Update app.py

app.py

@@ -30,7 +30,6 @@ def analyze_attrition_with_llm(df_dict, hr_query):
     df = df_dict["df"]
     employees_data = {row["Employee"].strip(): row["Sentiment"] for _, row in df.iterrows()}
     
-    # Use GPT-4-turbo to analyze the HR query
     response = client.chat.completions.create(
         model="gpt-4-turbo",
         messages=[
@@ -44,10 +43,9 @@ def analyze_attrition_with_llm(df_dict, hr_query):
                 "parameters": {
                     "type": "object",
                     "properties": {
-                        "employee_name": {"type": "string", "description": "Employee's name"},
-                        "sentiment": {"type": "string", "description": "Extracted sentiment"}
+                        "employee_names": {"type": "array", "items": {"type": "string"}, "description": "List of employee names"}
                     },
-                    "required": ["employee_name", "sentiment"]
+                    "required": ["employee_names"]
                 }
             }
         ],
@@ -58,13 +56,17 @@ def analyze_attrition_with_llm(df_dict, hr_query):
     if hasattr(message, "function_call") and message.function_call is not None:
         try:
             function_call = json.loads(message.function_call.arguments)
-            employee_name = function_call.get("employee_name")
-            sentiment = employees_data.get(employee_name)
+            employee_names = function_call.get("employee_names", [])
             
-            if sentiment:
-                return predict_attrition_risk(employee_name, sentiment)
-            else:
-                return f"{employee_name}: No records found for this employee."
+            results = []
+            for employee_name in employee_names:
+                sentiment = employees_data.get(employee_name)
+                if sentiment:
+                    results.append(predict_attrition_risk(employee_name, sentiment))
+                else:
+                    results.append(f"{employee_name}: No records found for this employee.")
+            
+            return "\n".join(results)
         except Exception as e:
             return f"❌ Error processing LLM function call: {str(e)}"