update for gradio latest version

app.py

@@ -21,27 +21,6 @@ categories = {
     "society": "Setšhaba"
 }
 
-with gr.Row():
-    gr.Column()
-    gr.Column(gr.Image(value="logo_transparent_small.png", elem_id="logo", label=None)) 
-    gr.Column()
-
-description = """
-<p style='text-align: center;'>
-    Setswana News Classification
-</p>
-<p>
-    This space provides a classification service for news in Setswana.
-</p>
-"""
-
-article = """
-<div style='text-align: center;'>
-    <a href='https://github.com/dsfsi/PuoBERTa-News' target='_blank'>GitHub</a> |
-    <a href='https://docs.google.com/forms/d/e/1FAIpQLSf7S36dyAUPx2egmXbFpnTBuzoRulhL5Elu-N1eoMhaO7v10w/viewform' target='_blank'>Feedback Form</a> 
-</div>
-"""
-
 def prediction(news):
     classifier = pipeline("text-classification", tokenizer=tokenizer, model=model, return_all_scores=True)
     preds = classifier(news)
@@ -68,52 +47,71 @@ def file_prediction(file):
 
     return results
 
-gradio_ui = gr.Interface(
-    fn=prediction,
-    title="Setswana News Classification",
-    description=f"Enter Setswana news article to see the category of the news.\n For this classification, the {MODEL_URL} model was used.",
-    inputs=gr.Textbox(lines=10, label="Paste some Setswana news here"),
-    outputs=gr.Label(num_top_classes=5, label="News categories probabilities"),
-    theme="default",
-    article=article,
-)
-
-gradio_file_ui = gr.Interface(
-    fn=file_prediction,
-    title="Upload File for Setswana News Classification",
-    description=f"Upload a text or CSV file with Setswana news articles. The first column in the CSV should contain the news text.",
-    inputs=gr.File(label="Upload text or CSV file"),
-    outputs=gr.Dataframe(headers=["News Text", "Category Predictions"], label="Predictions from file"),
-    theme="default" 
-)
-
-authors = """
-<div style='text-align: center;'>
-    Authors: Vukosi Marivate and Moseli Mots'Oehli and Valencia Wagner and Richard Lastrucci and Isheanesu Dzingirai
-</div>
-"""
-
-citation = """
-@inproceedings{marivate2023puoberta,
-  title   = {PuoBERTa: Training and evaluation of a curated language model for Setswana},
-  author  = {Vukosi Marivate and Moseli Mots'Oehli and Valencia Wagner and Richard Lastrucci and Isheanesu Dzingirai},
-  year    = {2023},
-  booktitle= {Artificial Intelligence Research. SACAIR 2023. Communications in Computer and Information Science},
-  url= {https://link.springer.com/chapter/10.1007/978-3-031-49002-6_17},
-  keywords = {NLP},
-  preprint_url = {https://arxiv.org/abs/2310.09141},
-  dataset_url = {https://github.com/dsfsi/PuoBERTa},
-  software_url = {https://huggingface.co/dsfsi/PuoBERTa}
-}
-"""
-
-doi = """
-<div style='text-align: center;'>
-    DOI: <a href="https://doi.org/10.1007/978-3-031-49002-6_17" target="_blank">10.1007/978-3-031-49002-6_17</a>
-</div>
-"""
-
-gradio_combined_ui = gr.TabbedInterface([gradio_ui, gradio_file_ui], ["Text Input", "File Upload"])
-
-gradio_combined_ui.launch()
-
+with gr.Blocks() as demo:
+    gr.Markdown("""
+    <div style='text-align: center;'>
+        <img src='file/logo_transparent_small.png' alt='Logo' width='150'/>
+    </div>
+    """)
+    
+    gr.Markdown("""
+    <h1 style='text-align: center;'>Setswana News Classification</h1>
+    <p style='text-align: center;'>This space provides a classification service for news in Setswana.</p>
+    """)
+    
+    with gr.Tabs():
+        with gr.Tab("Text Input"):
+            gr.Markdown(f"""
+            Enter Setswana news article to see the category of the news. <br>
+            For this classification, the <a href='{MODEL_URL}' target='_blank'>PuoBERTa-News</a> model was used.
+            """)
+            inp_text = gr.Textbox(lines=10, label="Paste some Setswana news here")
+            output_label = gr.Label(num_top_classes=5, label="News categories probabilities")
+            translate_button = gr.Button("Classify")
+            translate_button.click(prediction, inputs=inp_text, outputs=output_label)
+
+        with gr.Tab("File Upload"):
+            gr.Markdown("""
+            Upload a text or CSV file with Setswana news articles. The first column in the CSV should contain the news text.
+            """)
+            file_input = gr.File(label="Upload text or CSV file")
+            file_output = gr.Dataframe(headers=["News Text", "Category Predictions"], label="Predictions from file")
+            file_button = gr.Button("Classify File")
+            file_button.click(file_prediction, inputs=file_input, outputs=file_output)
+
+    gr.Markdown("""
+    <div style='text-align: center;'>
+        <a href='https://github.com/dsfsi/PuoBERTa-News' target='_blank'>GitHub</a> |
+        <a href='https://docs.google.com/forms/d/e/1FAIpQLSf7S36dyAUPx2egmXbFpnTBuzoRulhL5Elu-N1eoMhaO7v10w/viewform' target='_blank'>Feedback Form</a>
+    </div>
+    """)
+    
+    gr.Markdown("""
+    <div style='text-align: center;'>
+        Authors: Vukosi Marivate and Moseli Mots'Oehli and Valencia Wagner and Richard Lastrucci and Isheanesu Dzingirai
+    </div>
+    """)
+
+    gr.Markdown("""
+    <pre style="text-align: left; white-space: pre-wrap;">
+    @inproceedings{marivate2023puoberta,
+      title   = {PuoBERTa: Training and evaluation of a curated language model for Setswana},
+      author  = {Vukosi Marivate and Moseli Mots'Oehli and Valencia Wagner and Richard Lastrucci and Isheanesu Dzingirai},
+      year    = {2023},
+      booktitle= {Artificial Intelligence Research. SACAIR 2023. Communications in Computer and Information Science},
+      url= {https://link.springer.com/chapter/10.1007/978-3-031-49002-6_17},
+      keywords = {NLP},
+      preprint_url = {https://arxiv.org/abs/2310.09141},
+      dataset_url = {https://github.com/dsfsi/PuoBERTa},
+      software_url = {https://huggingface.co/dsfsi/PuoBERTa}
+    }
+    </pre>
+    """)
+
+    gr.Markdown("""
+    <div style='text-align: center;'>
+        DOI: <a href="https://doi.org/10.1007/978-3-031-49002-6_17" target="_blank">10.1007/978-3-031-49002-6_17</a>
+    </div>
+    """)
+
+demo.launch()