Update app.py

app.py

@@ -118,8 +118,33 @@ def analyze_productivity(task_completion, stress, satisfaction, support, collabo
 
     return f"Cluster {cluster}: {cluster_description}", tip
 
-# Re-using the same logic for the 'what-if' scenario
-analyze_what_if = analyze_productivity
+def analyze_what_if(what_if_task_completion, what_if_stress, what_if_satisfaction, what_if_support, what_if_collaboration):
+    """
+    Analyzes 'what-if' user productivity metrics, predicts a cluster, and generates a tip.
+    """
+    user_data = pd.DataFrame([{
+        "Task_Completion_Rate": what_if_task_completion,
+        "Stress_Level": what_if_stress,
+        "Job_Satisfaction": what_if_satisfaction,
+        "Manager_Support_Level": what_if_support,
+        "Team_Collaboration_Frequency": what_if_collaboration,
+        "Work_Life_Balance_Score": 0,
+        "Communication_Overhead": 0
+    }])
+
+    # One-hot encode the user input
+    user_processed = pd.get_dummies(user_data, columns=["Manager_Support_Level", "Team_Collaboration_Frequency"], drop_first=True)
+    user_aligned = user_processed.reindex(columns=df_processed.columns, fill_value=0)
+    scaled_input = scaler.transform(user_aligned)
+
+    # Predict the cluster
+    cluster = kmeans.predict(scaled_input)[0]
+    cluster_description = cluster_descriptions[cluster]
+
+    # Generate the tip
+    tip = generate_tip(what_if_task_completion, what_if_stress, what_if_satisfaction, what_if_support, what_if_collaboration, cluster_description)
+
+    return f"Cluster {cluster}: {cluster_description}", tip
 
 # Function to save user feedback
 def save_feedback(tip, feedback):