Update app.py

Sudhanshu-Ambastha · Apr 2, 2024 · 109f129 · 109f129
1 parent c5fa552
commit 109f129
Showing 1 changed file with 5 additions and 5 deletions.
diff --git 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)
+        st.success(heart_diagnosis)