Update app.py

app.py

@@ -3,7 +3,6 @@ from cProfile import label
 from joblib import load
 
 import matplotlib.pyplot as plt
-import skfuzzy as fuzz
 import gradio as gr
 import numpy as np
 
@@ -75,9 +74,6 @@ def greet(Age,Sex,CP,Trtbps,Chol,Fbs,Restecg,Thalachh,Oldpeak,Slp,Caa,Thall,Exng
     kmeans = load('kmeans.model') 
     y_km  = kmeans.predict(x_std)
 
-    neigh = load('neigh.model') 
-    y_nb  = neigh.predict(x_std)
-
     tree = load('tree.model') 
     y_tree  = tree.predict(x_std)
 
@@ -87,11 +83,7 @@ def greet(Age,Sex,CP,Trtbps,Chol,Fbs,Restecg,Thalachh,Oldpeak,Slp,Caa,Thall,Exng
     forest = load('forest.model') 
     y_forest  = forest.predict(X_test)
 
-    cntr = load('fcmeans.cntr') 
-    u, u0, d, jm, p, fpc = fuzz.cluster.cmeans_predict(x_std.T, cntr, 2, error=0.005, maxiter=1000)
-    y_fuzzy = np.argmax(u, axis=0)
-
-    r = [y_lr[0], y_fuzzy[0], y_km[0], y_nb[0], y_tree[0], y_bayes[0], y_forest[0]]
+    r = [y_lr[0], y_km[0], y_tree[0], y_bayes[0], y_forest[0]]
 
     f = mode(r)
 
@@ -108,11 +100,14 @@ def greet(Age,Sex,CP,Trtbps,Chol,Fbs,Restecg,Thalachh,Oldpeak,Slp,Caa,Thall,Exng
 
 
 interface = gr.Interface(
-    title = "HeartAttack prediction - UMG",
+    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, Fuzzy C-means, K-means, KNN, Decision Trees, Naive Bayes and Random Forest"+
-    "<br><b>Metrics:</b> Accuracy: 0.787, Precision: 0.750, Recall: 0.909, F1 Score: 0.822 <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> ~ José Adrián Ochoa Sánchez',
+    "<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 = [
@@ -130,7 +125,9 @@ interface = gr.Interface(
         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']
+    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)