Minor improvements

app.py

@@ -5,8 +5,6 @@ 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", 
@@ -33,7 +31,7 @@ def create_dataset(feature_id=2):
 
 def train_model(input_data):
 
-    # We reomved the sex variable 
+    # We removed the sex variable 
     if input_data == 'age':
         feature_id = 0
     else:
@@ -57,7 +55,7 @@ def train_model(input_data):
     # Plot outputs
     fig = plt.figure(figsize=FIGSIZE)
     
-    plt.title(input_data)
+    # 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)
 
@@ -68,7 +66,7 @@ def train_model(input_data):
 
     return fig, regr.coef_, mse, r2
 
-title = "Linear Regression Example"
+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}")
@@ -77,7 +75,7 @@ with gr.Blocks() as demo:
     with gr.Column():
     
         with gr.Row():
-            plot = gr.Plot(label="Feature")
+            plot = gr.Plot()
             with gr.Column():
                 input_data = gr.Dropdown(choices=feature_names, label="Feature", value="body-mass index")
                 coef = gr.Textbox(label="Coefficients")