add orchestrated version of agent in omnitool

omnitool/gradio/loop.py

@@ -18,6 +18,7 @@ from tools import ToolResult
 from agent.llm_utils.omniparserclient import OmniParserClient
 from agent.anthropic_agent import AnthropicActor
 from agent.vlm_agent import VLMAgent
+from agent.vlm_agent_with_orchestrator import VLMOrchestratedAgent
 from executor.anthropic_executor import AnthropicExecutor
 
 BETA_FLAG = "computer-use-2024-10-22"
@@ -74,6 +75,16 @@ def sampling_loop_sync(
             max_tokens=max_tokens,
             only_n_most_recent_images=only_n_most_recent_images
         )
+    elif model in set(["omniparser + gpt-4o-orchestrated", "omniparser + o1-orchestrated", "omniparser + o3-mini-orchestrated", "omniparser + R1-orchestrated", "omniparser + qwen2.5vl-orchestrated"]):
+        actor = VLMOrchestratedAgent(
+            model=model,
+            provider=provider,
+            api_key=api_key,
+            api_response_callback=api_response_callback,
+            output_callback=output_callback,
+            max_tokens=max_tokens,
+            only_n_most_recent_images=only_n_most_recent_images
+        )
     else:
         raise ValueError(f"Model {model} not supported")
     executor = AnthropicExecutor(
@@ -102,7 +113,7 @@ def sampling_loop_sync(
 
             messages.append({"content": tool_result_content, "role": "user"})
     
-    elif model in set(["omniparser + gpt-4o", "omniparser + o1", "omniparser + o3-mini", "omniparser + R1", "omniparser + qwen2.5vl"]):
+    elif model in set(["omniparser + gpt-4o", "omniparser + o1", "omniparser + o3-mini", "omniparser + R1", "omniparser + qwen2.5vl", "omniparser + gpt-4o-orchestrated", "omniparser + o1-orchestrated", "omniparser + o3-mini-orchestrated", "omniparser + R1-orchestrated", "omniparser + qwen2.5vl-orchestrated"]):
         while True:
             parsed_screen = omniparser_client()
             tools_use_needed, vlm_response_json = actor(messages=messages, parsed_screen=parsed_screen)