Add logging and comment out unused code

main.py

@@ -14,12 +14,14 @@ from toolz import concat, groupby, valmap
 from starlette.responses import RedirectResponse
 from cashews import cache
 from datetime import timedelta
+import logging
 
 cache.setup("mem://")
 
 
+logger = logging.getLogger(__name__)
 app = FastAPI()
-logger = logging.get_logger(__name__)
+# logger = logging.get_logger(__name__)
 load_dotenv()
 HF_TOKEN = os.getenv("HF_TOKEN")
 
@@ -127,9 +129,9 @@ def load_model(repo_id: str) -> fasttext.FastText._FastText:
     model_path = hf_hub_download(
         repo_id,
         "model.bin",
-        cache_dir="code/models",
-        local_dir="code/models",
-        local_dir_use_symlinks=False,
+        # cache_dir="code/models",
+        # local_dir="code/models",
+        # local_dir_use_symlinks=False,
     )
     return fasttext.load_model(model_path)
 
@@ -194,22 +196,15 @@ def predict_rows(rows, target_column, language_threshold_percent=0.2):
     filtered_dict = {k: v for k, v in predictions_by_lang.items() if k in keys_to_keep}
     return {
         "predictions": dict(valmap(get_mean_score, filtered_dict)),
-        "pred": predictions,
     }
 
 
-# @app.get("/", response_class=HTMLResponse)
-# async def read_index():
-#     html_content = Path("index.html").read_text()
-#     return HTMLResponse(content=html_content)
-
-
 @app.get("/", include_in_schema=False)
 def root():
     return RedirectResponse(url="/docs")
 
 
-@app.get("/predict_dataset_language/{hub_id}")
+@app.get("/predict_dataset_language/{hub_id:path}")
 @cache(ttl=timedelta(minutes=10))
 async def predict_language(
     hub_id: str,