Upload app.py

app.py

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+import streamlit as st
+import pickle
+import numpy as np
+
+# Load model and scaler
+model = pickle.load(open('model\model.pkl', 'rb'))
+scaler_model = pickle.load(open('model\scaler.pkl', 'rb'))
+
+# App title
+st.title("Diabetes Prediction App")
+st.write("Enter patient details to predict diabetes risk")
+
+# Input fields
+pregnancies = st.number_input("Pregnancies", min_value=0, step=1)
+glucose = st.number_input("Glucose Level", min_value=0.0)
+blood_pressure = st.number_input("Blood Pressure", min_value=0.0)
+skin_thickness = st.number_input("Skin Thickness", min_value=0.0)
+insulin = st.number_input("Insulin Level", min_value=0.0)
+bmi = st.number_input("BMI", min_value=0.0)
+dpf = st.number_input("Diabetes Pedigree Function", min_value=0.0)
+age = st.number_input("Age", min_value=0, step=1)
+
+# Predict button
+if st.button("Predict"):
+    # Prepare input
+    features = np.array([[pregnancies, glucose, blood_pressure,
+                          skin_thickness, insulin, bmi, dpf, age]])
+    
+    features_scaled = scaler_model.transform(features)
+
+    # Prediction
+    prediction = model.predict(features_scaled)[0]
+    probability = model.predict_proba(features_scaled)[0][1] * 100
+
+    # Risk classification
+    if probability >= 70:
+        risk = "High Risk"
+        st.error(" High Risk of Diabetes")
+    elif probability >= 40:
+        risk = "Moderate Risk"
+        st.warning(" Moderate Risk of Diabetes")
+    else:
+        risk = "Low Risk"
+        st.success(" Low Risk of Diabetes")
+
+    result = "Diabetic" if prediction == 1 else "Non-Diabetic"
+
+    # Display results
+    st.subheader("Prediction Result")
+    st.write(f"**Result:** {result}")
+    st.write(f"**Risk Level:** {risk}")
+    st.write(f"**Probability:** {probability:.2f}%")