Update app.py

app.py

@@ -12,7 +12,7 @@ This model is based on an encoder-decoder T5 architecture with 1.1B parameters.
 
 For more details, please refer to our paper.
 
-Note: First inference might take time as the models are downloaded on-the-go.
+**Note:** First inference might take time as the models are downloaded on-the-go.
 
 """
 
@@ -24,44 +24,31 @@ t2t_example = [["Paraphrase: Bu üründen çok memnun kaldım."]]
 nli_example = [["Bunu çok beğendim. Bunu çok sevdim."]]
 
 
-#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) 
-#title_gen = pipeline(model="boun-tabi-LMG/turna_title_generation_mlsum", 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)
-
+    
     if model_choice=="turna_nli_nli_tr":
+        nli_model = pipeline(model="boun-tabi-LMG/turna_nli_nli_tr", device=0) 
         return nli_model(input)[0]["generated_text"]
     else:
-        return stsb_model(input)[0]["generated_text"]
+        stsb_model = pipeline(model="boun-tabi-LMG/turna_semantic_similarity_stsb_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)
-
-    if model_choice=="turna_nli_nli_tr":
-        return nli_model(input)[0]["generated_text"]
-    else:
         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":
+        sentiment_model = pipeline(model="boun-tabi-LMG/turna_classification_17bintweet_sentiment", device=0) 
+
         return sentiment_model(input)[0]["generated_text"]
     else:
+        product_reviews = pipeline(model="boun-tabi-LMG/turna_classification_tr_product_reviews", device=0) 
+
         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"): 
@@ -84,20 +71,22 @@ def ner(input, model_choice="turna_ner_wikiann"):
 
 @spaces.GPU
 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":
+        paraphrasing = pipeline(model="boun-tabi-LMG/turna_paraphrasing_tatoeba", device=0) 
         return paraphrasing(input)[0]["generated_text"]
     else:
+        paraphrasing_sub = pipeline(model="boun-tabi-LMG/turna_paraphrasing_opensubtitles", device=0) 
+
         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":
+        news_sum = pipeline(model="boun-tabi-LMG/turna_summarization_tr_news", device=0) 
+
         return news_sum(input)[0]["generated_text"]
     else:
+        summarization_model = pipeline(model="boun-tabi-LMG/turna_summarization_mlsum", device=0) 
         return summarization_model(input)[0]["generated_text"]
 
 
@@ -105,15 +94,44 @@ def summarize(input, model_choice="turna_summarization_tr_news"):
 with gr.Blocks(theme="soft") as demo:
     gr.Markdown("# TURNA 🐦")
     gr.Markdown(DESCRIPTION)
+    
+    with gr.Tab("Sentiment Analysis"):
+        gr.Markdown("TURNA fine-tuned on sentiment analysis. Enter text to analyse sentiment and pick the model (tweets or product reviews).")
+        with gr.Column():
+            with gr.Row():
+                with gr.Column():
+                    sentiment_choice = gr.Radio(choices = ["turna_classification_17bintweet_sentiment", "turna_classification_tr_product_reviews"], label ="Model", value="turna_classification_17bintweet_sentiment")
+                    sentiment_input = gr.Textbox(label="Sentiment Analysis Input")
+                
+                    sentiment_submit = gr.Button()
+                sentiment_output = gr.Textbox(label="Sentiment Analysis Output")
+                sentiment_submit.click(nli, inputs=[pos_input, nli_choice], outputs=pos_output)
+            sentiment_examples = gr.Examples(examples = sentiment_example, inputs = [sentiment_input, sentiment_choice], outputs=sentiment_output, fn=sentiment)
+    
+    
+    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.")
+        with gr.Column():
+            with gr.Row():
+                with gr.Column():
+                    nli_choice = gr.Radio(choices = ["turna_nli_nli_tr", "turna_semantic_similarity_stsb_tr"], label ="Model", value="turna_nli_nli_tr")
+                    nli_input = gr.Textbox(label="NLI Input")
+                
+                    nli_submit = gr.Button()
+                nli_output = gr.Textbox(label="NLI Output")
+                nli_submit.click(nli, inputs=[pos_input, nli_choice], outputs=pos_output)
+            nli_examples = gr.Examples(examples = nli_example, inputs = [nli_input, nli_choice], outputs=nli_output, fn=nli)
+    
     with gr.Tab("POS"):
         gr.Markdown("TURNA fine-tuned on part-of-speech-tagging. Enter text to parse parts of speech and pick the model.")
         with gr.Column():
             with gr.Row():
                 with gr.Column():
-                    pos_choice = gr.Radio(choices = ["turna_pos_imst", "turna_pos_boun"], label ="Model")
+                    pos_choice = gr.Radio(choices = ["turna_pos_imst", "turna_pos_boun"], label ="Model", value="turna_pos_imst")
                     pos_input = gr.Textbox(label="POS Input")
-                    pos_output = gr.Textbox(label="POS Output")
+                
                     pos_submit = gr.Button()
+                pos_output = gr.Textbox(label="POS Output")
                 pos_submit.click(pos, inputs=[pos_input, pos_choice], outputs=pos_output)
             pos_examples = gr.Examples(examples = ner_example, inputs = [pos_input, pos_choice], outputs=pos_output, fn=pos)
     
@@ -122,7 +140,7 @@ with gr.Blocks(theme="soft") as demo:
         with gr.Column():
             with gr.Row():
                 with gr.Column():
-                    ner_choice = gr.Radio(choices = ["turna_ner_wikiann", "turna_ner_milliyet"], label ="Model")
+                    ner_choice = gr.Radio(choices = ["turna_ner_wikiann", "turna_ner_milliyet"], label ="Model", value="turna_ner_wikiann")
                     ner_input = gr.Textbox(label="NER Input")
                     ner_submit = gr.Button()
                 ner_output = gr.Textbox(label="NER Output")
@@ -134,10 +152,10 @@ with gr.Blocks(theme="soft") as demo:
         with gr.Column():
             with gr.Row():
                 with gr.Column():
-                    paraphrasing_choice = gr.Radio(choices = ["turna_paraphrasing_tatoeba", "turna_paraphrasing_opensubtitles"], label ="Model")
+                    paraphrasing_choice = gr.Radio(choices = ["turna_paraphrasing_tatoeba", "turna_paraphrasing_opensubtitles"], label ="Model", value="turna_paraphrasing_tatoeba")
                     paraphrasing_input = gr.Textbox(label = "Paraphrasing Input")
                     paraphrasing_submit = gr.Button()
-            paraphrasing_output = gr.Text(label="Paraphrasing Output")
+                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)
@@ -146,7 +164,7 @@ with gr.Blocks(theme="soft") as demo:
         with gr.Column():
             with gr.Row():
                 with gr.Column():
-                    sum_choice = gr.Radio(choices = ["turna_summarization_mlsum", "turna_summarization_tr_news"], label ="Model")
+                    sum_choice = gr.Radio(choices = ["turna_summarization_mlsum", "turna_summarization_tr_news"], label ="Model", value="turna_summarization_mlsum")
                     sum_input = gr.Textbox(label = "Summarization Input")
                     sum_submit = gr.Button()
                 sum_output = gr.Textbox(label = "Summarization Output")