Update app.py

app.py

@@ -2,9 +2,9 @@ import streamlit as st
 import pandas as pd
 import joblib
 
-# Load model and scaler with correct filenames
-model = joblib.load("model/tuned_xgboost_model.pkl")
-scaler = joblib.load("model/scaler.pkl")
+# Load model and scaler (assumed to be in root directory)
+model = joblib.load("tuned_xgboost_model.pkl")
+scaler = joblib.load("scaler.pkl")
 
 st.title("Hemoglobin Level Predictor")
 
@@ -22,32 +22,40 @@ st.markdown(
     unsafe_allow_html=True
 )
 
-# Input fields (ensure the feature names match your trained model's expected features)
+# Collect 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)
+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("Oxygen 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 expected model encoding
-gender_male = 1 if gender == "Male" else 0
-gender_female = 1 - gender_male  # if needed for one-hot features
+# Auto-derived features
+gender_encoded = 1 if gender == "Male" else 0
+gender_male = gender_encoded
+pulse_pressure = sbp - dbp
 
-# Build input dataframe with exact feature names your model expects
-input_df = pd.DataFrame([{
+# Create input DataFrame with exactly the features expected by the model
+input_data = pd.DataFrame([{
     "Age": age,
-    "Gender_Encoded": gender_male,          # or "Gender_Male" if that’s the column name
-    "OxygenSaturation(%)": o2_saturation,   # exact feature name from training
-    "BP_Systolic": bp_systolic,
-    "DBP": bp_diastolic,
-    "Respiratory_Rate": respiratory_rate,
-    # Add any other features your model needs here, with correct names!
+    "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
 }])
 
+# Run prediction
 if st.button("Predict Hemoglobin Level"):
-    # Scale inputs
-    input_scaled = scaler.transform(input_df)
-    # Predict using model
-    prediction = model.predict(input_scaled)[0]
-    st.success(f"Predicted Hemoglobin Level: {prediction:.2f} g/dL")
+    try:
+        input_scaled = scaler.transform(input_data)
+        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}")