Update app.py

app.py

@@ -23,7 +23,7 @@ def predict_attrition_risk(employee_name: str, sentiment: str, explanation: str)
     risk_mapping = {"positive": "Low Risk", "neutral": "Medium Risk", "negative": "High Risk"}
     risk_level = risk_mapping.get(sentiment.lower(), "Unknown Sentiment")
 
-    return f"**{employee_name}**: {risk_level} risk\n\n📝 {explanation}"
+    return f"**{employee_name}**: {risk_level} \n\n📝 {explanation}"
 
 
 def analyze_attrition_with_llm(df_dict, hr_query):
@@ -32,29 +32,21 @@ 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()}
-
-    # LLM function calling with enhanced prompt
-    prompt = f"""
-    HR Query: {hr_query}
-    Employees Data: {json.dumps(employees_data, indent=2)}
-
-    Based on the sentiment of employee feedback, predict their attrition risk as High, Medium, or Low.
-    Additionally, provide a **short summary (~50 words)** explaining why this risk level was assigned.
-    """
     
+    prompt = f"HR Query: {hr_query}\nEmployees Data: {json.dumps(employees_data, indent=2)}"
     response = client.chat.completions.create(
-        model="gpt-4-turbo",
+        model="gpt-4o-mini",
         messages=[{"role": "user", "content": prompt}],
         functions=[
             {
                 "name": "predict_attrition_risk",
-                "description": "Predicts attrition risk based on sentiment and explains the reasoning.",
+                "description": "Predicts attrition risk based on sentiment and provides explanation.",
                 "parameters": {
                     "type": "object",
                     "properties": {
                         "employee_name": {"type": "string", "description": "Employee's name"},
                         "sentiment": {"type": "string", "description": "Extracted sentiment"},
-                        "explanation": {"type": "string", "description": "Short reasoning for the risk level (50 words)."}
+                        "explanation": {"type": "string", "description": "Brief explanation"}
                     },
                     "required": ["employee_name", "sentiment", "explanation"]
                 }
@@ -63,20 +55,9 @@ def analyze_attrition_with_llm(df_dict, hr_query):
         function_call="auto"
     )
 
-    message = response.choices[0].message
-    if message.function_call:
-        try:
-            function_call = json.loads(message.function_call.arguments)
-            employee_name = function_call.get("employee_name")
-            sentiment = function_call.get("sentiment")
-            explanation = function_call.get("explanation")
+    # Directly return the full response as a formatted string
+    return response.choices[0].message.content if response.choices[0].message.content else "🤖 No response generated."
 
-            if employee_name and sentiment:
-                return f"**{employee_name}**: {sentiment} risk\n\n📝 {explanation}"
-        except Exception as e:
-            return f"❌ Error processing LLM function call: {str(e)}"
-    
-    return "🤖 I'm sorry, but I can only answer queries related to employee attrition risk."
 
 with gr.Blocks() as demo:
     gr.Markdown("<h1>AI-Driven Employee Attrition Risk Analysis</h1>")