[WIP] Template for InferenceAPI

setup.py

@@ -96,8 +96,10 @@ def _prepare_extras():
         # that you indicate whether you support Python 2, Python 3 or both.
         'Programming Language :: Python :: 3',
     ],
-    entry_points="""
-        [console_scripts]
-        torchserve-dashboard=torchserve_dashboard.cli:main
-        """,
+    entry_points={
+        'console_scripts':['torchserve-management=torchserve_dashboard.cli:manage',
+                          'torchserve-inference=torchserve_dashboard.cli:ui'] 
+                          # tentative..torchserve-dashboard may spin both independently.
+                          # definitely want things on different ports though (should have port assignements too, to avoid confusion)
+    }
 )

torchserve_dashboard/api/inference_api.py

@@ -0,0 +1,22 @@
+import os
+from typing import Any, Dict, List, Optional, Tuple, Union, Callable
+
+import httpx
+from httpx import Response
+
+import logging
+
+class InferenceAPI:
+    def __init__(self, address: str, error_callback: Callable = None) -> None:
+        pass
+
+    # get available models
+    # determine type of model (NOT currently possible, we don't know which endpoint maps to what type)
+    # auto generate python client based on model spec? (use library)
+    # or define explicitly like below (not sustainable)
+    def image_classify(endpoint,image):
+        # put image in request body
+        # 
+        # res send(address+endpoint,req)
+        # return res
+        pass

torchserve_dashboard/cli.py

@@ -10,7 +10,18 @@
 @configurator_options
 @click.argument("args", nargs=-1)
 @click.pass_context
-def main(ctx: click.Context, args: Any, **kwargs: Any):
+def manage(ctx: click.Context, args: Any, **kwargs: Any):
     dirname = os.path.dirname(__file__)
-    filename = os.path.join(dirname, 'dash.py')
+    filename = os.path.join(dirname, 'ui/management_dash.py')
+    ctx.forward(streamlit.cli.main_run, target=filename, args=args, *kwargs)
+
+
+@click.command(context_settings=dict(ignore_unknown_options=True,
+                                     allow_extra_args=True))
+@configurator_options
+@click.argument("args", nargs=-1)
+@click.pass_context
+def ui(ctx: click.Context, args: Any, **kwargs: Any):
+    dirname = os.path.dirname(__file__)
+    filename = os.path.join(dirname, 'ui/inference_dash.py')
     ctx.forward(streamlit.cli.main_run, target=filename, args=args, *kwargs)

torchserve_dashboard/ui/inference_dash.py

@@ -0,0 +1,16 @@
+import streamlit as st
+
+#def ImageClassifier Component (may auto generate based on spec)
+
+
+#def ImageSegmentation Component
+
+
+#def TextClassifier Component
+
+#...
+
+# list available endpoints
+# show component based on model type (NOT currently possible, we don't know which endpoint maps to what type)
+# call endpoint api with inputs
+

torchserve_dashboard/dash.py → torchserve_dashboard/ui/management_dash.py

@@ -4,7 +4,8 @@
 import streamlit as st
 from httpx import Response
 
-from torchserve_dashboard.api import ManagementAPI, LocalTS
+from torchserve_dashboard.api.management_api import ManagementAPI, LocalTS
+from torchserve_dashboard import _PACKAGE_ROOT
 from pathlib import Path
 
 st.set_page_config(
@@ -48,7 +49,7 @@ def check_args(args):
     metrics_location = args.metrics_location
     if not os.path.exists(config_path):
         st.write(f"Can't find config file at {config_path}. Using default config instead")
-        config_path = os.path.join(os.path.dirname(__file__), "default.torchserve.properties")
+        config_path = os.path.join(_PACKAGE_ROOT, "default.torchserve.properties")
     if os.path.exists(config_path):
         config = open(config_path, "r").readlines()
         for c in config: