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  1. LDA.pkl +0 -0
  2. QDA.pkl +0 -0
  3. README.md +12 -0
  4. Tree.pkl +0 -0
  5. app.py +94 -0
  6. gitattributes +34 -0
  7. heart.dat +270 -0
  8. requirements.txt +1 -0
  9. svm.pkl +0 -0
LDA.pkl ADDED
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QDA.pkl ADDED
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README.md ADDED
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+ ---
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+ title: Heart Disease
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+ emoji: 🦀
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+ colorFrom: pink
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+ colorTo: purple
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+ sdk: gradio
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+ sdk_version: 3.23.0
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+ app_file: app.py
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+ pinned: false
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+ ---
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+
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+ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
Tree.pkl ADDED
Binary file (4.88 kB). View file
 
app.py ADDED
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+ import gradio as gr
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+ import pickle
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+ import pandas as pd
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+
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+ # load the data
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+ heart=pd.read_csv('heart.dat', header=None, sep=' ', names=['age', 'sex', 'cp', 'trestbps', 'chol',
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+ 'fbs', 'restecg', 'thalach', 'exang',
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+ 'oldpeak', 'slope', 'ca', 'thal', 'heart disease'])
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+
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+ # load the saved models
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+ with open('Tree.pkl', 'rb') as f:
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+ tree_model = pickle.load(f)
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+
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+ with open('svm.pkl', 'rb') as f:
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+ svm_model = pickle.load(f)
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+
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+ with open('QDA.pkl', 'rb') as f:
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+ qda_model = pickle.load(f)
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+
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+ with open('MLP.pkl', 'rb') as f:
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+ mlp_model = pickle.load(f)
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+
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+ with open('Log.pkl', 'rb') as f:
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+ log_model = pickle.load(f)
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+
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+ with open('LDA.pkl', 'rb') as f:
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+ lda_model = pickle.load(f)
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+
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+ with open('For.pkl', 'rb') as f:
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+ for_model = pickle.load(f)
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+
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+ # Define the function to make predictions
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+ def make_prediction(age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal, model_name):
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+ # Create a pandas DataFrame from the inputs
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+ input_data = pd.DataFrame({
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+ 'age': [age],
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+ 'sex': [sex],
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+ 'cp': [cp],
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+ 'trestbps': [trestbps],
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+ 'chol': [chol],
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+ 'fbs': [fbs],
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+ 'restecg': [restecg],
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+ 'thalach': [thalach],
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+ 'exang': [exang],
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+ 'oldpeak': [oldpeak],
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+ 'slope': [slope],
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+ 'ca': [ca],
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+ 'thal': [thal]
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+ })
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+
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+ # feature scaling
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+ from sklearn.model_selection import train_test_split
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+ X = heart.drop('heart disease', axis=1)
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+ y = heart['heart disease']
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+ X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42, stratify=y)
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+ from sklearn.preprocessing import StandardScaler
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+ scaler = StandardScaler()
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+ X_train_std = scaler.fit_transform(X_train)
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+
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+ # choose the model and make prediction
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+ model_dict = {'Decision_Tree': tree_model,
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+ 'QDA': qda_model,
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+ 'Artificial_Neural_Networks': mlp_model,
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+ 'Logistic_Regression': log_model,
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+ 'LDA': lda_model,
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+ 'Random_Forest': for_model,
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+ 'SVM': svm_model}
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+ model = model_dict[model_name]
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+ input_data_std = scaler.transform(input_data)
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+ probas = model.predict_proba(input_data_std)
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+ outtext={1:'no heart_disease', 2:'heart disease'}
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+ return {f"Probability of Class {i+1}": proba for i, proba in enumerate(probas[0])}
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+
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+ # Create the Gradio interface
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+ inputs = [
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+ gr.inputs.Number(label='age'),
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+ gr.inputs.Radio(choices=[0,1], label='sex'),
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+ gr.inputs.Dropdown(choices=[1,2,3,4], label='chest pain type'),
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+ gr.inputs.Number(label='resting blood pressure'),
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+ gr.inputs.Number(label='serum cholestoral'),
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+ gr.inputs.Radio(choices=[0,1], label='fasting blood sugar'),
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+ gr.inputs.Radio(choices=[0,1,2], label='resting electrocardiographic'),
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+ gr.inputs.Number(label='maximum heart rate'),
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+ gr.inputs.Radio(choices=[0,1], label='exercise induced angina'),
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+ gr.inputs.Number(label='oldpeak'),
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+ gr.inputs.Dropdown(choices=[1,2,3], label='slope ST'),
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+ gr.inputs.Dropdown(choices=[0,1,2,3], label='major vessels'),
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+ gr.inputs.Dropdown(choices=[3,6,7], label='thal'),
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+ gr.inputs.Dropdown(choices=['Decision_Tree', 'QDA', 'Artificial_Neural_Networks', 'Logistic_Regression', 'LDA', 'Random_Forest', 'SVM'], label='Select the model')
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+ ]
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+
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+ outputs = gr.outputs.Label(label='Predicted class probabilities')
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+
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+ gr.Interface(fn=make_prediction, inputs=inputs, outputs=outputs).launch()
gitattributes ADDED
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heart.dat ADDED
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requirements.txt ADDED
@@ -0,0 +1 @@
 
 
1
+ scikit-learn
svm.pkl ADDED
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