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Procfile.txt

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+web: source setup.sh && python app.py

app.py

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+#importing data analysis libraries
+import numpy as np 
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+from sklearn.metrics import roc_curve, roc_auc_score
+from sklearn.metrics import confusion_matrix ,classification_report,precision_score, recall_score ,f1_score 
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression  
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.naive_bayes import GaussianNB
+from sklearn.svm import SVC 
+import warnings
+warnings.filterwarnings('ignore')
+
+#data is from https://www.kaggle.com/datasets/thedevastator/exploring-risk-factors-for-cardiovascular-diseas
+data = pd.read_csv('datasetforriskofcardiodisease.csv')
+data_num = data[['age','height','weight','ap_hi','ap_lo']]
+data_cat = data[['gender','cholesterol','gluc','smoke','alco','active']]
+xaxis = ['Age', 'Height', 'Weight', 'Systolic Blood Pressure', 'Diastolic Blood Pressure']
+
+for i, col in enumerate(data_num.columns):
+    plt.hist(data_num[col])
+    plt.title(f'Frequency vs. {xaxis[i]}')
+    plt.xlabel(xaxis[i])
+    plt.ylabel('Frequency')
+    #plt.show()
+   
+pd.pivot_table(data, index='cardio', values=['age','height','weight','ap_hi','ap_lo'])
+
+for i in data_cat.columns:
+    sns.barplot(x=data_cat[i].value_counts().index,y=data_cat[i].value_counts()).set_title(i)
+    #plt.show()
+
+#age and categorical variables  
+print(pd.pivot_table(data,index='cardio',columns='cholesterol', values='age'))
+print("="*100)
+print(pd.pivot_table(data,index='cardio',columns='gluc', values='age'))
+print("="*100)
+print(pd.pivot_table(data,index='cardio',columns='smoke', values='age'))
+print("="*100)
+print(pd.pivot_table(data,index='cardio',columns='alco', values='age'))
+print("="*100)
+print(pd.pivot_table(data,index='cardio',columns='active', values='age'))
+#ap_hi (systolic blood pressure) and categorical variables
+print(pd.pivot_table(data,index='cardio',columns='cholesterol', values='ap_hi'))
+print("="*100)
+print(pd.pivot_table(data,index='cardio',columns='gluc', values='ap_hi'))
+print("="*100)
+print(pd.pivot_table(data,index='cardio',columns='smoke', values='ap_hi'))
+print("="*100)
+print(pd.pivot_table(data,index='cardio',columns='alco', values='ap_hi'))
+print("="*100)
+print(pd.pivot_table(data,index='cardio',columns='active', values='ap_hi'))
+#ap_low (diastolic blood pressure) and categorical variables
+print(pd.pivot_table(data,index='cardio',columns='cholesterol', values='ap_lo'))
+print("="*100)
+print(pd.pivot_table(data,index='cardio',columns='gluc', values='ap_lo'))
+print("="*100)
+print(pd.pivot_table(data,index='cardio',columns='smoke', values='ap_lo'))
+print("="*100)
+print(pd.pivot_table(data,index='cardio',columns='alco', values='ap_lo'))
+print("="*100)
+print(pd.pivot_table(data,index='cardio',columns='active', values='ap_lo'))
+
+for i in data_num.columns:
+    sns.boxplot(data_num[i])
+    plt.title(i)
+    #plt.show()
+
+#Getting interquartile range
+def outlinefree(dataCol):
+    sorted(dataCol)    
+    Q1,Q3 = np.percentile(dataCol,[25,75])
+    IQR = Q3-Q1
+    LowerRange = Q1-(1.5 * IQR)
+    UpperRange = Q3+(1.5 * IQR)
+    return LowerRange,UpperRange
+
+#Removing outliers
+lwap_hi,upap_hi = outlinefree(data['ap_hi'])
+lwap_lo,upap_lo = outlinefree(data['ap_lo'])
+
+
+data['ap_hi'].replace(list(data[data['ap_hi'] > upap_hi].ap_hi) ,upap_hi,inplace=True)
+data['ap_lo'].replace(list(data[data['ap_lo'] > upap_lo].ap_lo) ,upap_lo,inplace=True)
+
+
+features = data.iloc[:,:-1].values
+label = data.iloc[:,-1].values
+
+#------------------------LogisticRegression-----------------------
+X_train, X_test, y_train, y_test= train_test_split(features,label, test_size= 0.25, random_state=102)
+
+classimodel= LogisticRegression()  
+classimodel.fit(X_train, y_train)
+trainscore =  classimodel.score(X_train,y_train)
+testscore =  classimodel.score(X_test,y_test)  
+print("Logistic Regression-----------------------------------------------------\n")
+print("test score: {} train score: {}".format(testscore,trainscore),'\n')
+
+y_pred =  classimodel.predict(X_test)
+
+#from sklearn.metrics import confusion_matrix
+confusion_matrix(y_test, y_pred)
+
+print(' f1 score: ',f1_score(y_test, y_pred),'\n')
+print(' precision score: ',precision_score(y_test, y_pred),'\n')
+print(' recall score: ',recall_score(y_test, y_pred),'\n')
+print(classification_report(y_test, y_pred))
+
+#--------------------------------------K-Nearest Neighbor(KNN)-----------------
+X_train, X_test, y_train, y_test= train_test_split(features,label, test_size= 0.25, random_state=193) 
+
+
+classifier= KNeighborsClassifier()  
+knnmodel =  classifier.fit(X_train, y_train) 
+
+trainscore =  knnmodel.score(X_train,y_train)
+testscore =  knnmodel.score(X_test,y_test)  
+print("KNN-----------------------------------------------------\n")
+print("test score: {} train score: {}".format(testscore,trainscore),'\n')
+
+y_predknn =  knnmodel.predict(X_test)
+
+print(confusion_matrix(y_test, y_predknn))
+
+print("f1_score: ",f1_score(y_test, y_predknn),'\n')
+print("precision_score: ",precision_score(y_test, y_predknn),'\n')
+print("recall_score: ",recall_score(y_test, y_predknn),'\n')
+print(classification_report(y_test, y_predknn))
+
+#------------------------------naive bayes---------------------------
+X_train, X_test, y_train, y_test= train_test_split(features,label, test_size= 0.25, random_state=34) 
+
+NBmodel = GaussianNB()  
+NBmodel.fit(X_train, y_train) 
+
+trainscore =  NBmodel.score(X_train,y_train)
+testscore =  NBmodel.score(X_test,y_test)  
+print("Naive Bayes-----------------------------------------------------\n")
+print("test score: {} train score: {}".format(testscore,trainscore),'\n')
+y_predNB =  NBmodel.predict(X_test)
+print(confusion_matrix(y_test, y_predNB))
+
+print("f1_score: ",f1_score(y_test, y_predNB),'\n')
+print("precision_score: ",precision_score(y_test, y_predNB),'\n')
+print("recall_score: ",recall_score(y_test, y_predNB),'\n')
+print(classification_report(y_test, y_predNB))
+
+
+#-------------------------------- XGBoost -------------------------------------  
+import xgboost as xgb
+from sklearn.metrics import mean_squared_error
+import pandas as pd
+import numpy as np
+
+X_train, X_test, y_train, y_test= train_test_split(features,label, test_size= 0.25, random_state=102)
+
+XGmodel= xgb.XGBRFClassifier()
+XGmodel.fit(X_train, y_train)
+trainscore =  XGmodel.score(X_train,y_train)
+testscore =  XGmodel.score(X_test,y_test)  
+print("XGBoost-----------------------------------------------------\n")
+print("test score: {} train score: {}".format(testscore,trainscore),'\n')
+
+y_predXG =  XGmodel.predict(X_test)
+
+confusion_matrix(y_test, y_pred)
+
+print("f1_score: ",f1_score(y_test, y_predXG),'\n')
+print("precision_score: ",precision_score(y_test, y_predXG),'\n')
+print("recall_score: ",recall_score(y_test, y_predXG),'\n')
+print(classification_report(y_test, y_predXG),'\n')
+print("AREA UNDER CURVES-----------------------------------------------------\n")
+#-------------------------------------- LogisticRegression -------------------------------------
+probabilityValues = classimodel.predict_proba(features)[:,1]
+#Calculate AUC
+auc = roc_auc_score(label,probabilityValues)
+print(auc)
+#Calculate roc_curve
+fpr,tpr, threshold =  roc_curve(label,probabilityValues)
+plt.plot([0,1],[0,1], linestyle = '--')
+plt.plot(fpr,tpr)
+
+#-------------------------------------- KNeighborsClassifier -------------------------------------
+probabilityValues = knnmodel.predict_proba(features)[:,1]
+#Calculate AUC
+auc = roc_auc_score(label,probabilityValues)
+print(auc)
+#Calculate roc_curve
+fpr,tpr, threshold =  roc_curve(label,probabilityValues)
+plt.plot([0,1],[0,1], linestyle = '--')
+plt.plot(fpr,tpr)
+
+#-------------------------------------- naive bayes -------------------------------------
+probabilityValues = NBmodel.predict_proba(features)[:,1]
+#Calculate AUC
+auc = roc_auc_score(label,probabilityValues)
+print(auc)
+#Calculate roc_curve
+fpr,tpr, threshold =  roc_curve(label,probabilityValues)
+plt.plot([0,1],[0,1], linestyle = '--')
+plt.plot(fpr,tpr)
+
+
+
+#-------------------------------------- XGBoost -------------------------------------
+probabilityValues = XGmodel.predict_proba(features)[:,1]
+#Calculate AUC
+auc = roc_auc_score(label,probabilityValues)
+print(auc)
+#Calculate roc_curve
+fpr,tpr, threshold =  roc_curve(label,probabilityValues)
+plt.plot([0,1],[0,1], linestyle = '--')
+plt.plot(fpr,tpr)
+'''
+#--------------------------------------INTERACE TIME LETS GO BOYS-----------------------
+from sklearn.feature_extraction.text import CountVectorizer
+import joblib
+import matplotlib
+matplotlib.use("agg")
+model_file_name = 'XG_best_model.joblib'
+model_folder = 'C:\\Users\\Ben Z\\Downloads\\Models\\'
+joblib.dump(XGmodel, model_folder+''+model_file_name)
+
+#Loading da model
+loaded_XG_model = joblib.load(open(model_folder+''+model_file_name, 'rb'))
+print (loaded_XG_model)
+def make_prediction(value1, checkbox1, value2, value3, value4, value5, value6, value7, checkbox3, checkbox4, checkbox5):
+    input_array = np.array([value1*365.25, checkbox1, value2, value3, value4, value5, value6, value7, checkbox3, checkbox4, checkbox5]).reshape(1, -1)
+    prediction = loaded_XG_model.predict(input_array)
+    info = '' 
+    if prediction[0] == 0:
+        info = "You are not currently at risk of a cardiovascular disease! ✅"
+    else:
+        info = "You are at risk of a cardiovascular disease. I would recommend going to the doctor however, take my advice with a grain of salt as I am an AI model capable of making mistakes. 🚨" 
+    final_info = "The prediction is: {}".format(info)
+    print (prediction[0])
+    return final_info
+
+input_values = [50.3572895, 1, 168, 62, 110, 80, 1, 1, 0, 0, 1]
+result = make_prediction(*input_values)
+print(result)
+
+#------------------------------------------------GRADIO Time lmfao
+import gradio as gr
+
+
+headline = "Cardiovascular Disease Risk Prediction Application"
+iface = gr.Interface(fn=make_prediction, inputs=
+    [gr.inputs.Number(label="Age (Years)"), 
+    gr.inputs.Checkbox(label="I am a male"),  
+    gr.inputs.Number(label="Height (cm)"),  
+    gr.inputs.Number(label="Weight (kg)"), 
+    gr.inputs.Number(label="Systolic Blood Pressure (mmHg)"),  
+    gr.inputs.Number(label="Diastolic Blood Pressure (mmHg)"), 
+    gr.inputs.Number(label="Cholesterol (per 20mg/dL)"),  
+    gr.inputs.Number(label="Glucose (per 1 mmol/L)"), 
+    gr.inputs.Checkbox(label="I have smoked."),
+    gr.inputs.Checkbox(label="I drink more alcohol than I should (>2 cups for men and >1 cup for women)."),  
+    gr.inputs.Checkbox(label="I am physically active.")  
+    ], outputs=gr.outputs.Textbox(label="Prediction Result"), title=headline, theme='soft')
+
+if __name__ == "__main__":
+    iface.launch(share=True)
+'''
+#--------------------------------------INTERACE TIME LETS GO BOYS-----------------------
+from sklearn.feature_extraction.text import CountVectorizer
+import joblib
+import matplotlib
+matplotlib.use("agg")
+model_file_name = 'XG_best_model.joblib'
+model_folder = 'C:\\Users\\Ben Z\\Downloads\\Models\\'
+joblib.dump(XGmodel, model_folder+''+model_file_name)
+
+#Loading da model
+loaded_XG_model = joblib.load(open(model_folder+''+model_file_name, 'rb'))
+print (loaded_XG_model)
+def make_prediction(value1, checkbox1, value2, value3, value4, value5, value6, value7, checkbox3, checkbox4, checkbox5):
+    checkbox1 = 1 if "Male" in checkbox1 else 0
+    input_array = np.array([value1*365.25, checkbox1, value2, value3, value4, value5, value6, value7, checkbox3, checkbox4, checkbox5]).reshape(1, -1)
+    prediction = loaded_XG_model.predict(input_array)
+    info = '' 
+    if prediction[0] == 0:
+        info = "You are not currently at risk of a cardiovascular disease! ✅"
+    else:
+        info = "You are at risk of a cardiovascular disease. I would recommend going to the doctor however, take my advice with a grain of salt as I am an AI model capable of making mistakes. 🚨" 
+    final_info = "The prediction is: {}".format(info)
+    return final_info
+
+#input_values = [50.3572895, 1, 168, 62, 110, 80, 1, 1, 0, 0, 1]
+#result = make_prediction(*input_values)
+#print(result)
+
+#------------------------------------------------GRADIO Time lmfao
+import gradio as gr
+
+
+headline = "Cardiovascular Disease Risk Prediction Application"
+iface = gr.Interface(fn=make_prediction, inputs=
+    [gr.inputs.Number(label="Age (Years)"), 
+    gr.inputs.CheckboxGroup(
+        label="Gender",
+        choices=["Male", "Female"],
+    ),  
+    gr.inputs.Number(label="Height (cm)"),  
+    gr.inputs.Number(label="Weight (kg)"), 
+    gr.inputs.Number(label="Systolic Blood Pressure (mmHg)"),  
+    gr.inputs.Number(label="Diastolic Blood Pressure (mmHg)"), 
+    gr.inputs.Number(label="Cholesterol (per 20mg/dL)"),  
+    gr.inputs.Number(label="Glucose (per 1 mmol/L)"), 
+    gr.inputs.Checkbox(label="I have smoked."),
+    gr.inputs.Checkbox(label="I drink more alcohol than I should (>2 cups for men and >1 cup for women)."),  
+    gr.inputs.Checkbox(label="I am physically active.")  
+    ], outputs=gr.outputs.Textbox(label="Prediction Result"), title=headline, theme='soft')
+
+if __name__ == "__main__":
+    iface.launch(share=False)

requirements.txt

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+numpy
+pandas
+seaborn
+matplotlib
+scikit-learn
+xgboost
+gradio
+joblib

setup.sh

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+export GRADIO_SERVER_NAME=0.0.0.0 
+export GRADIO_SERVER_PORT="$PORT"