| 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) | |