Upload 4 files

app.py

@@ -0,0 +1,47 @@
+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)

best_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:694a06e780838bd2584d332a685bf9cd0cc882189898bfad4233797162b176e9
+size 8826646

best_pipeline (1).pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ac24b8da27cf83967f38bbc64f2004ac5cb96e25692443daf2d92e335b3ae284
+size 1221

requirements.txt

@@ -0,0 +1,3 @@
+joblib
+scikit-learn==1.3.2
+xgboost