Create app.py

app.py

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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
+
+df= pd.read_csv("/content/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 = '/content/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 = '/content/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)
+