Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,12 +1,12 @@
 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")
+# Adjust these paths based on where your model/scaler are in the container
+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")
 
 model = joblib.load(MODEL_PATH)
 scaler = joblib.load(SCALER_PATH)
@@ -26,27 +26,38 @@ st.markdown(
     """
 )
 
-# Input fields
+# Input fields for features your model expects
 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)
+oxygen_saturation = st.slider("Oxygen Saturation (%)", min_value=50.0, max_value=100.0, value=98.0)
 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
+# Calculate derived features
+gender_encoded = 1 if gender == "Male" else 0
+gender_male = gender_encoded
+pulse_pressure = sbp - dbp
 
 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
+    "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
 }])
 
 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")
+    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}")