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853e9d1 3fc3fa4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | import streamlit as st
import pandas as pd
import joblib
import numpy as np
import os
# Load model and scaler
MODEL_PATH = os.path.join(os.path.dirname(__file__), "model", "xgb_model.joblib")
SCALER_PATH = os.path.join(os.path.dirname(__file__), "model", "scaler.joblib")
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.
---
"""
)
# Input fields
age = st.number_input("Age", min_value=0, max_value=120, value=30)
gender = st.selectbox("Gender", options=["Male", "Female"])
o2_saturation = st.slider("O2 Saturation (%)", min_value=50.0, max_value=100.0, value=98.0)
bp_systolic = st.number_input("Systolic BP", min_value=50, max_value=200, value=120)
bp_diastolic = st.number_input("Diastolic BP", min_value=30, max_value=130, value=80)
respiratory_rate = st.number_input("Respiratory Rate (breaths/min)", min_value=5, max_value=60, value=18)
# Map gender to numeric
gender_num = 1 if gender == "Male" else 0
input_df = pd.DataFrame([{
"Age": age,
"Gender": gender_num,
"O2_Saturation": o2_saturation,
"BP_Systolic": bp_systolic,
"BP_Diastolic": bp_diastolic,
"Respiratory_Rate": respiratory_rate
}])
if st.button("Predict Hemoglobin Level"):
input_scaled = scaler.transform(input_df)
prediction = model.predict(input_scaled)[0]
st.success(f"Predicted Hemoglobin Level: {prediction:.2f} g/dL")
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