Update src/streamlit_app.py

src/streamlit_app.py

@@ -3,11 +3,12 @@ import pandas as pd
 import joblib
 import os
 
-# Adjust these paths based on where your model/scaler are in the container
+# 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)
 
@@ -26,13 +27,13 @@ st.markdown(
     """
 )
 
-# Input fields for features your model expects
+# 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)
+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"])
 
@@ -41,6 +42,7 @@ 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,
@@ -54,6 +56,7 @@ input_df = pd.DataFrame([{
     "Gender_Male": gender_male
 }])
 
+# Predict on button click
 if st.button("Predict Hemoglobin Level"):
     try:
         input_scaled = scaler.transform(input_df)