Enhance error handling in pipeline execution and remove outdated API …

…request script

src/main.py

@@ -6,7 +6,7 @@
 from src.ui.ui import layout, header
 from src.ui.tabs.configure import update_state, update_nodes, nodes_flow
 from src.ui.tabs.presets import load_json
-from src.ui.tabs.run import run_btn_clicked, progress, circle_progress
+from src.ui.tabs.run import run_btn_clicked, error_notification, circle_progress
 
 from src.ui.dtl.Layer import Layer
 from src.ui.dtl.actions.input.images_project.images_project import ImagesProjectAction
@@ -205,6 +205,9 @@ def run_pipeline_from_api(response: Response, request: Request):
         run_btn_clicked()
         return {"result": "pipeline was successfully processed"}
     except Exception as e:
+        error_notification.set("Error", description=str(e))
+        error_notification.show()
+        circle_progress.set_status("exception")
         raise e
 
 

src/scripts/auto_start.py

@@ -13,45 +13,35 @@
 
 api: sly.Api = sly.Api.from_env()
 
-agent_id = 452  # 359
+agent_id = 555  # <- replace with your agent_id
 app_slug = "supervisely-ecosystem/data-nodes"
 
-# preset_path = "/data-nodes/presets/images/preset.json"
-preset_path = "/data-nodes/presets/images/api_pipeline.json"
+preset_path = "/data-nodes/presets/images/preset.json"  # <- replace with your preset path
 
 module_id = api.app.get_ecosystem_module_id(app_slug)
 module_info = api.app.get_ecosystem_module_info(module_id)
 
 
 params = {"modalityType": "images", "slyFile": preset_path}
 
-# app_params = {
-#     "agent_id": agent_id,
-#     # "app_id": 0,
-#     "module_id": module_id,
-#     "workspace_id": workspace_id,
-#     "description": "Start ML Pipelines from py",
-#     "task_name": "ML Pipelines",
-#     "params": {"autostart": False, **params},
-#     "app_version": None,
-#     "is_branch": False,
-# }
-
 app_params = {
     "agent_id": agent_id,
-    # "app_id": 0,
     "module_id": module_id,
     "workspace_id": workspace_id,
-    "description": "Start ML Pipelines from API",
+    "description": "Start ML Pipelines from py",
     "task_name": "ML Pipelines",
     "params": {"autostart": False, **params},
-    "app_version": "run-from-api",
-    "is_branch": True,
+    "app_version": None,
+    "is_branch": False,
 }
 
 session_info = api.app.start(**app_params)
 
-# Run pipeline
-is_ready = api.app.wait_until_ready_for_api_calls(session_info.task_id)
-if is_ready:
-    api.task.send_request(task_id=session_info.task_id, method="run_pipeline", data={})
+# Uncomment to run pipeline after starting
+# is_ready = api.app.wait_until_ready_for_api_calls(session_info.task_id)
+# if is_ready:
+#     api.task.send_request(task_id=session_info.task_id, method="run_pipeline", data={})
+
+# Use to check pipeline progress
+# status = api.task.send_request(task_id=task_id, method="get_pipeline_status", data={})
+# print(status)

src/scripts/api_request.py → src/scripts/example_api_request.py

@@ -1,7 +1,7 @@
 # Example on how to send API requests to ML Pipelines app from a script
 # Available endpoints:
-# 1. run_pipeline - runs a pipeline with currently selected layers on the scene
-# 2. get_pipeline_status - returns the status of the pipeline (e.g "Pipeline status is 21/50")
+# 1. run_pipeline - runs a pipeline with current layers on the canvas
+# 2. get_pipeline_status - returns the status of the pipeline ("Pipeline status is 21/50")
 
 import os
 
@@ -16,13 +16,12 @@
 
 api: sly.Api = sly.Api.from_env()
 
-task_id = 68139
+task_id = 55555  # <- replace with your task_id
 
-is_ready = api.app.is_ready_for_api_calls(task_id)
-
-# is_ready = api.app.wait_until_ready_for_api_calls(task_id)
-# if is_ready:
-api.task.send_request(task_id=task_id, method="run_pipeline", data={})
+is_ready = api.app.wait_until_ready_for_api_calls(task_id)
+if is_ready:
+    api.task.send_request(task_id=task_id, method="run_pipeline", data={})
 
+# Use to check pipeline progress
 # status = api.task.send_request(task_id=task_id, method="get_pipeline_status", data={})
 # print(status)

src/ui/tabs/run.py

@@ -319,6 +319,7 @@ def run_pipeline(run_dialog: Dialog = None):
 
 
 def start_pipeline(run_dialog: Dialog = None):
+    g.current = 0
     g.total = 0
     if g.pipeline_thread is not None or g.pipeline_running is True:
         error_notification.set(