Update app.py

app.py

@@ -23,15 +23,7 @@ nli_example = [["Bunu çok beğendim. Bunu çok sevdim."]]
 
 
 t2t_gen_model = pipeline(model="boun-tabi-LMG/TURNA", device=0) 
-# examples =t2t_example,  title="Text-to-Text Generation", description="Please enter an instruction with a prefix to generate.")
-summarization_model = pipeline(model="boun-tabi-LMG/turna_summarization_mlsum", device=0) 
-# examples =long_text, title="Summarization", description="TURNA fine-tuned on MLSUM. Enter a text to summarize below.")
-news_sum = pipeline(model="boun-tabi-LMG/turna_summarization_tr_news", device=0) 
-# examples =long_text, title="News Summarization", description="TURNA fine-tuned on News summarization. Enter a news to summarize.")
-paraphrasing = pipeline(model="boun-tabi-LMG/turna_paraphrasing_tatoeba", device=0) 
-# examples =long_text,title="Paraphrasing")
-paraphrasing_sub = pipeline(model="boun-tabi-LMG/turna_paraphrasing_opensubtitles", device=0) 
-# examples =long_text, title="Paraphrasing on Subtitles")
+
 
 ttc = pipeline(model="boun-tabi-LMG/turna_classification_ttc4900", device=0)
 # examples =long_text, title="Text Categorization")
@@ -42,18 +34,11 @@ title_gen = pipeline(model="boun-tabi-LMG/turna_title_generation_mlsum", device=
 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.")
 
-pos_imst = pipeline(model="boun-tabi-LMG/turna_pos_imst", device=0) 
-# title="Part of Speech Tagging", examples=ner_example,description="Enter a text to generate title to.")
+
 nli_model = pipeline(model="boun-tabi-LMG/turna_nli_nli_tr", device=0) 
-# title="NLI",examples=nli_example, description="Enter two texts to infer entailment.")
-pos_boun = pipeline(model="boun-tabi-LMG/turna_pos_boun", device=0) 
-# examples = ner_example, title="Part of Speech Tagging", description="Enter a text to tag parts of speech (POS).")
+
 stsb_model = pipeline(model="boun-tabi-LMG/turna_semantic_similarity_stsb_tr", device=0)
-# examples=nli_example, title="Semantic Similarity", description="Enter two texts in the input to assess semantic similarity.")
-ner_model = pipeline(model="boun-tabi-LMG/turna_ner_milliyet", device=0) 
-# title="NER WikiANN", examples=ner_example, description="Enter a text for NER.")
-ner_wikiann = pipeline(model="boun-tabi-LMG/turna_ner_wikiann", device=0) 
-#title="NER",examples=ner_example, description="Enter a text for NER.")
+
 
 
 @spaces.GPU
@@ -75,15 +60,19 @@ def t2t(input):
     return t2t_gen_model(input)
 
 @spaces.GPU
-def pos(input, boun=True):
-    if boun==True:
-        return pos_boun(input)
-    else:
+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) 
+    if model_choice=="turna_pos_imst":
         return pos_imst(input)
+    else:
+        return pos_boun(input)
 
 @spaces.GPU
-def ner(input, wikiann=True):
-    if wikiann==True:
+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)
     else:
         return ner_model(input)
@@ -91,6 +80,8 @@ def ner(input, wikiann=True):
 
 @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":
         return paraphrasing(input)
     else:
@@ -98,6 +89,8 @@ def paraphrase(input, model_choice="turna_paraphrasing_tatoeba"):
         
 @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)
     else:
@@ -108,8 +101,39 @@ def summarize(input, model_choice="turna_summarization_tr_news"):
 with gr.Blocks(theme="shivi/calm_seafoam") as demo:
     gr.Markdown("# TURNA 🐦")
     gr.Markdown(DESCRIPTION)
+    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():
+                pos_choice = gr.Radio(choices = ["turna_pos_imst", "turna_pos_boun"])
+                pos_input = gr.Text()
+                pos_output = gr.Text()
+                pos_submit = gr.Button()
+                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)
+    
+    with gr.Tab("NER"):
+        gr.Markdown("TURNA fine-tuned on named entity recognition. Enter text to parse named entities and pick the model.")
+        with gr.Column():
+            with gr.Row():
+                ner_choice = gr.Radio(choices = ["turna_ner_wikiann", "turna_ner_milliyet"])
+                ner_input = gr.Text()
+                ner_output = gr.Text()
+                ner_submit = gr.Button()
+                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"):
+        gr.Markdown("TURNA fine-tuned on paraphrasing. Enter text to paraphrase and pick the model.")
+        with gr.Column():
+            with gr.Row():
+                paraphrasing_choice = gr.Radio(choices = ["turna_paraphrasing_tatoeba", "turna_paraphrasing_opensubtitles"])
+                paraphrasing_input = gr.Text()
+                paraphrasing_output = gr.Text()
+                paraphrasing_submit = gr.Button()
+                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"):
-        gr.Markdown("TURNA fine-tuned on ummarization. Enter a news to summarize and pick the model.")
+        gr.Markdown("TURNA fine-tuned on summarization. Enter text to summarize and pick the model.")
         with gr.Column():
             with gr.Row():
                 sum_choice = gr.Radio(choices = ["turna_summarization_mlsum", "turna_summarization_tr_news"])
@@ -117,5 +141,5 @@ with gr.Blocks(theme="shivi/calm_seafoam") as demo:
                 sum_output = gr.Text()
                 sum_submit = gr.Button()
                 sum_submit.click(summarize, inputs=[sum_input, sum_choice], outputs=sum_output)
-                examples = gr.Examples(examples = long_text, inputs = [sum_input, sum_choice], outputs=sum_output, cache_examples=True, fn=summarize)
+                sum_examples = gr.Examples(examples = long_text, inputs = [sum_input, sum_choice], outputs=sum_output,  fn=summarize)
 demo.launch()