Add application file

app.py

@@ -1,4 +1,20 @@
 import gradio as gr
+from transformers import AutoFeatureExtractor, AutoTokenizer, SpeechEncoderDecoderModel
+import torch
+
+from transformers import pipeline
+classifier = pipeline("zero-shot-classification", model="NbAiLab/nb-bert-base-mnli")
+
+
+def sequence_to_classify(sequence, labels):
+    #sequence_to_classify = 'Folkehelseinstituttets mest optimistiske anslag er at alle voksne er ferdigvaksinert innen midten av september.'
+    #candidate_labels = ['politikk', 'helse', 'sport', 'religion']
+    hypothesis_template = 'Dette eksempelet er {}.'
+    #classifier(sequence_to_classify, candidate_labels, hypothesis_template=hypothesis_template, multi_class=True)
+    return classifier(sequence, labels, hypothesis_template=hypothesis_template, multi_class=True)
+
+    
+
 
 def greet(name):
     return "Hello " + name + "!!"
@@ -6,5 +22,12 @@ def greet(name):
 iface = gr.Interface(
     title = "Zero-shot Classification of Norwegian Text",
     description = "Demo of zero-shot classification using NB-Bert base model (Norwegian).",
-    fn=greet, inputs="text", outputs="text")
+    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=2, 
+        label="Possible candidate labels",
+        placeholder="labels here...")],  
+    outputs="text")
 iface.launch()