Create the lr example

app.py

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+import gradio as gr
+
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn import datasets, linear_model
+from sklearn.metrics import mean_squared_error, r2_score
+
+from functools import partial
+
+FIGSIZE = (10,10)
+
+feature_names = ["age", "body-mass index (BMI)", "blood pressure", 
+                "total serum cholesterol", "low-density lipoproteins (LDL)", 
+                "high-density lipoproteins (HDL)", "total cholesterol / HDL", 
+                "log of serum triglycerides level (possibly)","blood sugar level"]
+
+def create_dataset(feature_id=2):
+    # Load the diabetes dataset
+    diabetes_X, diabetes_y = datasets.load_diabetes(return_X_y=True)
+
+    # Use only one feature
+    diabetes_X = diabetes_X[:, np.newaxis, feature_id]
+
+    # Split the data into training/testing sets
+    diabetes_X_train = diabetes_X[:-20]
+    diabetes_X_test = diabetes_X[-20:]
+
+    # Split the targets into training/testing sets
+    diabetes_y_train = diabetes_y[:-20]
+    diabetes_y_test = diabetes_y[-20:]
+
+    return diabetes_X_train, diabetes_X_test, diabetes_y_train, diabetes_y_test
+
+def train_model(input_data):
+
+    # We reomved the sex variable 
+    if input_data == 'age':
+        feature_id = 0
+    else:
+        feature_id = feature_names.index(input_data) + 1
+
+    diabetes_X_train, diabetes_X_test, diabetes_y_train, diabetes_y_test = create_dataset(feature_id)
+
+    
+    # Create linear regression object
+    regr = linear_model.LinearRegression()
+
+    # Train the model using the training sets
+    regr.fit(diabetes_X_train, diabetes_y_train)
+
+    # Make predictions using the testing set
+    diabetes_y_pred = regr.predict(diabetes_X_test)
+
+    mse = mean_squared_error(diabetes_y_test, diabetes_y_pred)
+    r2 = r2_score(diabetes_y_test, diabetes_y_pred)
+
+    # Plot outputs
+    fig = plt.figure(figsize=FIGSIZE)
+    
+    plt.title(input_data)
+    plt.scatter(diabetes_X_test, diabetes_y_test, color="black")
+    plt.plot(diabetes_X_test, diabetes_y_pred, color="blue", linewidth=3)
+
+    plt.xticks(())
+    plt.yticks(())
+
+
+
+    return fig, regr.coef_, mse, r2
+
+title = "Linear Regression Example"
+description = "The example shows how linear regression attempts to draw a straight line that will best minimize the residual sum of squares between the observed responses in the dataset"
+with gr.Blocks() as demo:
+    gr.Markdown(f"## {title}")
+    gr.Markdown(description)
+
+    with gr.Column():
+    
+        with gr.Row():
+            plot = gr.Plot(label="Feature")
+            with gr.Column():
+                input_data = gr.Dropdown(choices=feature_names, label="Feature", value="body-mass index")
+                coef = gr.Textbox(label="Coefficients")
+                mse = gr.Textbox(label="MSE")
+                r2 = gr.Textbox(label="R2")
+
+        input_data.change(fn=train_model, inputs=[input_data], outputs=[plot, coef, mse, r2], queue=False)
+
+    
+demo.launch(enable_queue=True)

requirements.txt

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+scikit-learn
+matplotlib
+numpy
+