Update app.py

app.py

@@ -22,7 +22,7 @@ long_text = [["Eyfel Kulesi (Fransızca: La tour Eiffel [la tuʀ ɛˈfɛl]), Par
 ner_example = [["Benim adım Turna."]]
 t2t_example = [["Paraphrase: Bu üründen çok memnun kaldım."]]
 nli_example = [["Bunu çok beğendim. Bunu çok sevdim."]]
-
+text_category_example = [[" anadolu_efes e 18 lik star ! beko_basketbol_ligi nde iddialı bir kadroyla sezona giren anadolu_efes transfer harekatına devam ediyor"]]
 
 
 
@@ -89,9 +89,13 @@ def summarize(input, model_choice="turna_summarization_tr_news"):
         summarization_model = pipeline(model="boun-tabi-LMG/turna_summarization_mlsum", device=0) 
         return summarization_model(input)[0]["generated_text"]
 
+@spaces.GPU
+def categorize(input):
+    ttc = pipeline(model="boun-tabi-LMG/turna_classification_ttc4900", device=0) 
 
+    return ttc(input)[0]["generated_text"]
 
-with gr.Blocks(theme="soft") as demo:
+with gr.Blocks(theme="abidlabs/Lime") as demo:
     gr.Markdown("# TURNA 🐦")
     gr.Markdown(DESCRIPTION)
     
@@ -108,6 +112,18 @@ with gr.Blocks(theme="soft") as demo:
                 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("Text Categorization"):
+        gr.Markdown("TURNA fine-tuned on text categorization. Enter text to categorize text or try the example.")
+        with gr.Column():
+            with gr.Row():
+                with gr.Column():
+                    text_input = gr.Textbox(label="Text Categorization Input")
+                
+                    text_submit = gr.Button()
+                text_output = gr.Textbox(label="Text Categorization Output")
+                text_submit.click(categorize, inputs=[text_input, text_choice], outputs=text_output)
+            text_examples = gr.Examples(examples = text_category_example, inputs = [text_input, text_choice], outputs=text_output, fn=categorize)
+        
     
     with gr.Tab("NLI"):
         gr.Markdown("TURNA fine-tuned on natural language inference. Enter text to infer entailment and pick the model. You can also check for semantic similarity entailment.")