Create app.py

app.py

@@ -0,0 +1,25 @@
+import gradio as gr
+from joblib import load
+import numpy as np
+
+# Load the trained model
+model = load("ML_Model_CallCorr.joblib")
+
+def predict_corrected_calcium(total_calcium, total_protein, albumin):
+    # Calculate Albumin to Total Protein Ratio
+    atr = albumin / total_protein
+    
+    # Predict the actual calcium value using the model
+    predicted_value = model.predict([[total_calcium, atr]])[0]
+    
+    # Return the result string
+    return f"The Actual Calcium value with Correction is {predicted_value:.2f}. The model is a RDF Regression Model with a MSE of 0.06 and R-squared of 0.931."
+
+# Define the Gradio interface
+interface = gr.Interface(fn=predict_corrected_calcium, 
+                         inputs=[gr.inputs.Number(label="Total Calcium in mg/dL"),
+                                 gr.inputs.Number(label="Total Protein in g/dL"),
+                                 gr.inputs.Number(label="Albumin in g/dL")],
+                         outputs=gr.outputs.Textbox())
+
+interface.launch()