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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() |