Moved description within tab

app.py

@@ -128,10 +128,12 @@ def turna(input, max_new_tokens, length_penalty,
 
 
 with gr.Blocks(theme="abidlabs/Lime") as demo:
+
     gr.Markdown("# TURNA")
     gr.Image("images/turna-logo.png", width=100, show_label=False, show_download_button=False, show_share_button=False)
 
-    gr.Markdown(DESCRIPTION)
+    with gr.Tab("TURNA 🐦"):
+        gr.Markdown(DESCRIPTION)
     
     with gr.Tab("Sentiment Analysis"):
         gr.Markdown("TURNA fine-tuned on sentiment analysis. Enter text to analyse sentiment and pick the model (tweets or product reviews).")
@@ -145,38 +147,6 @@ with gr.Blocks(theme="abidlabs/Lime") as demo:
                 sentiment_output = gr.Textbox(label="Sentiment Analysis Output")
                 sentiment_submit.click(sentiment_analysis, inputs=[sentiment_input, sentiment_choice], outputs=sentiment_output)
             sentiment_examples = gr.Examples(examples = sentiment_example, inputs = [sentiment_input, sentiment_choice], outputs=sentiment_output, fn=sentiment_analysis)
-    
-    with gr.Tab("TURNA 🐦"):
-        gr.Markdown("Pre-trained TURNA. Enter text to start generating.")
-        with gr.Column():
-            with gr.Row():
-                with gr.Column():
-                    with gr.Accordion("Advanced Generation Parameters"):
-                        max_new_tokens = gr.Slider(label = "Maximum length",
-                        		minimum = 0,
-                        		maximum = 512,
-                        		value = 128)
-                        length_penalty = gr.Slider(label = "Length penalty",
-                            		value=1.0)
-                        top_k = gr.Slider(label = "Top-k", value=10)
-                        top_p = gr.Slider(label = "Top-p", value=0.95)
-                        temp = gr.Slider(label = "Temperature", value=1.0, minimum=0.1, maximum=100.0)
-                        no_repeat_ngram_size =gr.Slider(label="No Repeat N-Gram Size", minimum=0,value=3,)
-                        repetition_penalty = gr.Slider(label = "Repetition Penalty", minimum=0.0, value=3.1, step=0.1)
-                        num_beams = gr.Slider(label = "Number of beams", minimum=1,
-                            		maximum=10, value=3)
-                        do_sample = gr.Radio(choices = [True, False], value = True, label = "Sampling")
-                with gr.Column():
-                    text_gen_input = gr.Textbox(label="Text Generation Input")
-                
-                    text_gen_submit = gr.Button()
-                text_gen_output = gr.Textbox(label="Text Generation Output")
-            text_gen_submit.click(turna, inputs=[text_gen_input, max_new_tokens, length_penalty,
-                                    top_k, top_p, temp, num_beams,
-                                    do_sample, no_repeat_ngram_size, repetition_penalty], outputs=text_gen_output)
-            text_gen_example = [["Bir varmış, bir yokmuş, evvel zaman içinde, kalbur saman içinde, uzak diyarların birinde bir turna"]]
-            text_gen_examples = gr.Examples(examples = text_gen_example, inputs = [text_gen_input, max_new_tokens, length_penalty,
-                                    top_k, top_p, temp, num_beams, do_sample, no_repeat_ngram_size, repetition_penalty], outputs=text_gen_output, fn=turna)
         
     with gr.Tab("Text Categorization"):
         gr.Markdown("TURNA fine-tuned on text categorization. Enter text to categorize text or try the example.")
@@ -265,6 +235,38 @@ with gr.Blocks(theme="abidlabs/Lime") as demo:
                 sum_submit.click(summarize, inputs=[sum_input, sum_choice, max_new_tokens, length_penalty, no_repeat_ngram_size], outputs=sum_output)
             sum_examples = gr.Examples(examples = long_text, inputs = [sum_input, sum_choice, max_new_tokens, length_penalty, no_repeat_ngram_size], outputs=sum_output,  fn=summarize)
     
+    with gr.Tab("Text Generation"):
+        gr.Markdown("Pre-trained TURNA. Enter text to start generating.")
+        with gr.Column():
+            with gr.Row():
+                with gr.Column():
+                    with gr.Accordion("Advanced Generation Parameters"):
+                        max_new_tokens = gr.Slider(label = "Maximum length",
+                        		minimum = 0,
+                        		maximum = 512,
+                        		value = 128)
+                        length_penalty = gr.Slider(label = "Length penalty",
+                            		value=1.0)
+                        top_k = gr.Slider(label = "Top-k", value=10)
+                        top_p = gr.Slider(label = "Top-p", value=0.95)
+                        temp = gr.Slider(label = "Temperature", value=1.0, minimum=0.1, maximum=100.0)
+                        no_repeat_ngram_size =gr.Slider(label="No Repeat N-Gram Size", minimum=0,value=3,)
+                        repetition_penalty = gr.Slider(label = "Repetition Penalty", minimum=0.0, value=3.1, step=0.1)
+                        num_beams = gr.Slider(label = "Number of beams", minimum=1,
+                            		maximum=10, value=3)
+                        do_sample = gr.Radio(choices = [True, False], value = True, label = "Sampling")
+                with gr.Column():
+                    text_gen_input = gr.Textbox(label="Text Generation Input")
+                
+                    text_gen_submit = gr.Button()
+                text_gen_output = gr.Textbox(label="Text Generation Output")
+            text_gen_submit.click(turna, inputs=[text_gen_input, max_new_tokens, length_penalty,
+                                    top_k, top_p, temp, num_beams,
+                                    do_sample, no_repeat_ngram_size, repetition_penalty], outputs=text_gen_output)
+            text_gen_example = [["Bir varmış, bir yokmuş, evvel zaman içinde, kalbur saman içinde, uzak diyarların birinde bir turna"]]
+            text_gen_examples = gr.Examples(examples = text_gen_example, inputs = [text_gen_input, max_new_tokens, length_penalty,
+                                    top_k, top_p, temp, num_beams, do_sample, no_repeat_ngram_size, repetition_penalty], outputs=text_gen_output, fn=turna)
+
     gr.Markdown(CITATION)
 
 demo.launch()