Update app.py

app.py

@@ -1,20 +1,29 @@
 import gradio as gr
 from transformers import pipeline
-generator = pipeline('text-generation',model='gpt2')
+
+# Load the text generation pipeline
+generator = pipeline('text-generation', model='gpt2')
+
+# Define the function for text generation
 def generate(text):
-  result = generator(text,max_length=100, num_return_sequences=1)
-  return result[0]['generated_text']
-examples=[
+    result = generator(text, max_length=100, num_return_sequences=1)
+    return result[0]['generated_text']
+
+# Define examples
+examples = [
     ["What is the fundamental difference between supervised and unsupervised learning"],
     ["What is overfitting in supervised learning"],
-    ["What is a convolutional neural network "],
+    ["What is a convolutional neural network"],
     ["Describe the concept of transfer learning and its significance in deep learning"]
-    
 ]
+
+# Create the Gradio interface
 run = gr.Interface(
     fn=generate,
-    inputs=gr.inputs.Textbox(lines=5,label="Input Text"),
-    outputs=gr.outputs.Textbox(label="Generated Text"),
+    inputs=gr.Textbox(lines=5, label="Input Text"),
+    outputs=gr.Textbox(label="Generated Text"),
     examples=examples
 )
+
+# Launch the app
 run.launch()