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Procfile

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+web: sh setup.sh && streamlit run Fatal_Health.py

app.py

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+import streamlit as st
+
+st.set_page_config(layout="wide", page_icon=":hospital:")
+st.set_option('deprecation.showPyplotGlobalUse', False)
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import time
+import matplotlib.pyplot as plt
+
+plt.style.use('fivethirtyeight')
+plt.style.use('default')
+
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.svm import SVC
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.ensemble import GradientBoostingClassifier
+from xgboost import XGBClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler, LabelEncoder, StandardScaler
+from sklearn.metrics import precision_recall_fscore_support as score, mean_squared_error
+from sklearn.metrics import confusion_matrix, accuracy_score
+from sklearn.decomposition import PCA
+
+########################################################################################################################
+start_time = time.time()
+# Title for the webpage
+tit1, tit2 = st.beta_columns((4, 1))
+tit1.markdown("<h1 style='text-align: center;'><u>Activity/ Pain Prediction With Wearable Technology Data</u> </h1>",
+              unsafe_allow_html=True)
+st.sidebar.title("Dataset and ML Classifiers")
+
+dataset_select = st.sidebar.selectbox("Select Dataset: ", ('AppleWatch Data', "Fitbit Data"))
+classifier_select = st.sidebar.selectbox("Select ML Classifier: ",
+                                         ("Logistic Regression", "KNN", "SVM", "Decision Trees",
+                                          "Random Forest", "Gradient Boosting", "XGBoost"))
+
+LE = LabelEncoder()
+
+
+def get_dataset(dataset_select):
+    if dataset_select == "AppleWatch Data":
+        data = pd.read_csv(
+            "https://raw.githubusercontent.com/ajinkyalahade/PainPredictionProject/main/Data/data_applewatch.csv")
+        st.header("Activity Data Apple Watch")
+        return data
+
+    else:
+        data = pd.read_csv(
+            "https://raw.githubusercontent.com/ajinkyalahade/PainPredictionProject/main/Data/data_fitbit.csv")
+        st.header("Activity Data Fitbit Watch")
+        return data
+
+
+data = get_dataset(dataset_select)
+
+
+def selected_dataset(dataset_select):
+    if dataset_select == "AppleWatch Data":
+        X = data.drop(["activitytag"], axis=1)
+        Y = data.activitytag
+        return X, Y
+    elif dataset_select == "Fitbit Data":
+        X = data.drop(["tag"], axis=1)
+        Y = data.tag
+        return X, Y
+
+
+X, Y = selected_dataset(dataset_select)
+
+
+# Charts
+def plot_op(dataset_select):
+    col1, col2 = st.beta_columns((1, 5))
+    plt.figure(figsize=(12, 3))
+    plt.title("Classes in 'Y'")
+    if dataset_select == "AppleWatch Data":
+        col1.write(Y)
+        sns.countplot(Y, palette='colorblind')
+        col2.pyplot()
+
+    elif dataset_select == "Fitbit Data":
+        col1.write(Y)
+        sns.countplot(Y, palette='colorblind')
+        col2.pyplot()
+
+
+########################################################################################################################
+
+st.write(data)
+st.write("Shape of dataset: ", data.shape)
+st.write("Number of classes: ", Y.nunique())
+plot_op(dataset_select)
+
+
+########################################################################################################################
+
+def add_parameter_ui(clf_name):
+    params = {}
+    st.sidebar.write("Select Parameters: ")
+
+    if clf_name == "Logistic Regression":
+        R = st.sidebar.slider("Regularization", 0.1, 10.0, step=0.1)
+        MI = st.sidebar.slider("max_iter", 50, 400, step=50)
+        params["R"] = R
+        params["MI"] = MI
+
+    elif clf_name == "KNN":
+        K = st.sidebar.slider("n_neighbors", 1, 20)
+        params["K"] = K
+
+    elif clf_name == "SVM":
+        C = st.sidebar.slider("Regularization", 0.01, 10.0, step=0.01)
+        kernel = st.sidebar.selectbox("Kernel", ("linear", "poly", "rbf", "sigmoid", "precomputed"))
+        params["C"] = C
+        params["kernel"] = kernel
+
+    elif clf_name == "Decision Trees":
+        M = st.sidebar.slider("max_depth", 2, 20)
+        C = st.sidebar.selectbox("Criterion", ("gini", "entropy"))
+        SS = st.sidebar.slider("min_samples_split", 1, 10)
+        params["M"] = M
+        params["C"] = C
+        params["SS"] = SS
+
+    elif clf_name == "Random Forest":
+        N = st.sidebar.slider("n_estimators", 50, 500, step=50, value=100)
+        M = st.sidebar.slider("max_depth", 2, 20)
+        C = st.sidebar.selectbox("Criterion", ("gini", "entropy"))
+        params["N"] = N
+        params["M"] = M
+        params["C"] = C
+
+    elif clf_name == "Gradient Boosting":
+        N = st.sidebar.slider("n_estimators", 50, 500, step=50, value=100)
+        LR = st.sidebar.slider("Learning Rate", 0.01, 0.5)
+        L = st.sidebar.selectbox("Loss", ('deviance', 'exponential'))
+        M = st.sidebar.slider("max_depth", 2, 20)
+        params["N"] = N
+        params["LR"] = LR
+        params["L"] = L
+        params["M"] = M
+
+    elif clf_name == "XGBoost":
+        N = st.sidebar.slider("n_estimators", 50, 500, step=50, value=50)
+        LR = st.sidebar.slider("Learning Rate", 0.01, 0.5, value=0.1)
+        O = st.sidebar.selectbox("Objective", ('binary:logistic', 'reg:logistic', 'reg:squarederror', "reg:gamma"))
+        M = st.sidebar.slider("max_depth", 1, 20, value=6)
+        G = st.sidebar.slider("Gamma", 0, 10, value=5)
+        L = st.sidebar.slider("reg_lambda", 1.0, 5.0, step=0.1)
+        A = st.sidebar.slider("reg_alpha", 0.0, 5.0, step=0.1)
+        CS = st.sidebar.slider("colsample_bytree", 0.5, 1.0, step=0.1)
+        params["N"] = N
+        params["LR"] = LR
+        params["O"] = O
+        params["M"] = M
+        params["G"] = G
+        params["L"] = L
+        params["A"] = A
+        params["CS"] = CS
+
+    RS = st.sidebar.slider("Random State", 0, 100)
+    params["RS"] = RS
+    return params
+
+
+params = add_parameter_ui(classifier_select)
+
+
+# get classifier by selections above
+def get_classifier(clf_name, params):
+    global clf
+    if clf_name == "Logistic Regression":
+        clf = LogisticRegression(C=params["R"], max_iter=params["MI"])
+
+    elif clf_name == "KNN":
+        clf = KNeighborsClassifier(n_neighbors=params["K"])
+
+    elif clf_name == "SVM":
+        clf = SVC(kernel=params["kernel"], C=params["C"])
+
+    elif clf_name == "Decision Trees":
+        clf = DecisionTreeClassifier(max_depth=params["M"], criterion=params["C"], min_impurity_split=params["SS"])
+
+    elif clf_name == "Random Forest":
+        clf = RandomForestClassifier(n_estimators=params["N"], max_depth=params["M"], criterion=params["C"])
+
+    elif clf_name == "Gradient Boosting":
+        clf = GradientBoostingClassifier(n_estimators=params["N"], learning_rate=params["LR"], loss=params["L"],
+                                         max_depth=params["M"])
+
+    elif clf_name == "XGBoost":
+        clf = XGBClassifier(booster="gbtree", n_estimators=params["N"], max_depth=params["M"],
+                            learning_rate=params["LR"],
+                            objective=params["O"], gamma=params["G"], reg_alpha=params["A"], reg_lambda=params["L"],
+                            colsample_bytree=params["CS"])
+
+    return clf
+
+
+clf = get_classifier(classifier_select, params)
+
+
+########################################################################################################################
+# get model trained
+def model():
+    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.33, random_state=42)
+
+    # MinMax Scaling / Normalization of data
+    Std_scaler = StandardScaler()
+    X_train = Std_scaler.fit_transform(X_train)
+    X_test = Std_scaler.transform(X_test)
+
+    clf.fit(X_train, Y_train)
+    Y_pred = clf.predict(X_test)
+    acc = accuracy_score(Y_test, Y_pred)
+
+    return Y_pred, Y_test
+
+
+Y_pred, Y_test = model()
+
+
+########################################################################################################################
+# Plot Output
+def compute(Y_pred, Y_test):
+    # Plot PCA
+    pca = PCA(2)
+    X_projected = pca.fit_transform(X)
+    x1 = X_projected[:, 0]
+    x2 = X_projected[:, 1]
+    plt.figure(figsize=(16, 8))
+    plt.scatter(x1, x2, c=Y, alpha=0.8, cmap="cividis")
+    plt.xlabel("Principal Component 1")
+    plt.ylabel("Principal Component 2")
+    plt.colorbar()
+    st.pyplot()
+
+    c1, c2 = st.beta_columns((4, 3))
+    # Output plot
+    plt.figure(figsize=(12, 6))
+    plt.scatter(range(len(Y_pred)), Y_pred, color="blue", lw=5, label="Predictions")
+    plt.scatter(range(len(Y_test)), Y_test, color="red", label="Actual")
+    plt.title("Prediction Values vs Real Values")
+    plt.legend()
+    plt.grid(True)
+    c1.pyplot()
+
+    # Confusion Matrix
+    cm = confusion_matrix(Y_test, Y_pred)
+    class_label = ["High-Pain-risk", "Low-Pain-risk"]
+    df_cm = pd.DataFrame(cm, index=class_label, columns=class_label)
+    plt.figure(figsize=(12, 7.5))
+    sns.heatmap(df_cm, annot=True, cmap='Set1', linewidths=2, fmt='d')
+    plt.title("Confusion Matrix", fontsize=15)
+    plt.xlabel("Predicted")
+    plt.ylabel("True")
+    c2.pyplot()
+
+    # Calculate Metrics
+    acc = accuracy_score(Y_test, Y_pred)
+    mse = mean_squared_error(Y_test, Y_pred)
+    precision, recall, fscore, train_support = score(Y_test, Y_pred, pos_label=1, average='binary')
+    st.subheader("Metrics of the model: ")
+    st.text('Precision: {} \nRecall: {} \nF1-Score: {} \nAccuracy: {} %\nMean Squared Error: {}'.format(
+        round(precision, 3), round(recall, 3), round(fscore, 3), round((acc * 100), 3), round((mse), 3)))
+
+
+st.markdown("<hr>", unsafe_allow_html=True)
+st.header(f"1) Model for Prediction of {dataset_select}")
+st.subheader(f"Classifier Used: {classifier_select}")
+compute(Y_pred, Y_test)
+
+# Execution Time
+end_time = time.time()
+st.info(f"Total execution time: {round((end_time - start_time), 4)} seconds")
+
+
+# Get user values
+def user_inputs_ui(da, data):
+    user_val = {}
+    if dataset_select == "Fitbit Data":
+        X = data.drop(["tag"], axis=1)
+        for col in X.columns:
+            name = col
+            col = st.number_input(col, abs(X[col].min() - round(X[col].std())), abs(X[col].max() + round(X[col].std())))
+            user_val[name] = round((col), 4)
+
+    elif dataset_select == "AppleWatch Data":
+        X = data.drop(["activitytag"], axis=1)
+        for col in X.columns:
+            name = col
+            col = st.number_input(col, abs(X[col].min() - round(X[col].std())), abs(X[col].max() + round(X[col].std())))
+            user_val[name] = col
+
+    return user_val
+
+
+# User values
+st.markdown("<hr>", unsafe_allow_html=True)
+st.header("2) User Values")
+with st.beta_expander("Learn More"):
+    st.markdown("""
+    Please fill in your data to see the results.<br>
+    <p style='color: red;'> 1 - High Risk </p> <p style='color: green;'> 0 - Low Risk </p>
+    """, unsafe_allow_html=True)
+
+user_val = user_inputs_ui(dataset_select, data)
+
+
+# @st.cache(suppress_st_warning=True)
+def user_predict():
+    global U_pred
+    if dataset_select == "AppleWatch Data":
+        X = data.drop(["activitytag"], axis=1)
+        U_pred = clf.predict([[user_val[col] for col in X.columns]])
+
+    elif dataset_select == "Fitbit Data":
+        X = data.drop(["tag"], axis=1)
+        U_pred = clf.predict([[user_val[col] for col in X.columns]])
+
+    st.subheader("Your Status: ")
+    if U_pred == 0:
+        st.write(U_pred[0],
+                 " - NOT A PAIN EVENT -- THIS IS NOT A PROFESSIONAL MEDICAL ADVISE - CONTACT YOUR PRIMARY CARE PROVIDER")
+    else:
+        st.write(U_pred[0],
+                 "- POTENTIAL PAIN EVENT; PLEASE SEE YOUR DOCTOR -- THIS IS NOT A PROFESSIONAL MEDICAL ADVISE")
+
+
+user_predict()  # Predict the status of user.
+

runtime.txt

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+python-3.9.6

setup.sh

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+mkdir -p ~/.streamlit/
+
+echo "\
+[server]\n\
+port = $PORT\n\
+enableCORS = false\n\
+headless = true\n\
+\n\
+" > ~/.streamlit/config.toml