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app.py

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+
+import gradio as gr
+import joblib as jb
+import pandas as pd
+
+
+pipe = jb.load('heart1.pkl')
+
+
+
+
+
+
+def predict(age,	sex,	cp,	trtbps,	chol,	fbs,	restecg,	thalachh,	exng,	oldpeak,	slp,	caa,	thall):
+    if sex=="Male":
+      sex=1
+    else:
+      sex=0
+
+    if cp=="Typical Angina":
+      cp=0
+    elif cp=="Typical Angina":
+      cp=1
+    elif cp=="Atypical Angina":
+      cp=2
+    elif cp=="Non-Anginal pain":
+      cp=3
+    elif cp=="Asymptomatic":
+      cp=4
+    else:
+      cp=0
+
+    if fbs>120:
+      fbs=1
+    else:
+      fbs=0
+
+    if restecg=="Normal":
+      restecg=0
+    elif restecg=="ST-T wave abnormality":
+      restecg=1
+    elif restecg=="Definite left Ventricular hypertrophy":
+      restecg=2
+    else:
+      restecg=0
+
+    if slp=="Unsloping":
+      slp=0
+    elif restecg=="flat":
+      slp=1
+    elif restecg=="Downsloping":
+      slp=2
+    else:
+      slp=0
+    
+    if exng=="Yes":
+      exng=1
+    else:
+      exng=0
+    
+    if thall=="Null":
+      thall=0
+    elif thall=="Fixed defect":
+      thall=1
+    elif cp=="Normal":
+      thall=2
+    elif cp=="Reversable defect":
+      thall=3
+    else:
+      thall=0
+
+    
+    x = pd.DataFrame([[age,	sex,	cp,	trtbps,	chol,	fbs,	restecg,	thalachh,	exng,	oldpeak,	slp,	caa,	thall]])
+    x.columns = ["age",	"sex",	"cp",	"trtbps",	"chol",	"fbs",	"restecg",	"thalachh",	"exng",	"oldpeak",	"slp",	"caa",	"thall"]
+    prediction = pipe.predict(x)
+
+    ans = prediction[0]
+    
+    if ans>=1:
+      ans = 100
+    elif ans<=0:
+      ans = 0
+    else:
+      ans = ans*100 
+
+    if ans>50:
+      final = str(int(ans)) + "%" +"\n>50% Diameter narrowing.\nMore chance of heart disease."
+    else:
+      final = str(int(ans)) + "%" + "\n< 50% Diameter narrowing.\nLess chance of heart disease."
+
+
+    return final
+
+
+
+app = gr.Interface(title="Heart Attack Predictor",fn=predict, 
+        inputs=[
+        gr.inputs.Number(label='Age'), 
+        gr.inputs.Radio(["Male","Female"],label='Sex'),
+        gr.inputs.Radio(["Typical Angina","Atypical Angina","Non-Anginal pain","Asymptomatic"],label='Chest Pain'),
+        gr.inputs.Number(label='Resting blood pressure (in mm Hg)'),
+        gr.inputs.Number(label='Cholestoral in mg/dl '),
+        gr.inputs.Number(label='Fasting blood sugar'),
+        gr.inputs.Radio(["Normal","ST-T wave abnormality","Definite left Ventricular hypertrophy"],label='Resting Rlectrocardiographic Results'),
+        gr.inputs.Number(label='Maximum Heartrate achieved'),
+        gr.inputs.Radio(["Yes","No"],label='Exercise induced Angina'),
+        gr.inputs.Number(label='ST depression induced by exercise'),
+        gr.inputs.Radio(["Unsloping","flat","Downsloping"],label='Slope of the peak exercise ST segment'),
+        gr.inputs.Slider(minimum=0,maximum=3,step=1,label='Number of major vessels'),
+        gr.inputs.Radio(["Null","Fixed defect","Normal","Reversable defect"],label='Thalassemia'), 
+    ],
+    outputs=gr.outputs.Textbox(label="Prediction")
+)   
+
+
+
+app.launch()
+

heart1.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0ea135ed7e84201ee0622c35a012a0757d2d291a33f0a751910db244c702c57e
+size 3367