Create app.py

app.py

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+import gradio as gr
+import joblib
+import numpy as np
+
+# Load the saved model
+model = joblib.load('best_model.pkl')
+
+# Load the saved pipeline (which includes the scaler and the model)
+pipeline = joblib.load('best_pipeline.pkl')
+
+# Define the prediction function
+def predict(input1, input2, input3, input4, input5,input6):
+    # Create a numpy array from the inputs
+    inputs = np.array([input1, input2, input3, input4, input5,input6]).reshape(1, -1)
+    
+    # Transform the inputs using the scaler
+    inputs_scaled = pipeline.transform(inputs)
+    
+    # Make the prediction
+    prediction = model.predict(inputs_scaled)
+    
+    # Return the prediction and a description
+    return prediction[0], f"The predicted value is {prediction[0]:.2f}"
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=predict,  # Function to call
+    inputs=[
+        gr.Number(label="Age"),
+        gr.Number(label="Hours per day"),
+        gr.Number(label="Depression"),
+        gr.Number(label="Insomnia"),
+        gr.Number(label="OCD"),
+        gr.Number(label="BPM")
+    ],
+    outputs=[
+        gr.Number(label="Predicted Value"),
+        gr.Textbox(label="Prediction Description")
+    ],
+    title="Music & Mental Health Predictor",
+    description="This Model has been trained on this <a href="https://www.kaggle.com/datasets/catherinerasgaitis/mxmh-survey-results">Dataset</a>.",
+    theme=gr.themes.Soft(),
+    examples=[[18,3,0,1,0,156]]
+)
+
+# Launch the interface
+iface.launch(debug=True)