Update app.py

app.py

@@ -3,14 +3,9 @@ import numpy as np
 import matplotlib.pyplot as plt
 from sklearn import linear_model
 
-def plot(seed, num_points):
-	# Error handling of non-numeric seeds
-    if seed and not seed.isnumeric():
-      raise gr.Error("Invalid seed")
-	  
+def plot(seed, num_points):	  
 	# Setting the seed
-    if seed:
-      seed = int(seed)
+    if seed != -1:
       np.random.seed(seed)
     num_points = int(num_points)
 	
@@ -22,7 +17,7 @@ def plot(seed, num_points):
     X = np.r_[np.random.randn(half_num_points, 2) + [1, 1], np.random.randn(half_num_points, 2)]
     y = [1] * half_num_points + [-1] * half_num_points
     sample_weight = 100 * np.abs(np.random.randn(num_points))
-    # and assign a bigger weight to the last 10 samples
+    # and assign a bigger weight to the second half of samples
     sample_weight[:half_num_points] *= 10
 
     # plot the weighted data points
@@ -65,6 +60,7 @@ def plot(seed, num_points):
 	
 info = ''' # SGD: Weighted samples\n
 This is a demonstration of a modified version of [SGD](https://scikit-learn.org/stable/modules/sgd.html#id5) that takes into account the weights of the samples. Where the size of points is proportional to its weight.\n
+The algorithm is demonstrated using points sampled from the standard normal distribution, where the weighted class has a mean of one while the non-weighted class has a mean of zero.\n
 Created by [@Nahrawy](https://huggingface.co/Nahrawy) based on [scikit-learn docs](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_weighted_samples.html).
 '''
 
@@ -72,7 +68,7 @@ with gr.Blocks() as demo:
     gr.Markdown(info)
     with gr.Row():
       with gr.Column():
-        seed = gr.Textbox(label="Seed", info="Leave empty to generate new random points each run ",value=None)
+        seed = gr.Slider(label="Seed", minimum=-1, maximum=10000, step=1,info="Set to -1 to generate new random points each run ",value=-1)
         num_points = gr.Slider(label="Number of Points", value="20", minimum=5, maximum=100, step=2)
         btn = gr.Button("Run")
       out = gr.Plot()