First commit. Files added

app.py

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+import streamlit as st
+import pandas as pd
+import altair as alt
+import numpy as np
+from PIL import Image
+import pickle
+
+with open('best_model_hypertension.pkl', 'rb') as file:
+    hypertension_model = pickle.load(file)
+
+with open('hypertension_scaler.pkl', 'rb') as file:
+    hypertension_scaler = pickle.load(file)
+
+with open('best_model_stroke.pkl', 'rb') as file:
+    stroke_model = pickle.load(file)
+
+with open('stroke_scaler.pkl', 'rb') as file:
+    stroke_scaler = pickle.load(file)
+
+with open('best_model_diabetes.pkl', 'rb') as file:
+    diabetes_model = pickle.load(file)
+
+with open('diabetes_scaler.pkl', 'rb') as file:
+    diabetes_scaler = pickle.load(file)
+
+with open('best_model_heart.pkl', 'rb') as file:
+    heart_model = pickle.load(file)
+
+with open('best_scaler_heart.pkl', 'rb') as file:
+    heart_scaler = pickle.load(file)
+
+#Create header
+st.write("""# Heart Attack Risk Predictor""")
+st.write("""## How it works""")
+st.write("view your predictions about your health condition based on your answers to the questions on the side panel.")
+
+#image
+st.write("""## Training Flow Diagram""")
+image = Image.open('Train_diag.png')
+st.image(image)
+
+st.write("""## Prediction Flow Diagram""")
+image = Image.open('Test_diag_v2.png')
+st.image(image)
+
+
+#links
+st.write("""## Dataset links""")
+
+st.write("https://www.kaggle.com/datasets/prosperchuks/health-dataset?select=diabetes_data.csv")
+st.write("https://www.kaggle.com/datasets/iamsouravbanerjee/heart-attack-prediction-dataset")
+
+# model types
+st.write("""## Trained Model Types""")
+st.write("Hypertension: ")
+st.write("Stroke: ")
+st.write("Diabetes: ")
+st.write("Heart Attack: ")
+
+#Bring in the data
+data = pd.read_csv('heart_attack_prediction_dataset.csv')
+st.write("## HEART ATTACK TRAIN DATA")
+st.dataframe(data)
+
+#Create and name sidebar
+st.sidebar.header('Fill your survey')
+
+st.sidebar.write("""#### Choose your values""")
+def user_input_features():
+    age = st.sidebar.slider('Age', 18, 100, 25, 1)
+    sex = st.sidebar.slider('Sex (Male : 0, Female : 1)', 0, 1, 0, 1)
+    gen_health = st.sidebar.slider('General Health scale 1 = excellent ,2 = very good, 3 = good, 4 = fair, 5 = poor', 1, 5, 3, 1)
+    men_health = st.sidebar.slider('days of poor mental health scale 1-30 days', 0, 30, 0, 1)
+    cholesterol = st.sidebar.slider('Cholesterol level', 0, 600, 150, 5)
+    heart_rate = st.sidebar.slider('Heart Rate', 0, 160, 60, 1)
+    family_history = st.sidebar.slider('Family History(for heart attack). 0 = no,1 = yes', 0, 1, 0, 1)
+    obesity = st.sidebar.slider('Obesity. 0 = no,1 = yes', 0, 1, 0, 1)
+    alcohol_consumption = st.sidebar.slider('Alcohol Consumption(regularly).0 = no,1 = yes', 0, 1, 0, 1)
+    smoking_status = st.sidebar.slider('Smoking(regularly).0 = no,1 = yes', 0, 1, 0, 1)
+    exercise_hours = st.sidebar.slider('Exercise Hours Per Week', 0, 50, 15, 1)
+    stress_level = st.sidebar.slider('Stress Level', 0, 10, 3, 1)
+    sedentary_hours = st.sidebar.slider('Sedentary Hours Per Day', 0.0, 12.0, 6.0, 0.5)
+    income = st.sidebar.slider('Income', 0, 500000, 0, 1000)
+    education_level = st.sidebar.slider('Education level 1-10', 1, 10, 6, 1)
+    bmi = st.sidebar.slider('BMI', 0.0, 50.0, 20.0, 0.1)
+    triglycerides = st.sidebar.slider('Triglycerides Level', 0, 1000, 350, 10)
+    physical_days = st.sidebar.slider('Physical Activity Days Per Week', 0, 7, 3, 1)
+    sleep_hours = st.sidebar.slider('Sleep Hours Per Day', 0.0, 16.0, 8.0, 0.5)
+    systolic = st.sidebar.slider('Blood Pressure (Systolic)', 0, 200, 140, 1)
+    diastolic = st.sidebar.slider('Blood Pressure (Diastolic)', 0, 120, 80, 1)
+    diff_walk = st.sidebar.slider('Do you have serious difficulty walking or climbing stairs? 0 = no 1 = yes', 0, 1, 0, 1)
+    fruits = st.sidebar.slider('Consume Fruit 1 or more times per day. 0 = no,1 = yes',  0, 1, 0, 1)
+    veggies = st.sidebar.slider('Consume Vegetables 1 or more times per day. 0 = no ,1 = yes',  0, 1, 0, 1)
+    married = st.sidebar.slider('Ever Married. 0 = no,1 = yes',  0, 1, 0, 1)
+    work_type = st.sidebar.slider('patient job type: 0 - Never_worked, 1 - children, 2 - Govt_job, 3 - Self-employed, 4 - Private', 0, 4, 0, 1)
+    avg_glucose_level = st.sidebar.slider('Avg. glucose level',  0, 300, 100, 5)
+    cp = st.sidebar.slider('Chest pain type: 0: asymptomatic 1: typical angina 2: atypical angina 3: non-anginal pain', 0, 3, 0, 1)
+    trestbps = st.sidebar.slider('Resting blood pressure', 50, 250, 120, 1)
+    thalach = st.sidebar.slider('Maximum heart rate achieved', 50, 250, 120, 1)
+    exang = st.sidebar.slider('Exercise induced angina. 0 = no,1 = yes', 0, 1, 0, 1)
+    oldpeak = st.sidebar.slider('ST depression induced by exercise relative to rest.', 0.0, 10.0, 0.0, 0.1)
+    slope = st.sidebar.slider('The slope of the peak exercise ST segment: 0: upsloping 1: flat 2: downsloping', 0, 2, 2, 1)
+    ca = st.sidebar.slider('Number of major vessels (0–3) colored by flourosopy', 0, 5, 0, 1)
+    thal = st.sidebar.slider('3: Normal; 6: Fixed defect; 7: Reversable defect', 0, 10, 2, 1)
+
+    user_data_hypertension = {
+        'cp' : cp,
+        'trestbps' : trestbps,
+        'chol' : cholesterol,
+        'thalach' : thalach,
+        'exang' : exang,
+        'oldpeak' : oldpeak,
+        'slope' : slope,
+        'ca' : ca,
+        'thal' : thal,
+    }
+
+    features_hypertension = pd.DataFrame(user_data_hypertension, index=[0])
+    features_hypertension_scaled = hypertension_scaler.transform(features_hypertension)
+    pred_hypertension = hypertension_model.predict(features_hypertension_scaled)
+
+    user_data_stroke = {
+        'age' : age,
+        'hypertension' : pred_hypertension[0],
+        'heart_disease' : 0,
+        'ever_married' : married,
+        'work_type' : work_type,
+        'avg_glucose_level' : avg_glucose_level,
+        'bmi' : bmi,
+        'smoking_status' : smoking_status
+    }
+
+    features_stroke = pd.DataFrame(user_data_stroke, index=[0])
+    features_stroke_scaled = stroke_scaler.transform(features_stroke)
+    pred_stroke = stroke_model.predict(features_stroke_scaled)
+
+    if physical_days > 2:
+        PhysHlth = 1
+    else:
+        PhysHlth = 0
+
+    if exercise_hours > 8:
+        PhysActivity = 1
+    else:
+        PhysActivity = 0
+
+    age_level = ((age -18) // 5 ) + 1
+    income_level  = (income // 50000 ) + 1
+
+    user_data_diabetes = {
+        'HighBP': pred_hypertension[0],
+        'BMI': bmi,
+        'Stroke': pred_stroke[0],
+        'PhysActivity': PhysActivity,
+        'Fruits': fruits,
+        'Veggies': veggies,
+        'HvyAlcoholConsump': alcohol_consumption,
+        'GenHlth': gen_health,
+        'MentHlth': men_health,
+        'PhysHlth': PhysHlth,
+        'DiffWalk': diff_walk,
+        'Sex': 1 - sex,
+        'Age': age_level,
+        'Education': education_level,
+        'Income': income_level
+    }
+
+    features_diabetes = pd.DataFrame(user_data_diabetes, index=[0])
+    features_diabetes_scaled = diabetes_scaler.transform(features_diabetes)
+    pred_diabetes = diabetes_model.predict(features_diabetes_scaled)
+
+    user_data_heart_attack ={
+        'Age': age,
+        'Cholesterol': cholesterol,
+        'Heart Rate': heart_rate,
+        'Diabetes': pred_diabetes[0],
+        'Family History': family_history,
+        'Obesity': obesity,
+        'Alcohol Consumption': alcohol_consumption,
+        'Exercise Hours Per Week' : exercise_hours,
+        'Stress Level': stress_level,
+        'Sedentary Hours Per Day': sedentary_hours,
+        'Income': income,
+        'BMI': bmi,
+        'Triglycerides': triglycerides,
+        'Physical Activity Days Per Week': physical_days,
+        'Sleep Hours Per Day': sleep_hours,
+        'BP_Systolic': systolic,
+        'BP_Diastolic': diastolic,
+        'Sex_Female': sex,
+        'Sex_Male': 1 - sex,
+    }
+
+    features_heart_attack = pd.DataFrame(user_data_heart_attack, index=[0])
+    features_heart_attack_scaled = heart_scaler.transform(features_heart_attack)
+    pred_heart = heart_model.predict(features_heart_attack_scaled)
+
+    return features_stroke,pred_stroke, features_hypertension, pred_hypertension, features_diabetes, pred_diabetes, features_heart_attack, pred_heart
+
+df_stroke, pred_stroke,df_hypertension, pred_hypertension,df_diabetes, pred_diabetes, df_heart_attack, pred_heart = user_input_features()
+
+
+
+st.write("## YOUR PREDICTIONS: ")
+
+st.write("## Hypertension User Input: ")
+df_hypertension
+st.write("Predicted Hypertension: ")
+pred_hypertension
+st.write("## Stroke User Input and Hypertension(pred. vals added): ")
+df_stroke
+st.write("Predicted Stroke: ")
+pred_stroke
+st.write("## Diabetes User Input and Hypertension and Stroke(pred. vals added): ")
+df_diabetes
+st.write("Predicted Diabetes: ")
+pred_diabetes
+st.write("## Heart Attack User Input and Diabetes: ")
+df_heart_attack
+st.write("Predicted Heart Attack: ")
+pred_heart
+
+
+
+

best_model_diabetes.pkl

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+oid sha256:eb09ea1f5e97f6b3c261c242a8c01aae51afff3d9a84e81fff1f4afc540c75f6
+size 564133

best_model_heart.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:62ba22900ee91ff62878b441bcd30a600336247a685cd481d606a1185dd8d159
+size 42962

best_model_hypertension.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4316f4bf985eb9e4d116ab34b0da8d914d0c511f1e056eb47019bd5babd0266c
+size 9828

best_model_stroke.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1b72e110aec7aaa86181885d32105cdcf3e6fc5cf6129f6bd295411333ca25b5
+size 21252750

best_scaler_heart.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a75ff4113afd04e45b215f9410f7c2c01130408e5dc51d91d1401a3960dd537a
+size 1292

diabetes_scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:25af59d1cc0b07383b0e61892b5343da0a52e263b86e12eb1c7c17acc865021f
+size 1050

hypertension_scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:21b155a0a1fafbd177e98d4d4aeabf4e4b2f82a0e684b099e41fe2e080b1119b
+size 823

requirements.txt

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+streamlit~=1.32.0
+pandas~=2.0.3
+altair~=5.0.1
+numpy~=1.24.3
+pillow~=10.3.0
+scikit-learn==1.4.2
+xgboost==2.0.3

stroke_scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8f3809aed07f1a21066d7599707894d216e11e69544c8002332a4ce42a2c56b4
+size 835