Create app.py

app.py

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+import gradio as gr
+import joblib
+import numpy as np
+
+# Load the model
+model_filename = 'gbdt_inv_cd_ratio.joblib'
+gbdt = joblib.load(model_filename)
+
+# Define the prediction function
+def predict_immune_risk(ca, mg, zn, phos):
+    # Prepare input data
+    input_data = np.array([[ca, mg, zn, phos]])
+    
+    # Make predictions
+    predicted_class = gbdt.predict(input_data)[0]
+    predicted_probabilities = gbdt.predict_proba(input_data)[0]
+
+    # Determine the risk label and probability
+    if predicted_class == 1 or predicted_probabilities[1] > 0.5:
+        risk_label = "High Immune Risk (Inverted CD4/CD8 Ratio)"
+        probability = predicted_probabilities[1] * 100
+    else:
+        risk_label = "Low Immune Risk (Normal CD4/CD8 Ratio)"
+        probability = (1 - predicted_probabilities[1]) * 100  # Using the probability of the other class
+
+    return risk_label, f"{probability:.2f}%"
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict_immune_risk,
+    inputs=[gr.Number(label="Calcium (mg/dL)"),
+            gr.Number(label="Magnesium (mg/dL)"),
+            gr.Number(label="Zinc (mg/dL)"),
+            gr.Number(label="Phosphate (mg/dL)")],
+    outputs=[gr.Textbox(label="Immune Risk Prediction"),
+             gr.Textbox(label="Predicted Probability")],
+    title="HIV Immune Risk Predictor - CD4/CD8 Ratio and Essential Mineral Status",
+    description="Enter the levels of Calcium, Magnesium, Zinc, and Phosphate to predict immune risk based on Inversion of CD4/CD8 ratio."
+)
+
+# Launch the interface
+iface.launch()