Config: Isolate to a separate file

Reduce dependency of globals in main to simplify code a bit.

Signed-off-by: kingbri <bdashore3@proton.me>

main.py

@@ -1,7 +1,6 @@
 """The main tabbyAPI module. Contains the FastAPI server and endpoints."""
 import pathlib
 import uvicorn
-import yaml
 from asyncio import CancelledError
 from typing import Optional
 from uuid import uuid4
@@ -14,6 +13,14 @@ from progress.bar import IncrementalBar
 
 import gen_logging
 from auth import check_admin_key, check_api_key, load_auth_keys
+from config import (
+    read_config_from_file,
+    get_gen_logging_config,
+    get_model_config,
+    get_draft_model_config,
+    get_lora_config,
+    get_network_config
+)
 from generators import call_with_semaphore, generate_with_semaphore
 from model import ModelContainer
 from OAI.types.completion import CompletionRequest
@@ -48,7 +55,6 @@ app = FastAPI()
 
 # Globally scoped variables. Undefined until initalized in main
 MODEL_CONTAINER: Optional[ModelContainer] = None
-config: dict = {}
 
 
 def _check_model_container():
@@ -71,12 +77,11 @@ app.add_middleware(
 @app.get("/v1/model/list", dependencies=[Depends(check_api_key)])
 async def list_models():
     """Lists all models in the model directory."""
-    model_config = unwrap(config.get("model"), {})
+    model_config = get_model_config()
     model_dir = unwrap(model_config.get("model_dir"), "models")
     model_path = pathlib.Path(model_dir)
 
-    draft_config = unwrap(model_config.get("draft"), {})
-    draft_model_dir = draft_config.get("draft_model_dir")
+    draft_model_dir = get_draft_model_config().get("draft_model_dir")
 
     models = get_model_list(model_path.resolve(), draft_model_dir)
     if unwrap(model_config.get("use_dummy_models"), False):
@@ -127,9 +132,7 @@ async def get_current_model():
 @app.get("/v1/model/draft/list", dependencies=[Depends(check_api_key)])
 async def list_draft_models():
     """Lists all draft models in the model directory."""
-    model_config = unwrap(config.get("model"), {})
-    draft_config = unwrap(model_config.get("draft"), {})
-    draft_model_dir = unwrap(draft_config.get("draft_model_dir"), "models")
+    draft_model_dir = unwrap(get_draft_model_config().get("draft_model_dir"), "models")
     draft_model_path = pathlib.Path(draft_model_dir)
 
     models = get_model_list(draft_model_path.resolve())
@@ -149,13 +152,11 @@ async def load_model(request: Request, data: ModelLoadRequest):
     if not data.name:
         raise HTTPException(400, "model_name not found.")
 
-    model_config = unwrap(config.get("model"), {})
-    model_path = pathlib.Path(unwrap(model_config.get("model_dir"), "models"))
+    model_path = pathlib.Path(unwrap(get_model_config().get("model_dir"), "models"))
     model_path = model_path / data.name
 
     load_data = data.model_dump()
 
-    draft_config = unwrap(model_config.get("draft"), {})
     if data.draft:
         if not data.draft.draft_model_name:
             raise HTTPException(
@@ -163,7 +164,7 @@ async def load_model(request: Request, data: ModelLoadRequest):
             )
 
         load_data["draft"]["draft_model_dir"] = unwrap(
-            draft_config.get("draft_model_dir"), "models"
+            get_draft_model_config().get("draft_model_dir"), "models"
         )
 
     if not model_path.exists():
@@ -240,10 +241,7 @@ async def unload_model():
 @app.get("/v1/lora/list", dependencies=[Depends(check_api_key)])
 async def get_all_loras():
     """Lists all LoRAs in the lora directory."""
-    model_config = unwrap(config.get("model"), {})
-    lora_config = unwrap(model_config.get("lora"), {})
-    lora_path = pathlib.Path(unwrap(lora_config.get("lora_dir"), "loras"))
-
+    lora_path = pathlib.Path(unwrap(get_lora_config().get("lora_dir"), "loras"))
     loras = get_lora_list(lora_path.resolve())
 
     return loras
@@ -281,9 +279,7 @@ async def load_lora(data: LoraLoadRequest):
     if not data.loras:
         raise HTTPException(400, "List of loras to load is not found.")
 
-    model_config = unwrap(config.get("model"), {})
-    lora_config = unwrap(model_config.get("lora"), {})
-    lora_dir = pathlib.Path(unwrap(lora_config.get("lora_dir"), "loras"))
+    lora_dir = pathlib.Path(unwrap(get_lora_config().get("lora_dir"), "loras"))
     if not lora_dir.exists():
         raise HTTPException(
             400,
@@ -478,25 +474,16 @@ async def generate_chat_completion(request: Request, data: ChatCompletionRequest
 
 
 if __name__ == "__main__":
-    # Load from YAML config. Possibly add a config -> kwargs conversion function
-    try:
-        with open("config.yml", "r", encoding="utf8") as config_file:
-            config = unwrap(yaml.safe_load(config_file), {})
-    except Exception as exc:
-        logger.error(
-            "The YAML config couldn't load because of the following error: "
-            f"\n\n{exc}"
-            "\n\nTabbyAPI will start anyway and not parse this config file."
-        )
-        config = {}
+    # Load from YAML config
+    read_config_from_file(pathlib.Path("config.yml"))
 
-    network_config = unwrap(config.get("network"), {})
+    network_config = get_network_config()
 
     # Initialize auth keys
     load_auth_keys(unwrap(network_config.get("disable_auth"), False))
 
     # Override the generation log options if given
-    log_config = unwrap(config.get("logging"), {})
+    log_config = get_gen_logging_config()
     if log_config:
         gen_logging.update_from_dict(log_config)
 
@@ -504,7 +491,7 @@ if __name__ == "__main__":
 
     # If an initial model name is specified, create a container
     # and load the model
-    model_config = unwrap(config.get("model"), {})
+    model_config = get_model_config()
     if "model_name" in model_config:
         model_path = pathlib.Path(unwrap(model_config.get("model_dir"), "models"))
         model_path = model_path / model_config.get("model_name")
@@ -521,7 +508,7 @@ if __name__ == "__main__":
                 loading_bar.next()
 
     # Load loras
-    lora_config = unwrap(model_config.get("lora"), {})
+    lora_config = get_lora_config()
     if "loras" in lora_config:
         lora_dir = pathlib.Path(unwrap(lora_config.get("lora_dir"), "loras"))
         MODEL_CONTAINER.load_loras(lora_dir.resolve(), **lora_config)