app.py

import gradio as gr
import pandas as pd
import numpy as np
import joblib
import onnxruntime as ort
import os
import logging

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

# Set feature names. CRUCIAL - must match your training data
feature_names = ['Age', 'Sex', 'CD4+ T-cell count', 'Viral load', 'WBC count', 'Hemoglobin', 'Platelet count']

# Initialize model and scaler (set to None initially)
ort_session = None
scaler = None
model_loaded = False
scaler_loaded = False

# --- Attempt to Load Model and Scaler ---
try:
    # 1. Set the current working directory (as a precaution)
    script_dir = os.path.dirname(os.path.abspath(__file__))
    os.chdir(script_dir)
    logging.info(f"Current working directory set to: {os.getcwd()}")

    # 2. Check if files exist
    model_path = "hiv_model.onnx"
    scaler_path = "hiv_scaler.pkl"

    if not os.path.exists(model_path):
        logging.error(f"Model file not found: {model_path}")
        raise FileNotFoundError(f"Model file not found: {model_path}")

    if not os.path.exists(scaler_path):
        logging.error(f"Scaler file not found: {scaler_path}")
        raise FileNotFoundError(f"Scaler file not found: {scaler_path}")

    # 3. Load the model and scaler
    ort_session = ort.InferenceSession(model_path)
    scaler = joblib.load(scaler_path)

    model_loaded = True
    scaler_loaded = True

    logging.info("Model and scaler loaded successfully.")

except FileNotFoundError as e:
    logging.error(f"File not found: {e}")
    ort_session = None
    scaler = None
    model_loaded = False
    scaler_loaded = False # Make sure these are false if loading fails!

except Exception as e:
    logging.exception(f"An error occurred while loading the model or scaler: {e}")
    ort_session = None
    scaler = None
    model_loaded = False
    scaler_loaded = False
    # Log the full exception traceback for debugging
# --- End Model Loading ---

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.  Check the logs for errors. 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]) #Use ALL features now.
        scaled_df = pd.DataFrame(scaled_values, columns=feature_names)  #Use ALL feature names now.

        # 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
        if 0 < risk_probability <= 100:
            return f"HIV Risk Probability: {risk_probability:.4f}"
        elif risk_probability > 100:
            return f"HIV Risk Probability: 100"
        else:
            return f"HIV Risk Probability: 0"

    except Exception as e:
        logging.exception(f"An error occurred during prediction: {e}")
        return f"An error occurred during prediction: {e}.  Check the logs for details."

# 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()