Update app.py

app.py

@@ -34,9 +34,9 @@ def nli(input, model_choice="turna_nli_nli_tr"):
     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)
+        return nli_model(input)[0]["generated_text"]
     else:
-        return stsb_model(input)
+        return stsb_model(input)[0]["generated_text"]
 
 @spaces.GPU
 def nli(input, model_choice="turna_nli_nli_tr"):
@@ -44,47 +44,40 @@ def nli(input, model_choice="turna_nli_nli_tr"):
     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)
+        return nli_model(input)[0]["generated_text"]
     else:
-        return stsb_model(input)
+        return stsb_model(input)[0]["generated_text"]
 
 @spaces.GPU
 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)
+        return sentiment_model(input)[0]["generated_text"]
     else:
-        return product_reviews(input)
-
-@spaces.GPU
-def nli_stsb(input, nli=True):
-    if nli==True:
-        return nli_model(input)
-    else:
-        return stsb_model(input)
+        return product_reviews(input)[0]["generated_text"]
 
 @spaces.GPU
 def t2t(input):
     return t2t_gen_model(input)
 
 @spaces.GPU
-def pos(input, model_choice="turna_pos_imst"):
-    pos_imst = pipeline(model="boun-tabi-LMG/turna_pos_imst", device=0) 
-    pos_boun = pipeline(model="boun-tabi-LMG/turna_pos_boun", device=0) 
+def pos(input, model_choice="turna_pos_imst"): 
     if model_choice=="turna_pos_imst":
-        return pos_imst(input)
+        pos_imst = pipeline(model="boun-tabi-LMG/turna_pos_imst", device=0) 
+        return pos_imst(input)[0]["generated_text"]
     else:
-        return pos_boun(input)
+        pos_boun = pipeline(model="boun-tabi-LMG/turna_pos_boun", device=0)
+        return pos_boun(input)[0]["generated_text"]
 
 @spaces.GPU
 def ner(input, model_choice="turna_ner_wikiann"):
-    ner_model = pipeline(model="boun-tabi-LMG/turna_ner_milliyet", device=0) 
-    ner_wikiann = pipeline(model="boun-tabi-LMG/turna_ner_wikiann", device=0) 
     if model_choice=="turna_ner_wikiann":
-        return ner_wikiann(input)
+        ner_wikiann = pipeline(model="boun-tabi-LMG/turna_ner_wikiann", device=0) 
+        return ner_wikiann(input)[0]["generated_text"]
     else:
-        return ner_model(input)
+        ner_model = pipeline(model="boun-tabi-LMG/turna_ner_milliyet", device=0) 
+        return ner_model(input)[0]["generated_text"]
 
 
 @spaces.GPU
@@ -92,18 +85,18 @@ def paraphrase(input, model_choice="turna_paraphrasing_tatoeba"):
     paraphrasing = pipeline(model="boun-tabi-LMG/turna_paraphrasing_tatoeba", device=0) 
     paraphrasing_sub = pipeline(model="boun-tabi-LMG/turna_paraphrasing_opensubtitles", device=0) 
     if model_choice=="turna_paraphrasing_tatoeba":
-        return paraphrasing(input)
+        return paraphrasing(input)[0]["generated_text"]
     else:
-        return paraphrasing_sub(input)
+        return paraphrasing_sub(input)[0]["generated_text"]
         
 @spaces.GPU    
 def summarize(input, model_choice="turna_summarization_tr_news"):
     summarization_model = pipeline(model="boun-tabi-LMG/turna_summarization_mlsum", device=0) 
     news_sum = pipeline(model="boun-tabi-LMG/turna_summarization_tr_news", device=0) 
     if model_choice=="turna_summarization_tr_news":
-        return news_sum(input)
+        return news_sum(input)[0]["generated_text"]
     else:
-        return summarization_model(input)
+        return summarization_model(input)[0]["generated_text"]
 
 
 
@@ -127,8 +120,9 @@ with gr.Blocks(theme="soft") as demo:
             with gr.Row():
                 ner_choice = gr.Radio(choices = ["turna_ner_wikiann", "turna_ner_milliyet"], label ="Model")
                 ner_input = gr.Textbox(label="NER Input")
-                ner_output = gr.Textbox(label="NER Output")
                 ner_submit = gr.Button()
+                ner_output = gr.Textbox(label="NER Output")
+                
                 ner_submit.click(ner, inputs=[ner_input, ner_choice], outputs=ner_output)
             ner_examples = gr.Examples(examples = ner_example, inputs = [ner_input, ner_choice], outputs=ner_output, fn=ner)
     with gr.Tab("Paraphrase"):
@@ -137,8 +131,9 @@ with gr.Blocks(theme="soft") as demo:
             with gr.Row():
                 paraphrasing_choice = gr.Radio(choices = ["turna_paraphrasing_tatoeba", "turna_paraphrasing_opensubtitles"], label ="Model")
                 paraphrasing_input = gr.Textbox(label = "Paraphrasing Input")
-                paraphrasing_output = gr.Text(label="Paraphrasing Output")
                 paraphrasing_submit = gr.Button()
+                paraphrasing_output = gr.Text(label="Paraphrasing Output")
+                
                 paraphrasing_submit.click(paraphrase, inputs=[paraphrasing_input, paraphrasing_choice], outputs=paraphrasing_output)
             paraphrase_examples = gr.Examples(examples = long_text, inputs = [paraphrasing_input, paraphrasing_choice], outputs=paraphrasing_output,  fn=paraphrase)
     with gr.Tab("Summarization"):
@@ -147,8 +142,9 @@ with gr.Blocks(theme="soft") as demo:
             with gr.Row():
                 sum_choice = gr.Radio(choices = ["turna_summarization_mlsum", "turna_summarization_tr_news"], label ="Model")
                 sum_input = gr.Textbox(label = "Summarization Input")
-                sum_output = gr.Textbox(label = "Summarization Output")
                 sum_submit = gr.Button()
+                sum_output = gr.Textbox(label = "Summarization Output")
+                
                 sum_submit.click(summarize, inputs=[sum_input, sum_choice], outputs=sum_output)
             sum_examples = gr.Examples(examples = long_text, inputs = [sum_input, sum_choice], outputs=sum_output,  fn=summarize)
 demo.launch()