Update app.py

app.py

@@ -22,28 +22,37 @@ t2t_example = [["Paraphrase: Bu üründen çok memnun kaldım."]]
 nli_example = [["Bunu çok beğendim. Bunu çok sevdim."]]
 
 
-t2t_gen_model = pipeline(model="boun-tabi-LMG/TURNA", device=0) 
-
-
-ttc = pipeline(model="boun-tabi-LMG/turna_classification_ttc4900", device=0)
+#ttc = pipeline(model="boun-tabi-LMG/turna_classification_ttc4900", device=0)
 # examples =long_text, title="Text Categorization")
-product_reviews = pipeline(model="boun-tabi-LMG/turna_classification_tr_product_reviews", device=0) 
-# examples=sentiment_example, title="Product Reviews Categorization")
-title_gen = pipeline(model="boun-tabi-LMG/turna_title_generation_mlsum", device=0)
-# examples =long_text, title="Title Generation", description="Enter a text to generate title to.")
-sentiment_model = pipeline(model="boun-tabi-LMG/turna_classification_17bintweet_sentiment", device=0) 
-#examples=sentiment_example, title="Sentiment Analysis", description="Enter a text to generate title to.")
+#product_reviews = pipeline(model="boun-tabi-LMG/turna_classification_tr_product_reviews", device=0) 
+#title_gen = pipeline(model="boun-tabi-LMG/turna_title_generation_mlsum", device=0)
 
 
-nli_model = pipeline(model="boun-tabi-LMG/turna_nli_nli_tr", device=0) 
+@spaces.GPU
+def nli(input, model_choice="turna_nli_nli_tr"):
+    nli_model = pipeline(model="boun-tabi-LMG/turna_nli_nli_tr", device=0) 
+    stsb_model = pipeline(model="boun-tabi-LMG/turna_semantic_similarity_stsb_tr", device=0)
 
-stsb_model = pipeline(model="boun-tabi-LMG/turna_semantic_similarity_stsb_tr", device=0)
+    if model_choice=="turna_nli_nli_tr":
+        return nli_model(input)
+    else:
+        return stsb_model(input)
 
+@spaces.GPU
+def nli(input, model_choice="turna_nli_nli_tr"):
+    nli_model = pipeline(model="boun-tabi-LMG/turna_nli_nli_tr", device=0) 
+    stsb_model = pipeline(model="boun-tabi-LMG/turna_semantic_similarity_stsb_tr", device=0)
 
+    if model_choice=="turna_nli_nli_tr":
+        return nli_model(input)
+    else:
+        return stsb_model(input)
 
 @spaces.GPU
-def sentiment_analysis(input, sentiment=True):
-    if sentiment==True:
+def sentiment_analysis(input, model_choice="turna_classification_17bintweet_sentiment"):
+    product_reviews = pipeline(model="boun-tabi-LMG/turna_classification_tr_product_reviews", device=0) 
+    sentiment_model = pipeline(model="boun-tabi-LMG/turna_classification_17bintweet_sentiment", device=0) 
+    if model_choice=="turna_classification_17bintweet_sentiment":
         return sentiment_model(input)
     else:
         return product_reviews(input)