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| import numpy as np | |
| import pandas as pd | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.linear_model import LogisticRegression | |
| from sklearn.preprocessing import StandardScaler | |
| from sklearn.metrics import accuracy_score | |
| import gradio as gr | |
| # Example dataset (replace with actual dataset) | |
| # Load dataset | |
| df = pd.read_csv('heart.csv') | |
| # For demonstration, let's create a synthetic dataset | |
| data = { | |
| 'age': np.random.randint(29, 77, 303), | |
| 'sex': np.random.randint(0, 2, 303), | |
| 'cp': np.random.randint(0, 4, 303), | |
| 'trestbps': np.random.randint(94, 200, 303), | |
| 'chol': np.random.randint(126, 564, 303), | |
| 'fbs': np.random.randint(0, 2, 303), | |
| 'restecg': np.random.randint(0, 2, 303), | |
| 'thalach': np.random.randint(71, 202, 303), | |
| 'exang': np.random.randint(0, 2, 303), | |
| 'oldpeak': np.random.uniform(0, 6.2, 303), | |
| 'slope': np.random.randint(0, 3, 303), | |
| 'ca': np.random.randint(0, 4, 303), | |
| 'thal': np.random.randint(1, 4, 303), | |
| 'target': np.random.randint(0, 2, 303) | |
| } | |
| df = pd.DataFrame(data) | |
| # Define features and target | |
| X = df.drop('target', axis=1) | |
| y = df['target'] | |
| # Split data into training and testing sets | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) | |
| # Standardize the data | |
| scaler = StandardScaler() | |
| X_train = scaler.fit_transform(X_train) | |
| X_test = scaler.transform(X_test) | |
| # Create and train the logistic regression model | |
| model = LogisticRegression() | |
| model.fit(X_train, y_train) | |
| # Evaluate the model | |
| y_pred = model.predict(X_test) | |
| accuracy = accuracy_score(y_test, y_pred) | |
| print(f'Accuracy: {accuracy:.2f}') | |
| # Function to predict heart disease | |
| def predict_heart_disease(age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal): | |
| # Create input array | |
| input_data = np.array([age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal]).reshape(1, -1) | |
| input_data = scaler.transform(input_data) | |
| # Make prediction | |
| prediction = model.predict(input_data) | |
| if prediction[0] == 1: | |
| return "The person has heart disease." | |
| else: | |
| return "The person does not have heart disease." | |
| # Gradio integration | |
| def gradio_predict_heart_disease(age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal): | |
| return predict_heart_disease(age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal) | |
| iface = gr.Interface( | |
| fn=gradio_predict_heart_disease, | |
| inputs=[ | |
| gr.components.Number(label="Age"), | |
| gr.components.Radio(label="Sex", choices=[0, 1]), | |
| gr.components.Dropdown(label="Chest Pain Type (cp)", choices=[0, 1, 2, 3]), | |
| gr.components.Number(label="Resting Blood Pressure (trestbps)"), | |
| gr.components.Number(label="Serum Cholestoral in mg/dl (chol)"), | |
| gr.components.Radio(label="Fasting Blood Sugar > 120 mg/dl (fbs)", choices=[0, 1]), | |
| gr.components.Radio(label="Resting Electrocardiographic Results (restecg)", choices=[0, 1]), | |
| gr.components.Number(label="Maximum Heart Rate Achieved (thalach)"), | |
| gr.components.Radio(label="Exercise Induced Angina (exang)", choices=[0, 1]), | |
| gr.components.Number(label="ST depression induced by exercise relative to rest (oldpeak)"), | |
| gr.components.Dropdown(label="Slope of the peak exercise ST segment (slope)", choices=[0, 1, 2]), | |
| gr.components.Dropdown(label="Number of major vessels (0-3) colored by fluoroscopy (ca)", choices=[0, 1, 2, 3]), | |
| gr.components.Dropdown(label="Thalassemia (thal)", choices=[1, 2, 3]) | |
| ], | |
| outputs="text" | |
| ) | |
| iface.launch() |