Update app.py

app.py

@@ -1,35 +1,42 @@
-import gradio as gr
 import joblib
-import pandas as pd
-
-# Load the trained model and label encoder
-model = joblib.load("employability_model_logreg.joblib")  
-label_encoder = joblib.load("label_encoder_fixed.joblib")  
-
-# Define feature names based on the dataset
-FEATURES = [
-    "GENERAL APPEARANCE", "MANNER OF SPEAKING", "PHYSICAL CONDITION", "MENTAL ALERTNESS",
-    "SELF-CONFIDENCE", "ABILITY TO PRESENT IDEAS", "COMMUNICATION SKILLS", "Student Performance Rating"
-]
+import gradio as gr
+import numpy as np
 
-def predict_employability(*inputs):
-    # Convert inputs into DataFrame
-    user_df = pd.DataFrame([inputs], columns=FEATURES)
+def predict_employability(manner_of_speaking, self_confidence, ability_to_present_ideas, communication_skills, mental_alertness):
+    # Load the updated model and label encoder
+    model = joblib.load("employability_model_selected.joblib")
+    label_encoder = joblib.load("label_encoder_fixed.joblib") 
+    
+    # Prepare the input data
+    input_data = np.array([[manner_of_speaking, self_confidence, ability_to_present_ideas, communication_skills, mental_alertness]])
     
     # Make prediction
-    prediction = model.predict(user_df)[0]
-    result = "✅ Employable" if prediction == 1 else "😞 Less Employable"
-    return result
+    prediction = model.predict(input_data)[0]
+    
+    # Decode the prediction
+    result = label_encoder.inverse_transform([prediction])[0]
+    
+    # Return the result with an emoji
+    if result == "Employable":
+        return f"✅ {result}"
+    else:
+        return f"😞 {result}"
 
-# Create the Gradio interface
+# Define the Gradio interface
 iface = gr.Interface(
     fn=predict_employability,
-    inputs=[gr.Slider(1, 5, step=1, label=feature) for feature in FEATURES],
-    outputs="text",
+    inputs=[
+        gr.Slider(1, 5, step=1, label="Manner of Speaking"),
+        gr.Slider(1, 5, step=1, label="Self-Confidence"),
+        gr.Slider(1, 5, step=1, label="Ability to Present Ideas"),
+        gr.Slider(1, 5, step=1, label="Communication Skills"),
+        gr.Slider(1, 5, step=1, label="Mental Alertness")
+    ],
+    outputs=gr.Textbox(label="Prediction"),
     title="Employability Prediction",
-    description="Rate yourself on the following attributes (1-5) to check if you're employable!"
+    description="Rate yourself on the given attributes (1-5) to check your employability status."
 )
 
-# Launch the app
+# Run the Gradio app
 if __name__ == "__main__":
     iface.launch()