Add application file

app.py

@@ -0,0 +1,54 @@
+import gradio as gr
+gr.Interface.load("huggingface/NDugar/debertav3-mnli-snli-anli").launch()
+
+'''
+import gradio as gr
+import datetime
+from transformers import pipeline
+
+models = {'norsk': 'NbAiLab/nb-bert-base-mnli', #'english': 'DeepPavlov/xlm-roberta-large-en-ru-mnli',
+English: Narsil/deberta-large-mnli-zero-cls
+German: Sahajtomar/German_Zeroshot
+Spanish: Recognai/zeroshot_selectra_medium
+Italian: joeddav/xlm-roberta-large-xnl}
+classifier = pipeline("zero-shot-classification", model="NbAiLab/nb-bert-base-mnli")
+
+
+def sequence_to_classify(sequence, labels):
+    hypothesis_template = 'Dette eksempelet er {}.'
+    label_clean = str(labels).split(",")
+    response = classifier(sequence, label_clean, hypothesis_template=hypothesis_template, multi_class=True)
+    predicted_labels = response['labels']
+    predicted_scores = response['scores']
+    clean_output = {idx: float(predicted_scores.pop(0)) for idx in predicted_labels}
+    print("Date:{} , Sequece:{}, Labels: {}".format(
+        str(datetime.datetime.now()),
+        sequence,
+        predicted_labels)
+    )
+    return clean_output
+
+example_text1="Folkehelseinstituttets mest optimistiske anslag er at alle voksne er ferdigvaksinert innen midten av september."
+example_labels1="politikk,helse,sport,religion"
+example_text2="Kutt smør i terninger, og la det temperere seg litt mens deigen elter. Ha hvetemel, sukker, gjær, salt og kardemomme i en bakebolle til kjøkkenmaskin. Bruker du fersk gjær kan du smuldre gjæren i bollen, eller røre den ut i melken. Alt vil ettehvert blande seg godt, så begge deler er like bra."
+example_labels2="helse,sport,religion, mat"
+
+iface = gr.Interface(
+    title = "Zero-shot Classification of Norwegian Text",
+    description = "Demo of zero-shot classification using NB-Bert base model (Norwegian).",
+    fn=sequence_to_classify,
+    inputs=[gr.inputs.Textbox(lines=2,
+        label="Write a norwegian text you would like to classify...",
+        placeholder="Text here..."),
+        gr.inputs.Textbox(lines=10,
+        label="Possible candidate labels",
+        placeholder="labels here...")],
+    outputs=gr.outputs.Label(num_top_classes=3),
+    capture_session=True,
+    interpretation="default"
+    ,examples=[
+        [example_text1, example_labels1],
+        [example_text2, example_labels2]
+    ])
+iface.launch()
+'''