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import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import pickle
import gradio as gr
from math import sqrt
from datasets import load_dataset
df = load_dataset("csv", data_files = "vishal323/heart.csv")
df
df.info()
cp_data= df['cp'].value_counts().reset_index()
cp_data['index'][3]= 'asymptomatic'
cp_data['index'][2]= 'non-anginal'
cp_data['index'][1]= 'Atyppical Anigma'
cp_data['index'][0]= 'Typical Anigma'
cp_data
ecg_data= df['restecg'].value_counts().reset_index()
ecg_data['index'][0]= 'normal'
ecg_data['index'][1]= 'having ST-T wave abnormality'
ecg_data['index'][2]= 'showing probable or definite left ventricular hypertrophy by Estes'
ecg_data
def outbreak(feature):
fig = plt.figure()
plt.rcParams.update({'font.size': 10})
plt.rc('xtick', labelsize=5)
if (feature == "Age"):
plt.title("Age of Patients")
plt.xlabel("Age")
sns.countplot(x='age',data=df);
return fig
elif (feature == "Sex"):
plt.title("Sex of Patients,0=Female and 1=Male")
sns.countplot(x='sex',data=df);
return fig
elif (feature == "Chest Pain"):
plt.title("Chest Pain of Patients")
sns.barplot(x=cp_data['index'],y= cp_data['cp']);
return fig
elif (feature == "ECG"):
plt.title("ECG data of Patients")
sns.barplot(x=ecg_data['index'],y= ecg_data['restecg']);
return fig
elif (feature == "Blood Pressure"):
plt.title("Resting Blood Pressure (mmHg)")
sns.distplot(df['trestbps'], kde=True, color = 'magenta')
plt.xlabel("Resting Blood Pressure (mmHg)")
return fig
def op(target, sex, cp, age, bp, ch):
fig = plt.figure()
plt.rcParams.update({'font.size': 10})
plt.rc('xtick', labelsize=5)
print(target, sex, cp, age, bp, ch)
data = df[((df['target'] == 1) & df['sex'] == sex) & (df['cp'] == cp) & (df['age'] >= age) & (df['trestbps'] >= bp) & (df['chol'] >= ch) ]
if (data.empty):
return fig
if (target == "Age"):
plt.title("Count of age of diseased people")
plt.xlabel("Age")
sns.countplot(x='age',data=data);
return fig
elif (target == "Sex"):
plt.title("Count of sex of diseased people")
plt.xlabel("Sex")
sns.countplot(x='sex',data=data);
return fig
if (target == "Chest Pain"):
plt.title("Count of diseased people with cheast pain")
plt.xlabel("Chest Pain")
sns.countplot(x='cp',data=data);
return fig
if (target == "ECG"):
plt.title("Count of people with low glucose")
plt.xlabel("ECG")
sns.countplot(x='restecg',data=data);
return fig
if (target == "Blood Pressure"):
plt.title("Count of diseased people with high BP")
plt.xlabel("BP")
sns.countplot(x='trestbps',data=data);
return fig
def prd(model, age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal):
if model == "Random Forest":
filename = 'DACVV/randomforest.pkl'
X_test = np.array([[age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal],[52, 1, 0, 125, 212, 0, 1, 168, 0, 1.0, 2, 2, 3]])
loaded_model = pickle.load(open(filename, 'rb'))
result = loaded_model.predict(X_test)[0]
else:
filename = 'DACVV/scaling.pkl'
X_test = np.array([[age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal],[52, 1, 0, 125, 212, 0, 1, 168, 0, 1.0, 2, 2, 3]])
loaded_model = pickle.load(open(filename, 'rb'))
result = loaded_model.predict(X_test)[0]
return "😔, You may a Heart Disease" if (result == 1) else "😁, You are Healthy!!!"
inputs = gr.Dropdown(["Age", "Sex", "Chest Pain", "ECG", "Blood Pressure"], label="Input Feature")
outputs = gr.Plot()
visualisation = gr.Interface(
fn=outbreak,
inputs=inputs,
outputs=outputs,
)
vis = gr.Interface(
inputs = [
gr.Radio(["Age", "Sex", "Chest Pain", "ECG", "Blood Pressure"], label = "Target Feature"),
gr.Radio([1, 0], label = "Sex"),
gr.Radio([0,1,2,3], label = "Chest Pain"),
gr.Slider(25, 80, value=50, step = 1, label = "Age"),
gr.Slider(94, 200, value=150, step = 1, label = "Blood Pressure"),
gr.Slider(126, 564, value=130, step = 1, label = "Cholestrol")
],
fn=op,
outputs = gr.Plot(),
examples=[
["Age", 1, 2, 50, 100, 222],
["Sex", 0, 1, 30, 150, 322],
["Chest Pain", 1, 0, 40, 120, 422],
["ECG", 1, 3, 70, 98, 522],
["Blood Pressure", 0, 1, 28, 170, 262],
]
)
pred = gr.Interface(
inputs = [gr.Radio(["Random Forest", "Scaler"], label = "Model"),
"number",
gr.Radio([0, 1], label = "Sex"),
gr.Radio([0,1,2,3], label = "Chest Pain"),
gr.Slider(94, 200, value=150, step = 1, label = "Blood Pressure"),
gr.Slider(126, 564, value=130, step = 1, label = "Cholestrol"),
gr.Radio([0, 1], label = "FBS"),
gr.Radio([0, 1, 2], label = "RestECG"),
gr.Slider(71, 202, value=50, step = 1, label = "Thalach"),
gr.Radio([0, 1], label = "exang"),
gr.Slider(0, 6.2, value=3, label = "OldPeak"),
gr.Radio([0, 1, 2], label = "Slope"),
gr.Slider(0, 4, value=3, step = 1, label = "CA"),
gr.Slider(0, 3, value=50, step = 1, label = "Thal"),
],
fn=prd,
outputs = "text",
examples=[
["Random Forest", 52, 1, 0, 125, 212, 0, 1, 168, 0, 1.0, 2, 2, 3],
["Scaler", 62, 0, 0, 138, 294, 1, 1, 106, 0, 1.9, 1, 3, 2],
["Random Forest", 44, 0, 2, 108, 141, 0, 1, 175, 0, 0.6, 1, 0, 2],
["Scaler", 68, 0, 2, 120, 211, 0, 0, 115, 0, 1.5, 1, 0, 2]
]
)
interface = gr.TabbedInterface([visualisation, vis, pred], ["Visualisation", "Real Time Analysis", "Predictions"])
interface.launch(inline = False)
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