update css integration

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",
-    "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"
+    "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"
 }
 
 def prediction(news):
@@ -30,10 +30,10 @@ 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')] 
+        news_list = [file.read().decode('utf-8')]  
     
     results = []
     for news in news_list:
@@ -41,24 +41,6 @@ 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"),
-)
-
-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"),
-)
-
-gradio_combined_ui = gr.TabbedInterface([gradio_ui, gradio_file_ui], ["Text Input", "File Upload"])
-
 css = """
 body {
     background-color: white !important;
@@ -88,4 +70,25 @@ body {
 }
 """
 
-gradio_combined_ui.launch(css=css)
+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  
+)
+
+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"),
+    css=css  
+)
+
+gradio_combined_ui = gr.TabbedInterface([gradio_ui, gradio_file_ui], ["Text Input", "File Upload"])
+
+gradio_combined_ui.launch()
+