Python: DevUI fixes : Add multimodal input support for workflows and refactor chat input (#2593)

* show app version in devui .NET: Python: Improved Versioning for DevUI
Fixes #2059

* feat: Add multimodal input support for workflows and refactor chat input

This PR adds support for multimodal content (images, files) in workflow
inputs and refactors the chat input into a reusable component.

## Multimodal Workflow Support
- Add `isChatMessageSchema()` to detect ChatMessage input schemas
- Update `RunWorkflowButton` to use `ChatMessageInput` for ChatMessage workflows
- Wrap multimodal content in OpenAI message format for backend processing
- Add `_is_openai_multimodal_format()` to detect OpenAI ResponseInputParam
- Update `_parse_workflow_input()` to route multimodal input through
  existing `_convert_input_to_chat_message()` converter

## Reusable ChatMessageInput Component
- Extract chat input logic from agent-view into `ChatMessageInput` component
- Support file upload, drag & drop, paste handling, and attachments
- Add `useDragDrop` hook for parent-level drag handling with full-area
  drop zones
- Refactor agent-view to use the new shared component

## Other Improvements
- Add `isStreaming` prop to executor nodes for animation control
- Clean up unused imports and state variables in agent-view
- Add tests for multimodal workflow input handling

Fixes workflow input not receiving images when using AgentExecutor nodes.

* add self loop edge, fix #2470

* fix test

python/packages/devui/tests/test_conversations.py

@@ -119,13 +119,13 @@ async def test_list_conversations_by_metadata():
     conv3 = store.create_conversation(metadata={"agent_id": "agent1", "session_id": "sess_1"})
 
     # Filter by agent_id
-    results = store.list_conversations_by_metadata({"agent_id": "agent1"})
+    results = await store.list_conversations_by_metadata({"agent_id": "agent1"})
 
     assert len(results) == 2
     assert all(cast(dict[str, str], c.metadata).get("agent_id") == "agent1" for c in results if c.metadata)
 
     # Filter by agent_id and session_id
-    results = store.list_conversations_by_metadata({"agent_id": "agent1", "session_id": "sess_1"})
+    results = await store.list_conversations_by_metadata({"agent_id": "agent1", "session_id": "sess_1"})
 
     assert len(results) == 1
     assert results[0].id == conv3.id