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| import pickle | |
| import streamlit as st | |
| from streamlit_option_menu import option_menu | |
| # Load the saved models | |
| def load_models(): | |
| try: | |
| diabetes_model = pickle.load(open('Saved Models/diabetes_model.sav', 'rb')) | |
| heart_disease_model = pickle.load(open('Saved Models/heart_disease_model.sav', 'rb')) | |
| parkinsons_model = pickle.load(open('Saved Models/parkinsons_model.sav', 'rb')) | |
| return diabetes_model, heart_disease_model, parkinsons_model | |
| except Exception as e: | |
| st.error(f"Error loading models: {e}") | |
| return None, None, None | |
| # Function to get user input for diabetes prediction | |
| def get_diabetes_input(): | |
| col1, col2, col3 = st.columns(3) | |
| try: | |
| with col1: | |
| Pregnancies = int(st.text_input('Number of Pregnancies', '0')) | |
| with col2: | |
| Glucose = float(st.text_input('Glucose Level', '0')) | |
| with col3: | |
| BloodPressure = float(st.text_input('Blood Pressure value', '0')) | |
| with col1: | |
| SkinThickness = float(st.text_input('Skin Thickness value', '0')) | |
| with col2: | |
| Insulin = float(st.text_input('Insulin Level', '0')) | |
| with col3: | |
| BMI = float(st.text_input('BMI value', '0')) | |
| with col1: | |
| DiabetesPedigreeFunction = float(st.text_input('Diabetes Pedigree Function value', '0')) | |
| with col2: | |
| Age = int(st.text_input('Age of the Person', '0')) | |
| return Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age | |
| except ValueError: | |
| st.error("Please enter valid numbers for all fields.") | |
| return None | |
| # Function to get user input for heart disease prediction | |
| def get_heart_disease_input(): | |
| col1, col2, col3 = st.columns(3) | |
| try: | |
| with col1: | |
| age = int(st.text_input('Age', '0')) | |
| with col2: | |
| sex = int(st.text_input('Sex (1 = male, 0 = female)', '0')) | |
| with col3: | |
| cp = int(st.text_input('Chest Pain types (0-3)', '0')) | |
| with col1: | |
| trestbps = float(st.text_input('Resting Blood Pressure', '0')) | |
| with col2: | |
| chol = float(st.text_input('Serum Cholestoral in mg/dl', '0')) | |
| with col3: | |
| fbs = int(st.text_input('Fasting Blood Sugar > 120 mg/dl (1 = true; 0 = false)', '0')) | |
| with col1: | |
| restecg = int(st.text_input('Resting Electrocardiographic results (0-2)', '0')) | |
| with col2: | |
| thalach = float(st.text_input('Maximum Heart Rate achieved', '0')) | |
| with col3: | |
| exang = int(st.text_input('Exercise Induced Angina (1 = yes; 0 = no)', '0')) | |
| with col1: | |
| oldpeak = float(st.text_input('ST depression induced by exercise', '0')) | |
| with col2: | |
| slope = int(st.text_input('Slope of the peak exercise ST segment (0-2)', '0')) | |
| with col3: | |
| ca = int(st.text_input('Major vessels colored by flourosopy (0-3)', '0')) | |
| with col1: | |
| thal = int(st.text_input('Thalassemia (1 = normal; 2 = fixed defect; 3 = reversible defect)', '1')) | |
| return age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal | |
| except ValueError: | |
| st.error("Please enter valid numbers for all fields.") | |
| return None | |
| # Function to get user input for Parkinson's prediction | |
| def get_parkinsons_input(): | |
| col1, col2, col3, col4, col5 = st.columns(5) | |
| try: | |
| with col1: | |
| fo = float(st.text_input('MDVP:Fo(Hz)', '0')) | |
| with col2: | |
| fhi = float(st.text_input('MDVP:Fhi(Hz)', '0')) | |
| with col3: | |
| flo = float(st.text_input('MDVP:Flo(Hz)', '0')) | |
| with col4: | |
| Jitter_percent = float(st.text_input('MDVP:Jitter(%)', '0')) | |
| with col5: | |
| Jitter_Abs = float(st.text_input('MDVP:Jitter(Abs)', '0')) | |
| with col1: | |
| RAP = float(st.text_input('MDVP:RAP', '0')) | |
| with col2: | |
| PPQ = float(st.text_input('MDVP:PPQ', '0')) | |
| with col3: | |
| DDP = float(st.text_input('Jitter:DDP', '0')) | |
| with col4: | |
| Shimmer = float(st.text_input('MDVP:Shimmer', '0')) | |
| with col5: | |
| Shimmer_dB = float(st.text_input('MDVP:Shimmer(dB)', '0')) | |
| with col1: | |
| APQ3 = float(st.text_input('Shimmer:APQ3', '0')) | |
| with col2: | |
| APQ5 = float(st.text_input('Shimmer:APQ5', '0')) | |
| with col3: | |
| APQ = float(st.text_input('MDVP:APQ', '0')) | |
| with col4: | |
| DDA = float(st.text_input('Shimmer:DDA', '0')) | |
| with col5: | |
| NHR = float(st.text_input('NHR', '0')) | |
| with col1: | |
| HNR = float(st.text_input('HNR', '0')) | |
| with col2: | |
| RPDE = float(st.text_input('RPDE', '0')) | |
| with col3: | |
| DFA = float(st.text_input('DFA', '0')) | |
| with col4: | |
| spread1 = float(st.text_input('spread1', '0')) | |
| with col5: | |
| spread2 = float(st.text_input('spread2', '0')) | |
| with col1: | |
| D2 = float(st.text_input('D2', '0')) | |
| with col2: | |
| PPE = float(st.text_input('PPE', '0')) | |
| return [fo, fhi, flo, Jitter_percent, Jitter_Abs, RAP, PPQ, DDP, Shimmer, Shimmer_dB, APQ3, APQ5, APQ, DDA, NHR, HNR, RPDE, DFA, spread1, spread2, D2, PPE] | |
| except ValueError: | |
| st.error("Please enter valid numbers for all fields.") | |
| return None | |
| # Prediction function | |
| def make_prediction(model, input_data): | |
| try: | |
| prediction = model.predict([input_data]) | |
| return prediction[0] | |
| except Exception as e: | |
| st.error(f"Error making prediction: {e}") | |
| return None | |
| # Main function | |
| def main(): | |
| # Load models | |
| diabetes_model, heart_disease_model, parkinsons_model = load_models() | |
| if not diabetes_model or not heart_disease_model or not parkinsons_model: | |
| return | |
| # Sidebar for navigation | |
| with st.sidebar: | |
| selected = option_menu('Multiple Disease Prediction System', | |
| ['Diabetes Prediction', 'Heart Disease Prediction', 'Parkinsons Prediction'], | |
| icons=['activity', 'heart', 'person'], | |
| default_index=0) | |
| # Diabetes Prediction | |
| if selected == 'Diabetes Prediction': | |
| st.title('Diabetes Prediction using ML') | |
| input_data = get_diabetes_input() | |
| if input_data and st.button('Diabetes Test Result'): | |
| result = make_prediction(diabetes_model, input_data) | |
| if result == 1: | |
| st.success('The person is diabetic') | |
| else: | |
| st.success('The person is not diabetic') | |
| # Heart Disease Prediction | |
| elif selected == 'Heart Disease Prediction': | |
| st.title('Heart Disease Prediction using ML') | |
| input_data = get_heart_disease_input() | |
| if input_data and st.button('Heart Disease Test Result'): | |
| result = make_prediction(heart_disease_model, input_data) | |
| if result == 1: | |
| st.success('The person is having heart disease') | |
| else: | |
| st.success('The person does not have any heart disease') | |
| # Parkinson's Prediction | |
| elif selected == "Parkinsons Prediction": | |
| st.title("Parkinson's Disease Prediction using ML") | |
| input_data = get_parkinsons_input() | |
| if input_data and st.button("Parkinson's Test Result"): | |
| result = make_prediction(parkinsons_model, input_data) | |
| if result == 1: | |
| st.success("The person has Parkinson's disease") | |
| else: | |
| st.success("The person does not have Parkinson's disease") | |
| if __name__ == '__main__': | |
| main() | |