Update app.py

app.py

@@ -1,9 +1,22 @@
 import gradio as gr
+import pickle
+import numpy as np
 
+# Load the trained model
+with open("clf.pkl", "rb") as f:
+    clf = pickle.load(f)
+
+# Define the prediction function
 def predict(SupportiveGM, Merit, LearningDevelopment, WorkEnvironmente, Engagement, WellBeing, ChainScale):
-    # Replace this with your model logic
-    return f"Received: {SupportiveGM}, {Merit}, {LearningDevelopment}, {WorkEnvironmente}, {Engagement}, {WellBeing}, {ChainScale}"
+    # Convert inputs into a NumPy array
+    input_data = np.array([[SupportiveGM, Merit, LearningDevelopment, WorkEnvironmente, Engagement, WellBeing, ChainScale]])
+    
+    # Make prediction using the model
+    prediction = clf.predict(input_data)
+
+    return f"Predicted Turnover Probability: {prediction[0]}"
 
+# Create the Gradio interface
 iface = gr.Interface(
     fn=predict,
     inputs=["number"] * 7,
@@ -14,3 +27,4 @@ iface = gr.Interface(
 
 if __name__ == "__main__":
     iface.launch()
+