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| import streamlit as st | |
| from joblib import load | |
| import numpy as np | |
| import os | |
| from sklearn.preprocessing import StandardScaler | |
| # Global variable for model and scaler | |
| diabetes_model = None | |
| scaler = None | |
| # Load the diabetes prediction model with error handling | |
| def load_model(): | |
| global diabetes_model, scaler | |
| try: | |
| diabetes_model = load('models/diabetes.sav') | |
| # Try to load scaler if it exists | |
| try: | |
| scaler = load('models/scaler.sav') | |
| except FileNotFoundError: | |
| st.warning("Scaler not found. Using raw input values.") | |
| scaler = None | |
| return True | |
| except FileNotFoundError: | |
| st.error("❌ Model file not found at 'models/diabetes.sav'. Please ensure the model file exists.") | |
| return False | |
| except Exception as e: | |
| st.error(f"❌ Error loading model: {str(e)}") | |
| return False | |
| # Input validation functions | |
| def validate_inputs(pregnancies, glucose, bloodpressure, skinthickness, insulin, bmi, diabetespedigree, age): | |
| errors = [] | |
| warnings = [] | |
| # Critical validations (errors) | |
| if glucose <= 0: | |
| errors.append("Glucose level must be greater than 0") | |
| elif glucose < 70: | |
| warnings.append("Glucose level seems low (normal fasting: 70-100 mg/dL)") | |
| elif glucose > 200: | |
| warnings.append("Glucose level is very high - please consult a doctor immediately") | |
| if bloodpressure <= 0: | |
| errors.append("Blood pressure must be greater than 0") | |
| elif bloodpressure < 60: | |
| warnings.append("Blood pressure seems low (normal systolic: 90-120 mmHg)") | |
| elif bloodpressure > 180: | |
| warnings.append("Blood pressure is critically high - seek immediate medical attention") | |
| if bmi <= 0: | |
| errors.append("BMI must be greater than 0") | |
| elif bmi < 16: | |
| warnings.append("BMI indicates severe underweight") | |
| elif bmi > 40: | |
| warnings.append("BMI indicates severe obesity") | |
| if age <= 0: | |
| errors.append("Age must be greater than 0") | |
| # Logical validations | |
| if pregnancies > 0 and age < 12: | |
| errors.append("Age seems too low for the number of pregnancies") | |
| if insulin > 0 and insulin < 10: | |
| warnings.append("Insulin level seems unusually low") | |
| elif insulin > 300: | |
| warnings.append("Insulin level is very high") | |
| if skinthickness > 50: | |
| warnings.append("Skin thickness measurement seems unusually high") | |
| if diabetespedigree > 1.5: | |
| warnings.append("Diabetes pedigree function value is quite high") | |
| return errors, warnings | |
| # Function to preprocess input data | |
| def preprocess_input(pregnancies, glucose, bloodpressure, skinthickness, insulin, bmi, diabetespedigree, age): | |
| try: | |
| # Create input array | |
| input_data = np.array([[pregnancies, glucose, bloodpressure, skinthickness, insulin, bmi, diabetespedigree, age]]) | |
| # Apply scaling if scaler is available | |
| if scaler is not None: | |
| input_data = scaler.transform(input_data) | |
| return input_data | |
| except Exception as e: | |
| st.error(f"Error preprocessing input data: {str(e)}") | |
| return None | |
| # Function to predict diabetes with probability | |
| def predict_diabetes_with_probability(pregnancies, glucose, bloodpressure, skinthickness, insulin, bmi, diabetespedigree, age): | |
| try: | |
| # Preprocess input | |
| input_data = preprocess_input(pregnancies, glucose, bloodpressure, skinthickness, insulin, bmi, diabetespedigree, age) | |
| if input_data is None: | |
| return None, None | |
| # Make prediction | |
| prediction = diabetes_model.predict(input_data) | |
| # Get probability if available | |
| try: | |
| probability = diabetes_model.predict_proba(input_data) | |
| prob_positive = probability[0][1] * 100 # Probability of positive class | |
| except AttributeError: | |
| # Model doesn't support predict_proba | |
| prob_positive = None | |
| return bool(prediction[0]), prob_positive | |
| except Exception as e: | |
| st.error(f"Error making prediction: {str(e)}") | |
| return None, None | |
| # Function to display logo with error handling | |
| def display_logo(): | |
| logo_path = "static/logo.png" | |
| if os.path.exists(logo_path): | |
| try: | |
| st.image(logo_path, use_column_width=True) | |
| except Exception as e: | |
| st.warning(f"Could not load logo: {str(e)}") | |
| else: | |
| # Display a simple text logo instead | |
| st.markdown(""" | |
| <div style='text-align: center; padding: 20px;'> | |
| <h2 style='color: #1f77b4;'>🩺 DIABETES PREDICTION</h2> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # Function to display results with enhanced formatting | |
| def display_results(name, prediction, probability, errors, warnings): | |
| if prediction is None: | |
| st.error("❌ Could not make prediction due to errors.") | |
| return | |
| # Display warnings if any | |
| if warnings: | |
| st.warning("⚠️ **Please note the following:**") | |
| for warning in warnings: | |
| st.warning(f"• {warning}") | |
| # Display result | |
| if prediction: | |
| st.error(f"🔴 **Hello {name}, your diabetes risk assessment is: POSITIVE**") | |
| if probability: | |
| st.error(f"**Risk Probability: {probability:.1f}%**") | |
| st.markdown(""" | |
| **⚠️ IMPORTANT:** This is a preliminary assessment. Please consult with a healthcare professional | |
| for proper medical diagnosis and treatment. | |
| """) | |
| else: | |
| st.success(f"✅ **Hello {name}, your diabetes risk assessment is: NEGATIVE**") | |
| if probability: | |
| st.success(f"**Risk Probability: {probability:.1f}%**") | |
| st.markdown(""" | |
| **ℹ️ NOTE:** This indicates lower risk based on the provided parameters. Continue maintaining | |
| a healthy lifestyle and regular medical check-ups. | |
| """) | |
| # Streamlit app | |
| def main(): | |
| # Set page config | |
| st.set_page_config( | |
| page_title="Diabetes Prediction App", | |
| page_icon="💉", | |
| layout="centered", | |
| initial_sidebar_state="expanded", | |
| ) | |
| # Load model | |
| if not load_model(): | |
| st.stop() | |
| # Header | |
| st.title("🩺 Diabetes Risk Assessment Tool") | |
| display_logo() | |
| st.markdown(""" | |
| This tool uses machine learning to assess diabetes risk based on clinical parameters. | |
| **Please note:** This is for educational purposes only and should not replace professional medical advice. | |
| """) | |
| # Input form | |
| st.sidebar.header("📋 Patient Information") | |
| # User details | |
| name = st.sidebar.text_input("👤 Full Name", placeholder="Enter patient name", help="Enter the patient's full name") | |
| if not name: | |
| st.info("👈 Please enter patient details in the sidebar to begin assessment.") | |
| return | |
| # Clinical details | |
| st.sidebar.subheader("🔬 Clinical Parameters") | |
| # Create two columns for better organization | |
| col1, col2 = st.sidebar.columns(2) | |
| with col1: | |
| pregnancies = st.number_input("🤱 Pregnancies", min_value=0, max_value=20, value=0, | |
| help="Number of times pregnant") | |
| glucose = st.number_input("🍯 Glucose (mg/dL)", min_value=0, max_value=300, value=120, | |
| help="Plasma glucose concentration (normal fasting: 70-100)") | |
| bloodpressure = st.number_input("💓 Blood Pressure (mmHg)", min_value=0, max_value=200, value=80, | |
| help="Diastolic blood pressure (normal: 60-80)") | |
| insulin = st.number_input("💉 Insulin (mu U/ml)", min_value=0, max_value=500, value=0, | |
| help="2-Hour serum insulin") | |
| with col2: | |
| bmi = st.number_input("⚖️ BMI", min_value=0.0, max_value=50.0, value=25.0, format="%.1f", | |
| help="Body mass index (normal: 18.5-24.9)") | |
| diabetespedigree = st.number_input("🧬 Diabetes Pedigree", min_value=0.0, max_value=2.0, value=0.5, format="%.3f", | |
| help="Diabetes pedigree function (family history)") | |
| age = st.number_input("📅 Age (years)", min_value=1, max_value=100, value=30, | |
| help="Age in years") | |
| skinthickness = st.number_input("📏 Skin Thickness (mm)", min_value=0, max_value=100, value=20, | |
| help="Triceps skin fold thickness") | |
| # Add reference ranges | |
| st.sidebar.markdown(""" | |
| **📊 Reference Ranges:** | |
| - Glucose (fasting): 70-100 mg/dL | |
| - Blood Pressure: 60-80 mmHg (diastolic) | |
| - BMI: 18.5-24.9 (normal) | |
| - Age: Consider diabetes risk increases with age | |
| """) | |
| # Prediction button | |
| if st.sidebar.button("🔍 Assess Diabetes Risk", key="predict_button"): | |
| # Validate inputs | |
| errors, warnings = validate_inputs(pregnancies, glucose, bloodpressure, skinthickness, | |
| insulin, bmi, diabetespedigree, age) | |
| if errors: | |
| st.error("❌ **Input Validation Errors:**") | |
| for error in errors: | |
| st.error(f"• {error}") | |
| st.info("Please correct the errors above and try again.") | |
| return | |
| # Make prediction | |
| with st.spinner("🔄 Analyzing patient data..."): | |
| prediction, probability = predict_diabetes_with_probability( | |
| pregnancies, glucose, bloodpressure, skinthickness, insulin, bmi, diabetespedigree, age | |
| ) | |
| # Display results | |
| display_results(name, prediction, probability, errors, warnings) | |
| # Display input summary | |
| with st.expander("📋 Input Summary"): | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.write(f"**Pregnancies:** {pregnancies}") | |
| st.write(f"**Glucose:** {glucose} mg/dL") | |
| st.write(f"**Blood Pressure:** {bloodpressure} mmHg") | |
| st.write(f"**Insulin:** {insulin} mu U/ml") | |
| with col2: | |
| st.write(f"**BMI:** {bmi:.1f}") | |
| st.write(f"**Diabetes Pedigree:** {diabetespedigree:.3f}") | |
| st.write(f"**Age:** {age} years") | |
| st.write(f"**Skin Thickness:** {skinthickness} mm") | |
| # Footer | |
| st.sidebar.markdown("---") | |
| st.sidebar.markdown(""" | |
| **⚠️ Medical Disclaimer:** | |
| This tool is for educational purposes only. | |
| Always consult healthcare professionals for medical advice. | |
| **📞 Emergency:** If you have severe symptoms, seek immediate medical attention. | |
| © 2024 Diabetes Prediction App | |
| """) | |
| # Additional information in main area | |
| if name: | |
| st.markdown("---") | |
| st.subheader("📚 About Diabetes Risk Factors") | |
| tab1, tab2, tab3 = st.tabs(["🔍 Risk Factors", "📊 Understanding Results", "🏥 Next Steps"]) | |
| with tab1: | |
| st.markdown(""" | |
| **Key Risk Factors for Diabetes:** | |
| - **Age:** Risk increases with age, especially after 45 | |
| - **BMI:** Higher BMI indicates increased risk | |
| - **Family History:** Genetic predisposition (pedigree function) | |
| - **Blood Glucose:** Elevated glucose levels | |
| - **Blood Pressure:** Hypertension is linked to diabetes risk | |
| - **Previous Pregnancies:** Gestational diabetes history | |
| """) | |
| with tab2: | |
| st.markdown(""" | |
| **Understanding Your Results:** | |
| - **NEGATIVE:** Lower risk based on current parameters | |
| - **POSITIVE:** Higher risk - requires medical evaluation | |
| - **Probability Score:** When available, shows percentage risk | |
| **Important:** This is a screening tool, not a diagnosis. | |
| """) | |
| with tab3: | |
| st.markdown(""" | |
| **Recommended Next Steps:** | |
| **If Result is POSITIVE:** | |
| - 🏥 Schedule appointment with healthcare provider | |
| - 🩸 Request proper diagnostic tests (HbA1c, OGTT) | |
| - 📝 Discuss family history and symptoms | |
| **If Result is NEGATIVE:** | |
| - 🔄 Continue regular health check-ups | |
| - 🥗 Maintain healthy diet and exercise | |
| - 📅 Rescreen annually if risk factors present | |
| """) | |
| # Run the Streamlit app | |
| if __name__ == "__main__": | |
| main() |