Update llmeval.py

llmeval.py

@@ -8,10 +8,8 @@ client = Groq(api_key=AK)
 de=DatabaseEngine()
 
 
-
-
-
-SYSTEM_FOR_BIO_CONTEXT_ALIGNMENT=f'''
+def PROMPT_UPDATER(agenttype):
+    SYSTEM_FOR_BIO_CONTEXT_ALIGNMENT=f'''
 Task:
 Evaluate the biological quality of a Prompt, Context, and Response from an {agenttype} Agent on a 0–10 continuous scale.
 
@@ -52,7 +50,7 @@ No extra commentary, no markdown, no explanations before or after.
 Think step by step
 '''
 
-SYSTEM_FOR_CONTEXTUAL_RELEVANCE_ALIGNMENT=f'''
+    SYSTEM_FOR_CONTEXTUAL_RELEVANCE_ALIGNMENT=f'''
 Task:
 Evaluate how well the {agenttype} Response addresses the specific Prompt by leveraging the provided Context on a 0–10 continuous scale.
 
@@ -93,7 +91,7 @@ Think step by step
 '''
 
 
-SYSTEM_PROMPT_FOR_TRIAD_COHERENCE=f'''
+    SYSTEM_PROMPT_FOR_TRIAD_COHERENCE=f'''
 Task:
 Evaluate the logical and semantic coherence of the Prompt, Context, and Response of {agenttype} as a unified set on a 0–10 continuous scale.
 
@@ -131,7 +129,7 @@ Think step by step
 '''
 
 
-SYSTEM_PROMPT_FOR_RESPONSE_SPECIFICITY=f'''
+    SYSTEM_PROMPT_FOR_RESPONSE_SPECIFICITY=f'''
 Task:
 Evaluate how focused, detailed, and context-aware the {agenttype} Response is with respect to the Prompt and Context on a 0–10 continuous scale.
 
@@ -170,6 +168,9 @@ Think step by step
 
 '''
 
+    return  SYSTEM_FOR_BIO_CONTEXT_ALIGNMENT,SYSTEM_FOR_CONTEXTUAL_RELEVANCE_ALIGNMENT,SYSTEM_PROMPT_FOR_RESPONSE_SPECIFICITY,SYSTEM_PROMPT_FOR_TRIAD_COHERENCE 
+
+    
 class LLM_as_Evaluator():
     
     def __init__(self):
@@ -198,6 +199,7 @@ class LLM_as_Evaluator():
         
         data_to_evaluate=de.GetData(promptversion)
         
+        SYSTEM_FOR_BIO_CONTEXT_ALIGNMENT,SYSTEM_FOR_CONTEXTUAL_RELEVANCE_ALIGNMENT,SYSTEM_PROMPT_FOR_RESPONSE_SPECIFICITY,SYSTEM_PROMPT_FOR_TRIAD_COHERENCE = PROMPT_UPDATER("observation agent")
         
         evaluation_responses=[]
 
@@ -263,7 +265,7 @@ class LLM_as_Evaluator():
                     evaluation_responses.append({"unit_coherence":evaluation_response})
 
 
-            data={
+        data={
                 
                 
                     "promptversion":promptversion,
@@ -271,17 +273,17 @@ class LLM_as_Evaluator():
                     "contextual_relevance_alignment":"",
                     "unit_coherence":"",
                     "response_specificity":""
-                }
+        }
             
-            for resp in evaluation_responses:
+        for resp in evaluation_responses:
                 
-                data["biological_context_alignment"]=resp["biological_context_alignment"]
-                data["contextual_relevance_alignment"]=resp["contextual_relevance_alignment"]
-                data["unit_coherence"]=resp["unit_coherence"]
-                data["response_specificity"]=resp["response_specificity"]
+            data["biological_context_alignment"]=resp["biological_context_alignment"]
+            data["contextual_relevance_alignment"]=resp["contextual_relevance_alignment"]
+            data["unit_coherence"]=resp["unit_coherence"]
+            data["response_specificity"]=resp["response_specificity"]
                 
                 
-            de.Update(data=data)
+        de.Update(data=data)