Create app.py

app.py

@@ -0,0 +1,35 @@
+import gradio as gr
+import joblib
+import pandas as pd
+
+# Load the trained model and label encoder
+model = joblib.load("employability_model.pkl")
+label_encoder = joblib.load("label_encoder.pkl")
+
+# 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"
+]
+
+def predict_employability(*inputs):
+    # Convert inputs into DataFrame
+    user_df = pd.DataFrame([inputs], columns=FEATURES)
+    
+    # Make prediction
+    prediction = model.predict(user_df)[0]
+    result = "✅ Employable" if prediction == 1 else "😞 Less Employable"
+    return result
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_employability,
+    inputs=[gr.Slider(1, 5, step=1, label=feature) for feature in FEATURES],
+    outputs="text",
+    title="Employability Prediction",
+    description="Rate yourself on the following attributes (1-5) to check if you're employable!"
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()