Refactor finetuning dashboard flow

.gitignore

@@ -1,3 +1,8 @@
+my_yamls/
+browser/
+.codex
+.vite/
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
@@ -162,4 +167,13 @@ cython_debug/
 #.idea/
 
 # Misc
-.vscode/
+.vscode/
+
+# Project-specific
+ignore/
+*.zarr/
+.claude/
+test_corrections.zarr/
+correction_slices/
+corrections/
+output/

cellmap_flow/cli/server_cli.py

@@ -23,7 +23,6 @@
 from cellmap_flow.utils.plugin_manager import load_plugins
 
 
-logging.basicConfig()
 logger = logging.getLogger(__name__)
 
 
@@ -47,7 +46,7 @@ def cli(log_level):
         cellmap_flow_server script -s /path/to/script.py -d /path/to/data
         cellmap_flow_server cellmap -f /path/to/model -n mymodel -d /path/to/data
     """
-    logging.basicConfig(level=getattr(logging, log_level.upper()))
+    logging.basicConfig(level=getattr(logging, log_level.upper()), force=True)
 
 
 @cli.command(name="list-models")

cellmap_flow/cli/yaml_cli.py

@@ -58,16 +58,17 @@ def _submit_model(model):
         )
         return model_name
 
-    with ThreadPoolExecutor(max_workers=len(models)) as executor:
-        futures = {executor.submit(_submit_model, model): model for model in models}
-        for future in as_completed(futures):
-            try:
-                name = future.result()
-                logger.info(f"Job for {name} is ready")
-            except Exception as e:
-                model = futures[future]
-                model_name = getattr(model, "name", None) or type(model).__name__
-                logger.error(f"Failed to start job for {model_name}: {e}")
+    if models:
+        with ThreadPoolExecutor(max_workers=len(models)) as executor:
+            futures = {executor.submit(_submit_model, model): model for model in models}
+            for future in as_completed(futures):
+                try:
+                    name = future.result()
+                    logger.info(f"Job for {name} is ready")
+                except Exception as e:
+                    model = futures[future]
+                    model_name = getattr(model, "name", None) or type(model).__name__
+                    logger.error(f"Failed to start job for {model_name}: {e}")
 
     generate_neuroglancer_url(dataset_path,wrap_raw=wrap_raw)
 
@@ -191,7 +192,11 @@ def main(config_path: str, log_level: str, list_types: bool, validate_only: bool
 
     # Build model configuration objects dynamically
     logger.info("Building model configurations...")
-    g.models_config = build_models(config["models"])
+    if config["models"]:
+        g.models_config = build_models(config["models"])
+    else:
+        g.models_config = []
+        logger.info("No models configured — starting dashboard for interactive use")
 
     logger.info(f"Configured {len(g.models_config)} model(s):")
     for i, model in enumerate(g.models_config, 1):

cellmap_flow/dashboard/app.py

@@ -5,15 +5,15 @@
 from flask import Flask
 from flask_cors import CORS
 
-from cellmap_flow.dashboard import state
-from cellmap_flow.dashboard.state import LogHandler
+from cellmap_flow.globals import g, LogHandler
 from cellmap_flow.dashboard.routes.logging_routes import logging_bp
 from cellmap_flow.dashboard.routes.index_page import index_bp
 from cellmap_flow.dashboard.routes.pipeline_builder_page import pipeline_builder_bp
 from cellmap_flow.dashboard.routes.models import models_bp
 from cellmap_flow.dashboard.routes.pipeline import pipeline_bp
 from cellmap_flow.dashboard.routes.blockwise import blockwise_bp
 from cellmap_flow.dashboard.routes.bbx_generator import bbx_bp
+from cellmap_flow.dashboard.routes.finetune import finetune_bp
 
 logger = logging.getLogger(__name__)
 
@@ -37,11 +37,12 @@
 app.register_blueprint(pipeline_bp)
 app.register_blueprint(blockwise_bp)
 app.register_blueprint(bbx_bp)
+app.register_blueprint(finetune_bp)
 
 
 def create_and_run_app(neuroglancer_url=None, inference_servers=None):
-    state.NEUROGLANCER_URL = neuroglancer_url
-    state.INFERENCE_SERVER = inference_servers
+    g.NEUROGLANCER_URL = neuroglancer_url
+    g.INFERENCE_SERVER = inference_servers
     hostname = socket.gethostname()
     port = 0
     logger.warning(f"Host name: {hostname}")