From 109f129d39a28e6712ce1a8fec2320e9a6307920 Mon Sep 17 00:00:00 2001 From: Sudhanshu Ambastha <135802131+Sudhanshu-Ambastha@users.noreply.github.com> Date: Tue, 2 Apr 2024 22:06:04 +0530 Subject: [PATCH] Update app.py --- Streamlit app/app.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/Streamlit app/app.py b/Streamlit app/app.py index b29e85c..c6e682f 100644 --- a/Streamlit app/app.py +++ b/Streamlit app/app.py @@ -50,8 +50,8 @@ def predict_diabetes(symptoms, features, svm_classifier, scaler): # Load data if selected == "🩸 Diabetes Prediction" or selected == "❤️ Heart Disease Prediction": # Load data for diabetes and heart disease prediction - diabetes_data = pd.read_csv('C:\\Users\\sudha\\OneDrive\\Documents\\GitHub\\combined-disease-prediction-test\\Streamlit app\\diabetes.csv') # Update with your diabetes dataset path - heart_disease_data = pd.read_csv('C:\\Users\\sudha\\OneDrive\\Documents\\GitHub\\combined-disease-prediction-test\\Streamlit app\\heart.csv') # Update with your heart disease dataset path + diabetes_data = pd.read_csv('C:\\Users\\sudha\\OneDrive\\Documents\\GitHub\\Poly-Disease-Predictor\\Streamlit app\\diabetes.csv') # Update with your diabetes dataset path + heart_disease_data = pd.read_csv('C:\\Users\\sudha\\OneDrive\\Documents\\GitHub\\Poly-Disease-Predictor\\Streamlit app\\heart.csv') # Update with your heart disease dataset path # Training SVM model for diabetes prediction X_diabetes = diabetes_data.drop(columns='Outcome', axis=1) @@ -78,8 +78,8 @@ def predict_diabetes(symptoms, features, svm_classifier, scaler): st.title("Multiple Disease Prediction using Symptoms") # Load data for multiple disease prediction - train_data = pd.read_csv('C:\\Users\\sudha\\OneDrive\\Documents\\GitHub\\combined-disease-prediction-test\\Streamlit app\\Training.csv') - test_data = pd.read_csv('C:\\Users\\sudha\\OneDrive\\Documents\\GitHub\\combined-disease-prediction-test\\Streamlit app\\Testing.csv') + train_data = pd.read_csv('C:\\Users\\sudha\\OneDrive\\Documents\\GitHub\\Poly-Disease-Predictor\\Streamlit app\\Training.csv') + test_data = pd.read_csv('C:\\Users\\sudha\\OneDrive\\Documents\\GitHub\\Poly-Disease-Predictor\\Streamlit app\\Testing.csv') # Split data into features and target variable features = train_data.drop('prognosis', axis=1) @@ -150,4 +150,4 @@ def predict_diabetes(symptoms, features, svm_classifier, scaler): [[age_heart, sex_heart, cp_heart, trestbps_heart, chol_heart, fbs_heart, restecg_heart, thalach_heart, exang_heart, oldpeak_heart, slope_heart, ca_heart, thal_heart]] ) heart_diagnosis = "The person is having heart disease" if heart_prediction[0] == 1 else "The person does not have any heart disease" - st.success(heart_diagnosis) \ No newline at end of file + st.success(heart_diagnosis)