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app.py

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+from statistics import mode
+from cProfile import label
+from joblib import load
+
+import matplotlib.pyplot as plt
+import gradio as gr
+import numpy as np
+
+def getdata(Age,Sex,CP,Trtbps,Chol,Fbs,Restecg,Thalachh,Oldpeak,Slp,Caa,Thall,Exng):
+
+    if Sex == "Male":
+        Sex = 1
+    else: 
+        Sex = 0
+
+    if CP == "Typical Angina":
+        CP = 0
+    elif CP == "Atypical Angina":
+        CP = 1
+    elif CP == "Non-anginal Pain":
+        CP = 2
+    else: 
+        CP = 3
+
+    if Fbs == "True":
+        Fbs = 1
+    else: 
+        Fbs = 0
+
+    if Restecg == "Normal":
+        Restecg = 0
+    elif Restecg == "ST-T wave normality":
+        Restecg = 1
+    else: 
+        Restecg = 2
+
+    if Exng == "Yes":
+        Exng = 1
+    else: 
+        Exng = 0
+
+    a = [Age,Sex,CP,Trtbps,Chol,Fbs,Restecg,Thalachh,Exng,Oldpeak,Slp,Caa,Thall]
+    arr = np.array([a])
+
+    return arr
+
+def getfig(X_test):
+    X_pca = load('X_pca.data')
+    y = load('y.data')
+
+    pca = load('pca.dim')
+    u_pca  = pca.transform(X_test)
+
+    fig = plt.figure(figsize=(5,4))
+
+    plt.scatter(X_pca[:, 0], X_pca[:, 1], c = y, cmap = plt.cm.Spectral, s =  10)
+    plt.scatter(u_pca[:, 0], u_pca[:, 1], c = 'g', cmap = plt.cm.Spectral, s =  40)
+    plt.title(f"PCA, Exp. Variance: {np.round(np.sum(pca.explained_variance_ratio_), 4)}")
+    plt.xlabel("PC 1")
+    plt.ylabel("PC 2")
+
+    return fig
+
+def greet(Age,Sex,CP,Trtbps,Chol,Fbs,Restecg,Thalachh,Oldpeak,Slp,Caa,Thall,Exng):
+    
+    X_test = getdata(Age,Sex,CP,Trtbps,Chol,Fbs,Restecg,Thalachh,Oldpeak,Slp,Caa,Thall,Exng)
+    
+    scaler = load('stdscaler.model') 
+    x_std = scaler.transform(X_test)
+
+    log_reg = load('log_reg.model') 
+    y_lr  = log_reg.predict(x_std)
+    
+    kmeans = load('kmeans.model') 
+    y_km  = kmeans.predict(x_std)
+
+    tree = load('tree.model') 
+    y_tree  = tree.predict(x_std)
+
+    nb = load('nb.model') 
+    y_bayes  = nb.predict(X_test)
+
+    forest = load('forest.model') 
+    y_forest  = forest.predict(X_test)
+
+    r = [y_lr[0], y_km[0], y_tree[0], y_bayes[0], y_forest[0]]
+
+    f = mode(r)
+
+    if f == 0:
+        x = "You have less chance of heart attack"
+    else:
+        x = "You have more chance of heart attack"
+
+    fig = load('dime.fig')
+
+    fig2 = getfig(X_test)
+
+    return x, fig, fig2
+
+
+interface = gr.Interface(
+    title = "HeartAttack prediction - UMG <br> Project Coeur ❤",
+    description = "<h3>The idea is to classify between 0 = less chance of heart attack and 1 = more chance of heart attack, according to the data provided by the user.</h3>"+
+    "<b>Models:</b> Logistic Regression, K-means, Decision Trees, Naive Bayes and Random Forest"+
+    "<br><b>Metrics:</b> Accuracy: 0.82, Precision: 0.775, Recall: 0.939, F1 Score: 0.849 <br>  <br><b>Please provide the requested data:</b>",
+    article='Step-by-step on GitHub <a href="https://github.com/Adrian8aS/Machine-Learning-App-Gradio/blob/21246d9ba87859e9068369b89d48b4c6ee13dfe5/Proyecto%20integrador.ipynb"> notebook </a> '+
+    '<br>Dashboard of our train data <a href="https://1drv.ms/x/s!At7E16oDTBiKktUagvJHHpF5CCoITA?e=fOLjUq"> here! </a> '+
+    '<br>Privacy Policy <a href="https://raw.githubusercontent.com/rulasvrdz/DataMining/main/Texto.txt"> here! </a> '+
+    "<br><br> ~ Project Coeur",
+    allow_flagging = "never",
+    fn = greet, 
+    inputs = [
+        gr.Number(label="Age of the patient"),
+        gr.Radio(["Male", "Female"], label="Sex of the patient"),
+        gr.Radio(["Typical Angina", "Atypical Angina", "Non-anginal Pain", "Asymptomatic"], label="Chest pain type"),
+        gr.Number(label="Resting blood pressure (in mm Hg)"),
+        gr.Number(label="Cholestoral in mg/dl fetched via BMI sensor"),
+        gr.Radio(["True", "False"], label="Fasting blood sugar > 120 mg/dl"),
+        gr.Radio(["Normal", "ST-T wave normality", "Left ventricular hypertrophy"], label="Resting electrocardiographic results"),
+        gr.Number(label="Maximum heart rate achieved"),
+        gr.Number(label="Previous peak"),
+        gr.Radio([0, 1, 2], label="Slope"),
+        gr.Radio([0, 1, 2, 3, 4], label="Number of major vessels"),
+        gr.Radio([0, 1, 2, 3], label="Thalium Stress Test result"),
+        gr.Radio(["Yes", "No"], label="Exercise induced angina")
+    ],
+    outputs = [gr.Text(label="Prediction"), 'plot', 'plot'],
+    examples = [[41,"Female","Typical Angina",130,204,"False","Normal",172,1.4,2,0,2,"No"],
+                [45,"Male","Non-anginal Pain",110,264,"False","ST-T wave normality",132,0.2,1,0,3,"No"]]
+)
+
+interface.launch(share = False)

forest.model

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+oid sha256:3da56481146795354070b14c02880418a191267b056eec5414347134757d79b1
+size 633758

kmeans.model

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+oid sha256:4f9d8cdebc8c83b6946b475004332c9413f97c45eb3c09c7978cd3e10aff1834
+size 1831

log_reg.model

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+oid sha256:49d5c1cded04e1f5d3c61a6363b5333bfd28a2888fbce2f4115c4b47e3aa570b
+size 886

nb.model

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+oid sha256:69ad050f1449fcb6aa41f6187d977ad21adac71073006b8697d9ac219917027f
+size 1100

stdscaler.model

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+oid sha256:f1460e2c8a2c392f8b31f63ba3665c7857e5c0436eefa1c25f2dd9bb44e9beb3
+size 1287

tree.model

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+size 4220