Update app.py

app.py

@@ -30,19 +30,48 @@ 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()}
     
-    employee_name = hr_query.strip()
-    sentiment = employees_data.get(employee_name)
-    if sentiment:
-        return predict_attrition_risk(employee_name, sentiment)
-    else:
-        return f"{employee_name}: No records found for this employee."
+    # LLM function calling
+    prompt = f"HR Query: {hr_query}\nEmployees Data: {json.dumps(employees_data, indent=2)}"
+    response = client.chat.completions.create(
+        model="gpt-4-turbo",
+        messages=[{"role": "user", "content": prompt}],
+        functions=[
+            {
+                "name": "predict_attrition_risk",
+                "description": "Predicts attrition risk based on sentiment.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "employee_name": {"type": "string", "description": "Employee's name"},
+                        "sentiment": {"type": "string", "description": "Extracted sentiment"}
+                    },
+                    "required": ["employee_name", "sentiment"]
+                }
+            }
+        ],
+        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")
+            
+            if employee_name and sentiment:
+                return predict_attrition_risk(employee_name, sentiment)
+        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>")
     file_input = gr.File(label="Upload Employee Feedback CSV", file_types=[".csv"])
     process_button = gr.Button("Process CSV")
     process_message = gr.Markdown()
-    hr_input = gr.Textbox(label="Employee Name")
+    hr_input = gr.Textbox(label="Employee Name or HR Query")
     analyze_button = gr.Button("Check Attrition Risk")
     output_text = gr.Markdown()
     df_state = gr.State()