Create app.py

app.py

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+import gradio as gr
+import joblib
+import pandas as pd
+import numpy as np
+
+# Load your dataset here
+file_path_gr = '/content/drive/MyDrive/Colab Notebooks/Homework_04/heart.dat'
+df = pd.read_csv(file_path_gr, header=None, sep='\s+')
+
+# Define the X variable with the feature columns
+X = df.drop(df.columns[-1], axis=1)
+
+# Load the saved models
+best_models = [
+    ("Logistic Regression", joblib.load('/content/drive/MyDrive/Colab Notebooks/Homework_04/Logistic Regression_best_model.pkl')),
+    ("Support Vector Machine", joblib.load('/content/drive/MyDrive/Colab Notebooks/Homework_04/Support Vector Machine_best_model.pkl')),
+    ("Decision Trees", joblib.load('/content/drive/MyDrive/Colab Notebooks/Homework_04/Decision Trees_best_model.pkl')),
+    ("Random Forests", joblib.load('/content/drive/MyDrive/Colab Notebooks/Homework_04/Random Forests_best_model.pkl')),
+    ("MLPClassifier", joblib.load('/content/drive/MyDrive/Colab Notebooks/Homework_04/MLPClassifier_best_model.pkl')),
+    ("K-Nearest Neighbors", joblib.load('/content/drive/MyDrive/Colab Notebooks/Homework_04/K-Nearest Neighbors_best_model.pkl')),
+    ("Linear Discriminant Analysis",joblib.load('/content/drive/MyDrive/Colab Notebooks/Homework_04/Linear Discriminant Analysis_best_model.pkl'))
+]
+
+# Load the saved scaler
+scaler = joblib.load('/content/drive/MyDrive/Colab Notebooks/Homework_04/scaler.pkl')
+
+attribute_names = [
+    "age", "sex", "chest pain type", "resting blood pressure",
+    "serum cholesterol", "fasting blood sugar > 120 mg/dl",
+    "resting ECG results", "maximum heart rate achieved",
+    "exercise induced angina", "oldpeak", "slope of peak exercise ST segment",
+    "number of major vessels", "thal"
+]
+
+def predict(*args):
+    user_features = list(args[:-1])
+    model_name = args[-1]
+    model = dict(best_models)[model_name]
+    input_data = np.array(user_features).reshape(1, -1)
+    input_data_scaled = scaler.transform(input_data)
+    probs = model.predict_proba(input_data_scaled)
+    return {f"Absence of heart disease" if i == 0 else "Presence of heart disease": p for i, p in enumerate(probs[0])}
+
+# List of model names for the dropdown menu
+model_names = [name for name, _ in best_models]
+
+# Define inputs
+user_features = [gr.inputs.Number(label=col_name) for col_name in attribute_names]
+model_name = gr.inputs.Dropdown(choices=model_names, label="Model")
+
+# Define output
+output = gr.outputs.Label(num_top_classes=len(np.unique(df[df.columns[-1]])), label="Predicted Class Probabilities")
+
+# Define examples
+examples = [
+    [70.0, 1.0, 4.0, 130.0, 322.0, 0.0, 2.0, 109.0, 0.0, 2.4, 2.0, 3.0, 3.0, "Logistic Regression"],
+    [67.0, 0.0, 3.0, 115.0, 564.0, 0.0, 2.0, 160.0, 0.0, 1.6, 2.0, 0.0, 7.0, "Logistic Regression"]
+]
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=user_features + [model_name],
+    outputs=output,
+    title="Machine Learning Model",
+    description="Select a model and enter user features to predict class probabilities.",
+    examples=examples
+).launch(debug=True)
+
+iface.launch()