Loaded classifiers apriori

app.py

@@ -19,6 +19,82 @@ hypothesis_templates = {'en': 'This example is {}.', # English
                         'tr': 'Bu örnek {}.', # Turkish
                         'no': 'Dette eksempelet er {}.'} # Norsk
 
+classifiers = {'en': pipeline("zero-shot-classification", hypothesis_template=hypothesis_templates['en'],
+                              model=models['en']),
+               'de': pipeline("zero-shot-classification", hypothesis_template=hypothesis_templates['de'],
+                              model=models['de']),
+               'es': pipeline("zero-shot-classification", hypothesis_template=hypothesis_templates['es'],
+                              model=models['es']),
+               'it': pipeline("zero-shot-classification", hypothesis_template=hypothesis_templates['it'],
+                              model=models['it']),
+               'ru': pipeline("zero-shot-classification", hypothesis_template=hypothesis_templates['ru'],
+                              model=models['ru']),
+               'tr': pipeline("zero-shot-classification", hypothesis_template=hypothesis_templates['tr'],
+                              model=models['tr']),
+               'no': pipeline("zero-shot-classification", hypothesis_template=hypothesis_templates['no'],
+                              model=models['no'])}
+
+def prep_examples():
+    example_text1 = "Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Most \
+    people who fall sick with COVID-19 will experience mild to moderate symptoms and recover without special treatment. \
+    However, some will become seriously ill and require medical attention."
+    example_labels1 = "business,health related,politics,climate change"
+
+    example_text2 = "Elephants are"
+    example_labels2 = "big,small,strong,fast,carnivorous"
+
+    example_text3 = "Elephants"
+    example_labels3 = "are big,can be very small,generally not strong enough,are faster than you think"
+
+    example_text4 = "Dogs are man's best friend"
+    example_labels4 = "positive,negative,neutral"
+
+    example_text5 = "Amar sonar bangla ami tomay bhalobasi"
+    example_labels5 = "bhalo,kharap"
+
+    example_text6 = "Letzte Woche gab es einen Selbstmord in einer nahe gelegenen kolonie"
+    example_labels6 = "verbrechen,tragödie,stehlen"
+
+    example_text7 = "El autor se perfila, a los 50 años de su muerte, como uno de los grandes de su siglo"
+    example_labels7 = "cultura,sociedad,economia,salud,deportes"
+
+    example_text8 = "Россия в среду заявила, что военные учения в аннексированном Москвой Крыму закончились \
+    и что солдаты возвращаются в свои гарнизоны, на следующий день после того, как она объявила о первом выводе \
+    войск от границ Украины."
+    example_labels8 = "новости,комедия"
+
+    example_text9 = "I quattro registi - Federico Fellini, Pier Paolo Pasolini, Bernardo Bertolucci e Vittorio De Sica - \
+    hanno utilizzato stili di ripresa diversi, ma hanno fortemente influenzato le giovani generazioni di registi."
+    example_labels9 = "cinema,politica,cibo"
+
+    example_text10 = "Ja, vi elsker dette landet,\
+    som det stiger frem,\
+    furet, værbitt over vannet,\
+    med de tusen hjem.\
+    Og som fedres kamp har hevet\
+    det av nød til seir"
+    example_labels10 = "helse,sport,religion,mat,patriotisme og nasjonalisme"
+
+    example_text11 = "Şampiyonlar Ligi’nde 5. hafta oynanan karşılaşmaların ardından sona erdi. Real Madrid, \
+    Inter ve Sporting oynadıkları mücadeleler sonrasında Son 16 turuna yükselmeyi başardı. \
+    Gecenin dev mücadelesinde ise Manchester City, PSG’yi yenerek liderliği garantiledi."
+    example_labels11 = "dünya,ekonomi,kültür,siyaset,spor,teknoloji"
+
+    examples = [
+        [example_text1, example_labels1],
+        [example_text2, example_labels2],
+        [example_text3, example_labels3],
+        [example_text4, example_labels4],
+        [example_text5, example_labels5],
+        [example_text6, example_labels6],
+        [example_text7, example_labels7],
+        [example_text8, example_labels8],
+        [example_text9, example_labels9],
+        [example_text10, example_labels10],
+        [example_text11, example_labels11]]
+
+    return examples
+
 def detect_lang(sequence, labels):
     DetectorFactory.seed = 0
     seq_lang = 'en'
@@ -57,14 +133,10 @@ def detect_lang(sequence, labels):
 
     return seq_lang
 
-
 def sequence_to_classify(sequence, labels):
-    label_clean = str(labels).split(",")
+    classifier = classifiers[detect_lang(sequence, labels)]
 
-    lang = detect_lang(sequence, labels)
-    classifier = pipeline("zero-shot-classification",
-                          hypothesis_template=hypothesis_templates[lang],
-                          model=models[lang])
+    label_clean = str(labels).split(",")
     response = classifier(sequence, label_clean, multi_label=True)
 
     predicted_labels = response['labels']
@@ -77,75 +149,19 @@ def sequence_to_classify(sequence, labels):
 
     return clean_output
 
-example_text1 = "Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Most \
-people who fall sick with COVID-19 will experience mild to moderate symptoms and recover without special treatment. \
-However, some will become seriously ill and require medical attention."
-example_labels1 = "business,health related,politics,climate change"
-
-example_text2 = "Elephants are"
-example_labels2 = "big,small,strong,fast,carnivorous"
-
-example_text3 = "Elephants"
-example_labels3 = "are big,can be very small,generally not strong enough,are faster than you think"
-
-example_text4 = "Dogs are man's best friend"
-example_labels4 = "positive,negative,neutral"
-
-example_text5 = "Amar sonar bangla ami tomay bhalobasi"
-example_labels5 = "bhalo,kharap"
-
-example_text6 = "Letzte Woche gab es einen Selbstmord in einer nahe gelegenen kolonie"
-example_labels6 = "verbrechen,tragödie,stehlen"
-
-example_text7 = "El autor se perfila, a los 50 años de su muerte, como uno de los grandes de su siglo"
-example_labels7 = "cultura,sociedad,economia,salud,deportes"
-
-example_text8 = "Россия в среду заявила, что военные учения в аннексированном Москвой Крыму закончились \
-и что солдаты возвращаются в свои гарнизоны, на следующий день после того, как она объявила о первом выводе \
-войск от границ Украины."
-example_labels8 = "новости,комедия"
-
-example_text9 = "I quattro registi - Federico Fellini, Pier Paolo Pasolini, Bernardo Bertolucci e Vittorio De Sica - \
-hanno utilizzato stili di ripresa diversi, ma hanno fortemente influenzato le giovani generazioni di registi."
-example_labels9 = "cinema,politica,cibo"
-
-example_text10 = "Ja, vi elsker dette landet,\
-som det stiger frem,\
-furet, værbitt over vannet,\
-med de tusen hjem.\
-Og som fedres kamp har hevet\
-det av nød til seir"
-example_labels10 = "helse,sport,religion,mat,patriotisme og nasjonalisme"
-
-example_text11 = "Şampiyonlar Ligi’nde 5. hafta oynanan karşılaşmaların ardından sona erdi. Real Madrid, \
-Inter ve Sporting oynadıkları mücadeleler sonrasında Son 16 turuna yükselmeyi başardı. \
-Gecenin dev mücadelesinde ise Manchester City, PSG’yi yenerek liderliği garantiledi."
-example_labels11 = "dünya,ekonomi,kültür,siyaset,spor,teknoloji"
-
 iface = gr.Interface(
     title="Multilingual Multi-label Zero-shot Classification",
     description="Currently supported languages are English, German, Spanish, Italian, Russian, Turkish, Norsk.",
     fn=sequence_to_classify,
-    inputs=[gr.inputs.Textbox(lines=20,
+    inputs=[gr.inputs.Textbox(lines=10,
         label="Please enter the text you would like to classify...",
         placeholder="Text here..."),
-        gr.inputs.Textbox(lines=5,
+        gr.inputs.Textbox(lines=2,
         label="Possible candidate labels (separated by comma)...",
         placeholder="Labels here separated by comma...")],
     outputs=gr.outputs.Label(num_top_classes=5),
     capture_session=True,
     #interpretation="default",
-    examples=[
-        [example_text1, example_labels1],
-        [example_text2, example_labels2],
-        [example_text3, example_labels3],
-        [example_text4, example_labels4],
-        [example_text5, example_labels5],
-        [example_text6, example_labels6],
-        [example_text7, example_labels7],
-        [example_text8, example_labels8],
-        [example_text9, example_labels9],
-        [example_text10, example_labels10],
-        [example_text11, example_labels11]]
-    )
+    examples=prep_examples())
+
 iface.launch()