Update mtdna_backend.py

mtdna_backend.py

@@ -17,6 +17,7 @@ from oauth2client.service_account import ServiceAccountCredentials
 from io import StringIO
 import hashlib
 import threading
+import confidence_score
 
 # @lru_cache(maxsize=3600)
 # def classify_sample_location_cached(accession):
@@ -299,7 +300,12 @@ async def summarize_results(accession, stop_flag=None, niche_cases=None):
             label = key + "(Isolate: " + outputs[key]["isolate"] + ")"
         else: label = key  
         if len(outputs[key]["source"]) == 0:  outputs[key]["source"] = ["No Links"]
-        
+        # signals for confidence score
+        signals_confidence_score = outputs[key]["signals"]
+        rules = confidence_score.set_rules()
+        score, tier, explanations_score = confidence_score.compute_confidence_score_and_tier(signals_confidence_score,rules)
+        confidence_values = f"{tier} ({score})" + "\n" + explanations_score
+          
         if niche_cases:
             row = {
                 "Sample ID": truncate_cell(label or "unknown"),
@@ -310,7 +316,8 @@ async def summarize_results(accession, stop_flag=None, niche_cases=None):
                 "Predicted " + niche_cases[0]: truncate_cell(pred_niche or "unknown"),
                 niche_cases[0] + " Explanation": truncate_cell(niche_explanation or "unknown"),
                 "Sources": truncate_cell("\n".join(outputs[key]["source"]) or "No Links"),
-                "Time cost": truncate_cell(outputs[key]["time_cost"])
+                "Time cost": truncate_cell(outputs[key]["time_cost"]),
+                "Confidence Score":confidence_values,
             }
             #row_score.append(row)
             # rows.append(list(row.values()))
@@ -328,7 +335,8 @@ async def summarize_results(accession, stop_flag=None, niche_cases=None):
             "Query_cost": outputs[key]["query_cost"] or "",
             "Time cost": outputs[key]["time_cost"] or "",
             "file_chunk": truncate_cell(outputs[key]["file_chunk"] or ""),
-            "file_all_output": truncate_cell(outputs[key]["file_all_output"] or "")
+            "file_all_output": truncate_cell(outputs[key]["file_all_output"] or ""),
+            "Confidence Score":confidence_values,
             }
     
             #row_score.append(row)
@@ -342,7 +350,8 @@ async def summarize_results(accession, stop_flag=None, niche_cases=None):
                 "Predicted Sample Type": truncate_cell(pred_sample or "unknown"),
                 "Sample Type Explanation": truncate_cell(sample_explanation or "unknown"),
                 "Sources": truncate_cell("\n".join(outputs[key]["source"]) or "No Links"),
-                "Time cost": truncate_cell(outputs[key]["time_cost"])
+                "Time cost": truncate_cell(outputs[key]["time_cost"]),
+                "Confidence Score":confidence_values,
             }
             #row_score.append(row)
             # rows.append(list(row.values()))
@@ -357,7 +366,8 @@ async def summarize_results(accession, stop_flag=None, niche_cases=None):
             "Query_cost": outputs[key]["query_cost"] or "",
             "Time cost": outputs[key]["time_cost"] or "",
             "file_chunk": truncate_cell(outputs[key]["file_chunk"] or ""),
-            "file_all_output": truncate_cell(outputs[key]["file_all_output"] or "")
+            "file_all_output": truncate_cell(outputs[key]["file_all_output"] or ""),
+            "Confidence Score":confidence_values,
             }
 
             #row_score.append(row)