Create app.py

app.py

@@ -0,0 +1,38 @@
+import gradio as gr
+from joblib import load
+import numpy as np
+
+# Load the trained XGBoost model
+model = load('Model_SCD_XGB.joblib')
+
+def predict_sickle_cell_crisis(Hct, Lymper, Age, JtPainFreq_yr):
+    # Prepare the input data as a numpy array
+    input_data = np.array([[Hct, Lymper, Age, JtPainFreq_yr]])
+    
+    # Use the model to make a prediction
+    prediction = model.predict(input_data)[0]
+    prediction_prob = model.predict_proba(input_data)[0]
+    
+    # Calculate the scaled risk score
+    risk_score = 2 * prediction_prob[1] - 1
+    
+    # Prepare the result string
+    result_str = f"The XSCP (XGBoost Sickle-Cell Crisis Prediction) Model predicts the chances of patient going into Sickle Cell Crisis as {'Positive' if prediction == 1 else 'Negative'}, with a scaled probability Risk Score of : {risk_score:.2f}. This trained model has a F1 Accuracy of 1.00 with a 100% Precision and Recall."
+    
+    return result_str
+
+# Create Gradio Interface
+iface = gr.Interface(
+    fn=predict_sickle_cell_crisis, 
+    inputs=[
+        gr.inputs.Number(label="Hematocrit value in %"),
+        gr.inputs.Number(label="Lymphocyte % of TLC"),
+        gr.inputs.Slider(minimum=0, maximum=120, default=25, label="Age (in years)"),  # Slider for Age
+        # gr.inputs.Number(label="Hb Haemoglobin in g/dL"),
+        gr.inputs.Number(label="Joint Pain Frequency as Counts in past one year"),
+        # gr.inputs.Radio([0, 1], label="Any H/O Joint Pain & Swelling in the past one year (1 - Yes, 0 - No)")
+    ], 
+    outputs=gr.outputs.Textbox()
+)
+
+iface.launch(inbrowser=True)