages=[
+                {
+                    "role": "user",
+                    "content": promt
+                }
+            ],
+        )
+        return completion.choices[0].message
+else:
+    # we try to run on a ZERO GPU space
+    import spaces
+    from diffusers import DiffusionPipeline
+
+    pipe = DiffusionPipeline.from_pretrained(MODEL)
+    pipe.to('cuda')
+
+    @spaces.GPU
+    def generate(promt, history, code):
+        pass
+
+
 
 from gradio_motioncanvasplayer import MotionCanvasPlayer
 
@@ -15,41 +50,31 @@ from gradio_motioncanvasplayer import MotionCanvasPlayer
 example_project_path = "https://prathje-gradio-motioncanvasplayer.hf.space/gradio_api/file=/home/user/app/public/project-3.17.2.js"
 
 
-client = InferenceClient(
-    provider="hf-inference",
-    api_key=os.environ["HF_TOKEN"],
-)
-
-def get_completion(prompt, history):
-    completion = client.chat.completions.create(
-        model="Qwen/Qwen2.5-Coder-32B-Instruct",
-        messages=[
-            {
-                "role": "user",
-                "content": "What is the capital of France?"
-            }
-        ],
-    )
-    return completion.choices[0].message
-
 def load_example(example):
     return example['project_path'], example['code'], ""
 
 with gr.Blocks(theme=gr.themes.Monochrome()) as app:
     gr.Markdown("# Motion Canvas Agent")
     gr.Markdown("Leverage the power of AI and Motion Canvas to create animations using TypeScript.")
+
+    player = MotionCanvasPlayer(example_project_path, auto=True, quality=0.5, width=1920, height=1080, variables="{}", render=False)
+
+    code = gr.Code(value="", language="typescript", render=False)
+    logs = gr.Textbox(value="", label="Build Logs", interactive=False, render=False)
+
     with gr.Row():
         with gr.Column():
             gr.Markdown("## Chat")
-            chat = gr.ChatInterface(fn=get_completion, type="messages")
-        
+            chat = gr.ChatInterface(fn=generate, type="messages", additional_inputs=[code, logs], additional_outputs=[player, code, logs])
+
             gr.Markdown("### TypeScript Code for Your Scene")
-            code = gr.Code(value="", language="typescript")
-            logs = gr.Textbox(value="", label="Build Logs", interactive=False)
+            code.render()
+            logs.render()
 
         with gr.Column():
             gr.Markdown("## Preview")
-            player = MotionCanvasPlayer(example_project_path, auto=True, quality=0.5, width=1920, height=1080, variables="{}")
+            player.render()
+            
 
 if __name__ == "__main__":
     # Todo: In the future we could allow to use this as an MCP server, but right now, we need the preview to be available.

gradio_mcp_server.py

@@ -5,6 +5,8 @@ import io
 import time
 from gradio_client import Client
 
+
+
 sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8', errors='replace')
 sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding='utf-8', errors='replace')