Create app.py

app.py

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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()