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import streamlit as st
import pandas as pd
import joblib
import os
# Adjust paths based on container directory structure
BASE_DIR = os.path.dirname(os.path.dirname(__file__)) # one level up from src/
MODEL_PATH = os.path.join(BASE_DIR, "Hb_predict", "tuned_xgboost_model.pkl")
SCALER_PATH = os.path.join(BASE_DIR, "Hb_predict", "scaler.pkl")
# Load model and scaler
model = joblib.load(MODEL_PATH)
scaler = joblib.load(SCALER_PATH)
st.title("Hemoglobin Level Predictor")
st.markdown(
"""
### Developed by Dr. Vinod Kumar Yata's research group
School of Allied and Healthcare Sciences, Malla Reddy University, Hyderabad, India
---
⚠️ **Warning**:
This is an experimental tool and should not be used for medical diagnosis.
Always consult a licensed healthcare provider for medical advice.
---
"""
)
# User inputs
age = st.number_input("Age", min_value=0, max_value=120, value=30)
sbp = st.number_input("Systolic Blood Pressure (SBP)", min_value=50, max_value=200, value=120)
dbp = st.number_input("Diastolic Blood Pressure (DBP)", min_value=30, max_value=130, value=80)
heart_rate = st.number_input("Heart Rate", min_value=30, max_value=200, value=75)
respiratory_rate = st.number_input("Respiratory Rate", min_value=5, max_value=60, value=18)
temperature_c = st.number_input("Temperature (°C)", min_value=30.0, max_value=45.0, value=36.5, format="%.1f")
oxygen_saturation = st.slider("Oxygen Saturation (%)", min_value=50.0, max_value=100.0, value=98.0)
gender = st.selectbox("Gender", options=["Male", "Female"])
# Calculate derived features
gender_encoded = 1 if gender == "Male" else 0
gender_male = gender_encoded
pulse_pressure = sbp - dbp
# Prepare input DataFrame matching model feature names exactly
input_df = pd.DataFrame([{
"Age": age,
"SBP": sbp,
"DBP": dbp,
"HeartRate": heart_rate,
"RespiratoryRate": respiratory_rate,
"TemperatureC": temperature_c,
"OxygenSaturation(%)": oxygen_saturation,
"Gender_Encoded": gender_encoded,
"Pulse_Pressure": pulse_pressure,
"Gender_Male": gender_male
}])
# Predict on button click
if st.button("Predict Hemoglobin Level"):
try:
input_scaled = scaler.transform(input_df)
prediction = model.predict(input_scaled)[0]
st.success(f"Predicted Hemoglobin Level: {prediction:.2f} g/dL")
except Exception as e:
st.error(f"Prediction failed: {e}")
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