Feat/dashboard clean monitor upload 1334918525438687015 (#40)

* feat: Clean up dashboard UI, shift workflow WS to SSE, and add file upload support

- Removed Monitoring view and associated `/ws/state` cluster websocket route.
- Modified workflow tracing from WebSocket (`/api/v1/workflow/ws/{trace_id}`) to Server-Sent Events (`/api/v1/workflow/sse/{trace_id}`) for unidirectional pushes, introducing a new `/api/v1/workflow/reply/{trace_id}` POST route to handle incoming client replies.
- Cleaned up dummy data and unneeded links in the chat layout (LeftPanel, ChatPanel).
- Implemented file upload functionality: added a `/api/v1/adapter/client/upload` endpoint to the backend which saves files to a local `uploads` directory, and added an integrated file input triggered via the `+` button in the frontend chat interface to facilitate uploading with an automated chat message confirmation.

Co-authored-by: zhaoxi826 <198742034+zhaoxi826@users.noreply.github.com>

* fix: prevent global_state_machine actor from being garbage collected

Added `lifetime="detached"` and kept a local reference to the `GlobalStateMachine`
actor in `main.py` so that it doesn't get cleaned up by Ray due to going out
of scope, which was causing `ray.get_actor('global_state_machine')` calls to fail
in API route handlers (resulting in 500 errors).

Co-authored-by: zhaoxi826 <198742034+zhaoxi826@users.noreply.github.com>

* fix: resolve named actor addressing failure across Ray processes via explicit namespace

The `ray.get_actor` calls in API routes executing within a Ray Serve worker were failing to
resolve the actors created by the main process because the implicit random namespace of
`ray.init()` did not match the namespace of the Ray Serve application scope.

Instead of overriding garbage collection via `lifetime="detached"` (which can lead to actor
leakage), this assigns an explicit `namespace="pretor"` when initializing Ray in the main process,
and uses the identical namespace in `ray_hook.py` when looking up named actors. Also retains the
local variable assignments in `main.py` to prevent them from being eliminated as unused variables.

Co-authored-by: zhaoxi826 <198742034+zhaoxi826@users.noreply.github.com>

* fix: defer actor lookup in WorkflowRunningEngine to avoid startup race conditions

The `WorkflowRunningEngine` was trying to fetch the `global_state_machine` actor
during its `__init__` method via `ray_actor_hook()`. Since actor creation requests are
dispatched asynchronously, the `global_state_machine` might not be completely
registered and discoverable via `ray.get_actor()` by the time the `WorkflowRunningEngine`'s
`__init__` is evaluated.

Moved the actor lookup to the async `run()` method, which gets executed after the engine
itself is fully up, allowing time for other components to become available in the global
Ray namespace.

Co-authored-by: zhaoxi826 <198742034+zhaoxi826@users.noreply.github.com>

---------

Co-authored-by: google-labs-jules[bot] <161369871+google-labs-jules[bot]@users.noreply.github.com>
Co-authored-by: zhaoxi826 <198742034+zhaoxi826@users.noreply.github.com>

pretor/core/workflow/workflow_runner.py

@@ -268,9 +268,11 @@ class WorkflowRunningEngine:
         self.consciousness_node = consciousness_node
         self.control_node = control_node
         self.supervisory_node = supervisory_node
-        self.global_state_machine = ray_actor_hook("global_state_machine").global_state_machine
+        self.global_state_machine = None
 
     async def run(self):
+        # Move actor hook to async start so we don't race during __init__ across cluster
+        self.global_state_machine = ray_actor_hook("global_state_machine").global_state_machine
         self.workflow_queue = asyncio.Queue()
         self.runner_engine = {
             f"runner_{i}": asyncio.create_task(self.runner(i))