use huggingface theme

app.py

@@ -9,16 +9,16 @@ tokenizer = AutoTokenizer.from_pretrained("dsfsi/PuoBERTa-News")
 model = AutoModelForSequenceClassification.from_pretrained("dsfsi/PuoBERTa-News")
 
 categories = {
-    "arts_culture_entertainment_and_media": "Botsweretshi, setso, boitapoloso le bobegakgang/Arts, culture, entertainment and media",
-    "crime_law_and_justice": "Bosenyi, molao le bosiamisi/Crime, law and justice",
-    "disaster_accident_and_emergency_incident": "Masetlapelo, kotsi le tiragalo ya maemo a tshoganyetso/Disaster, accident and emergency incident",
-    "economy_business_and_finance": "Ikonomi, tsa kgwebo le tsa ditšhelete/Economy, business and finance",
-    "education": "Thuto/Education",
-    "environment": "Tikologo/Environment",
-    "health": "Boitekanelo/Health",
-    "politics": "Dipolotiki/Politics",
-    "religion_and_belief": "Bodumedi le tumelo/Religion and belief",
-    "society": "Setšhaba/Society"
+    "arts_culture_entertainment_and_media": "Botsweretshi, setso, boitapoloso le bobegakgang",
+    "crime_law_and_justice": "Bosenyi, molao le bosiamisi",
+    "disaster_accident_and_emergency_incident": "Masetlapelo, kotsi le tiragalo ya maemo a tshoganyetso",
+    "economy_business_and_finance": "Ikonomi, tsa kgwebo le tsa ditšhelete",
+    "education": "Thuto",
+    "environment": "Tikologo",
+    "health": "Boitekanelo",
+    "politics": "Dipolotiki",
+    "religion_and_belief": "Bodumedi le tumelo",
+    "society": "Setšhaba"
 }
 
 def prediction(news):
@@ -30,8 +30,8 @@ def prediction(news):
 def file_prediction(file):
     # Load the file (CSV or text)
     if file.name.endswith('.csv'):
-        df = pd.read_csv(file.name) 
-        news_list = df.iloc[:, 0].tolist() 
+        df = pd.read_csv(file.name)  
+        news_list = df.iloc[:, 0].tolist()  
     else:
         news_list = [file.read().decode('utf-8')]  
     
@@ -41,42 +41,14 @@ def file_prediction(file):
     
     return results
 
-css = """
-body {
-    background-color: white !important;
-    color: black !important;
-}
-
-.gradio-container {
-    background-color: white !important;
-    color: black !important;
-}
-
-.gr-input, .gr-button, .gr-textbox, .gr-file, .gr-dataframe {
-    background-color: white !important;
-    color: black !important;
-    border-color: #ccc !important;
-}
-
-.gr-button {
-    background-color: #f0f0f0 !important;
-    color: black !important;
-    border: 1px solid #ccc !important;
-}
-
-.gr-dataframe th, .gr-dataframe td {
-    background-color: #f9f9f9 !important;
-    color: black !important;
-}
-"""
-
 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"),
-    css=css  
+    theme="huggingface",  # Apply the dark mode theme
+    article="<p style='text-align: center'>For our other AI works: <a href='https://www.kodiks.com/ai_solutions.html' target='_blank'>https://www.kodiks.com/ai_solutions.html</a> | <a href='https://twitter.com/KodiksBilisim' target='_blank'>Contact us</a></p>",
 )
 
 gradio_file_ui = gr.Interface(
@@ -85,10 +57,9 @@ gradio_file_ui = gr.Interface(
     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"),
-    css=css  
+    theme="huggingface"  # Apply the dark mode theme
 )
 
 gradio_combined_ui = gr.TabbedInterface([gradio_ui, gradio_file_ui], ["Text Input", "File Upload"])
 
 gradio_combined_ui.launch()
-