Update mtdna_backend.py

mtdna_backend.py

@@ -23,10 +23,10 @@ import threading
 #     return classify_sample_location(accession)
 
 #@lru_cache(maxsize=3600)
-def pipeline_classify_sample_location_cached(accession,stop_flag=None, save_df=None):
+async def pipeline_classify_sample_location_cached(accession,stop_flag=None, save_df=None):
     print("inside pipeline_classify_sample_location_cached, and [accession] is ", [accession])
     print("len of save df: ", len(save_df))
-    return pipeline.pipeline_with_gemini([accession],stop_flag=stop_flag, save_df=save_df)    
+    return await pipeline.pipeline_with_gemini([accession],stop_flag=stop_flag, save_df=save_df)    
 
 # Count and suggest final location
 # def compute_final_suggested_location(rows):
@@ -152,7 +152,7 @@ def get_incomplete_accessions(file_path):
 # GOOGLE_SHEET_NAME = "known_samples"
 # USAGE_DRIVE_FILENAME = "user_usage_log.json"
 
-def summarize_results(accession, stop_flag=None):
+async def summarize_results(accession, stop_flag=None):
     # Early bail
     if stop_flag is not None and stop_flag.value:
         print(f"🛑 Skipping {accession} before starting.")
@@ -189,7 +189,7 @@ def summarize_results(accession, stop_flag=None):
 
         save_df = pd.DataFrame(data[1:], columns=data[0])
         print("before pipeline, len of save df: ", len(save_df))
-        outputs = pipeline_classify_sample_location_cached(accession, stop_flag, save_df)
+        outputs = await pipeline_classify_sample_location_cached(accession, stop_flag, save_df)
         if stop_flag is not None and stop_flag.value:
             print(f"🛑 Skipped {accession} mid-pipeline.")
             return []