Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+import joblib
+import onnxruntime as ort
+
+# Load the ONNX model and scaler outside the function for efficiency
+try:
+    ort_session = ort.InferenceSession("hiv_model.onnx")
+    scaler = joblib.load("hiv_scaler.pkl")
+    feature_names = ['Age', 'Sex', 'CD4+ T-cell count', 'Viral load', 'WBC count', 'Hemoglobin', 'Platelet count']  # Match your training data
+
+    model_loaded = True
+    scaler_loaded = True
+except Exception as e:
+    print(f"Error loading model or scaler: {e}")
+    model_loaded = False
+    scaler_loaded = False
+    ort_session = None
+    scaler = None
+    feature_names = []  # Set to empty to avoid errors later
+
+def predict_risk(age, sex, cd4_count, viral_load, wbc_count, hemoglobin, platelet_count):
+    """
+    Predicts HIV risk probability based on input features.
+    """
+    if not model_loaded or not scaler_loaded:
+        return "Model or scaler not loaded. Please ensure 'hiv_model.onnx' and 'hiv_scaler.pkl' are in the same directory."
+
+    try:
+        # 1. Create a DataFrame
+        input_data = {
+            'Age': [age],
+            'Sex': [0 if sex == "Female" else 1],  # Encode Sex
+            'CD4+ T-cell count': [cd4_count],
+            'Viral load': [viral_load],
+            'WBC count': [wbc_count],
+            'Hemoglobin': [hemoglobin],
+            'Platelet count': [platelet_count]
+        }
+        input_df = pd.DataFrame(input_data)
+
+        # 2. Standardize the data
+        scaled_values = scaler.transform(input_df[feature_names])
+        scaled_df = pd.DataFrame(scaled_values, columns=feature_names)
+
+        # 3. ONNX Prediction
+        input_array = scaled_df[feature_names].values.astype(np.float32)  # Enforce float32
+        ort_inputs = {ort_session.get_inputs()[0].name: input_array}
+        ort_outs = ort_session.run(None, ort_inputs)
+
+        # 4. Process Output
+        probabilities = ort_outs[0][0]
+        risk_probability = probabilities[1] # Probability of High Risk
+
+        return f"High Risk Probability: {risk_probability:.4f}"
+
+    except Exception as e:
+        return f"An error occurred during prediction: {e}"
+
+
+# Define Gradio inputs
+age_input = gr.Number(label="Age", value=30)
+sex_input = gr.Radio(["Female", "Male"], label="Sex", value="Female")
+cd4_input = gr.Number(label="CD4+ T-cell count", value=500)
+viral_input = gr.Number(label="Viral load", value=10000)
+wbc_input = gr.Number(label="WBC count", value=7000)
+hemoglobin_input = gr.Number(label="Hemoglobin", value=14.0)
+platelet_input = gr.Number(label="Platelet count", value=250000)
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict_risk,
+    inputs=[age_input, sex_input, cd4_input, viral_input, wbc_input, hemoglobin_input, platelet_input],
+    outputs="text",
+    title="Sentinel-P1: HIV Risk Prediction Demo",
+    description="Enter blood report values to estimate HIV risk. This is a demonstration model and should not be used for medical advice.",
+)
+
+iface.launch()