add gradio

app.py

@@ -1,18 +1,18 @@
 import joblib
-import gradio as gr
 import numpy as np
+import gradio as gr
 
-# Load the model
+# Load your trained model
 model = joblib.load("model.joblib")
 
-# Define prediction function
-def predict(input1, input2, input3):  # adjust based on your features
-    X = np.array([[input1, input2, input3]])
-    prediction = model.predict(X)
-    return prediction[0]
+# Define the prediction function
+def predict(input1, input2, input3):
+    inputs = np.array([[input1, input2, input3]])
+    prediction = model.predict(inputs)
+    return str(prediction[0])  # Convert to string for display
 
-# Create Gradio interface
-demo = gr.Interface(
+# Build the Gradio interface
+interface = gr.Interface(
     fn=predict,
     inputs=[
         gr.Number(label="Feature 1"),
@@ -20,9 +20,10 @@ demo = gr.Interface(
         gr.Number(label="Feature 3")
     ],
     outputs="text",
-    title="My Sklearn Model"
+    title="Datathon Model Predictor",
+    description="Enter 3 features to get prediction from the deployed model."
 )
 
-# Run Gradio app
+# Run the app
 if __name__ == "__main__":
-    demo.launch()
+    interface.launch()