Update app.py

app.py

@@ -1,28 +1,36 @@
 import gradio as gr
 from transformers import pipeline
 
+# Define the model and the text generation function
 fine_tuned_model = "Amitesh007/text_generation-finetuned-gpt2"
-def generate(text):
-    generator = pipeline('text-generation', model=fine_tuned_model)
-    result = generator(text, num_return_sequences=2,
-    max_length=100
-                      
-     )
-    return result[0]["generated_text"],result[1]["generated_text"]
+generator = pipeline('text-generation', model=fine_tuned_model)
 
-Examples = [["A light snow had fallen the night before, and there were"],["The pig face had been smashed in with a mace, but Tyrion"]]
+def generate(text):
+    results = generator(text, num_return_sequences=2, max_length=100)
+    return results[0]["generated_text"], results[1]["generated_text"]
 
-demo = gr.Interface(
-    fn=generate,
-    inputs=[gr.Textbox(lines=5, label="Input Text here....")],
-    outputs=[
-        gr.Textbox(label="Generated Text 1"),
-        gr.Textbox(label="Generated Text 2")
+# Create the Gradio Blocks interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Text Generator GPT2 Pipeline")
+    gr.Markdown("This is a fine-tuned base GPT2 model inference, trained on a small 'Game of Thrones' dataset.")
+    
+    with gr.Row():
+        with gr.Column():
+            input_text = gr.Textbox(lines=5, label="Input Text here....", placeholder="Type a sentence to start generating text")
+            generate_button = gr.Button("Generate")
+        
+        with gr.Column():
+            output_text1 = gr.Textbox(label="Generated Text 1")
+            output_text2 = gr.Textbox(label="Generated Text 2")
+    
+    examples = gr.Examples(
+        examples=[["A light snow had fallen the night before, and there were"], 
+                  ["The pig face had been smashed in with a mace, but Tyrion"]],
+        inputs=input_text
+    )
+    
+    generate_button.click(fn=generate, inputs=input_text, outputs=[output_text1, output_text2])
 
-        ],
-    title = "Text Generator GPT2 Pipeline",
-    description = "this is a fine-tuned base gpt2 model inference, trained on a small 'game of thrones' dataset",
-    examples = Examples
-)
+# Launch the interface
 demo.queue(concurrency_count=2)
-demo.launch()
+demo.launch()