App and Requirements

app.py

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+import numpy
+import gradio as gr
+import pandas as pd
+from sklearn.preprocessing import LabelEncoder
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import accuracy_score
+from sklearn.svm import SVC
+from sklearn.preprocessing import MinMaxScaler
+def heart(Age,Sex,ChestPainType,RestingBP,Cholesterol,FastingBS,RestingECG,MaxHR,ExerciseAngina,Oldpeak,ST_Slope):
+  d=pd.read_csv("https://raw.githubusercontent.com/akarshsnair/Dataset-cart/main/heart.csv")
+  for col in d.columns:
+      if d[col].dtype == 'object':
+          d[col] = LabelEncoder().fit_transform(d[col])
+  svm=SVC(probability=True)
+  xdf=d.drop("HeartDisease",axis=1)
+  ydf=d["HeartDisease"]
+  x_train,x_test,y_train,y_test=train_test_split(xdf,ydf,test_size=0.35,random_state=20)
+  mms=MinMaxScaler(feature_range=(0,1))
+  x_train=mms.fit_transform(x_train)
+  x_test=mms.fit_transform(x_test)
+  x_train=pd.DataFrame(x_train)
+  x_test=pd.DataFrame(x_test)
+  svm=SVC(probability=True)
+  svm.fit(x_train,y_train)
+  predictions=svm.predict(x_test)
+  data = {'Age':Age,'Sex':Sex,'Chest Pain Type':ChestPainType,'Resting Blood Pressure':RestingBP,'Cholesterol level ':Cholesterol,'Fasting Blood Sugar':FastingBS,'Resting E.C.G':RestingECG,'Maximum Heart Rate achieved ':MaxHR,'Exercise induced Angina ':ExerciseAngina,'Old peak':Oldpeak,'ST_Slope':ST_Slope}
+  index = [0]
+  cust_df = pd.DataFrame(data, index)
+  costpredLog = svm.predict(cust_df)
+  if costpredLog ==0:
+    Prediction = "There is less chance for the patient to catch with heart disease"
+  else:
+    Prediction = "There is more of a chance for the patient to catch heart disease."
+  return  Prediction
+  
+  
+iface = gr.Interface(fn = heart,
+                     
+inputs =['number','number','number','number','number','number','number','number','number','number','number'],
+
+outputs =['text'],
+
+title="Onset of heart disease failure prediction",
+
+description ='''    Description
+Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worlwide. 
+Heart failure is a common event caused by CVDs and this dataset contains 12 features that can be used to predict mortality by heart failure.
+Most cardiovascular diseases can be prevented by addressing behavioural risk factors such as tobacco use, unhealthy diet and obesity, 
+physical inactivity and harmful use of alcohol using population-wide strategies. People with cardiovascular disease or who are at high cardiovascular risk 
+(due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) 
+need early detection and management wherein a machine learning model can be of great help.
+
+More details about the Inputs taken and how they needed to be taken are given below:
+
+     * Age (Your Age)
+     * Sex (Male (1) or Female (0))
+     * Chest pain 
+        1) ASY - 0
+        2) ATA - 1
+        3) NAP - 2
+        4) TA - 3
+     * RestingBP - Resting Blood pressure
+     * Cholesterol - Cholesterol level as of now
+     * FastingBS - Fasting blood sugar 
+        1) Above 120,Type 1
+        2) Below 120,Type 0
+     * RestingECG 
+        1) LVH - 0
+        2) NORMAL - 1
+        3) ST - 2
+     * MaxHR - Is the maximum heart rate recorded
+     * ExerciseAngina - Do you have Angine while you exercise 
+        1) No - 0
+        2) Yes - 1
+     * Oldpeak -  ST depression induced by exercise relative to rest.
+     * ST_Slope - The ST segment shift relative to exercise-induced increments in heart rate
+        1) Down - 0
+        2) Flat - 1
+        3) UP - 2
+
+''',
+
+article=''' This application is made for Hackathon'22
+
+Through this project, we are looking forward to providing insight into the health of each and every person.
+The dataset is taken from Kaggle and the link for the dataset is provided here:
+
+Dataset link as Raw form: https://raw.githubusercontent.com/ADITHYASNAIR2021/Dataset-cart/main/heart.csv
+
+''')
+
+iface.launch(debug = True)

requirements.txt

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