added zero shot

app.py

@@ -3,7 +3,8 @@ from transformers import pipeline
 
 get_completion = pipeline("summarization",model="sshleifer/distilbart-cnn-12-6")
 get_ner = pipeline("ner", model="dslim/bert-base-NER")
-get_caption = pipeline("image-to-text")
+get_zero = pipeline("zero-shot-classification", model="MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli")
+
 def summarize_text(input):
     output = get_completion(input)
     return output[0]['summary_text']
@@ -27,6 +28,17 @@ def named_entity_recognition(input):
     merged_output = merge_tokens(output)
     return {"text": input, "entities": output}
 
+def zero_shot_pred(text,check_labels):
+    output = get_zero(text,check_labels)
+    return output
+
+def label_score_dict(text,check_labels):
+    zero_shot_out = zero_shot_pred(text,check_labels)
+    out = {}
+    for i,j in zip(zero_shot_out['labels'],zero_shot_out['scores']):
+        out.update({i:j})
+    print(out)
+    return out
 
 interface_summarise = gr.Interface(fn=summarize_text,
  inputs=[gr.Textbox(label="Text to summarise", lines=5)],
@@ -45,11 +57,27 @@ interface_ner = gr.Interface(fn=named_entity_recognition,
                         "My name is Bose and I am a physicist living in Delhi"
                     ])
 
+interface_zero_shot=gr.Interface(fn=label_score_dict,
+                    inputs=[
+                        gr.Textbox(label="Text to classify", lines=2),
+                        gr.Textbox(label="Check for labels")
+                    ],
+                    outputs=gr.Label(num_top_classes=4),
+                    title="Zero-Shot Preds using DeBERTa-v3-base-mnli",
+                    description="Classify sentence on self defined target vars",
+                    examples=[
+                        ["Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.",
+                        "mobile, website, billing, account access"],
+                        # "My name is Bose and I am a physicist living in Delhi"
+                    ])
+
 demo = gr.TabbedInterface([
     interface_summarise, 
-    interface_ner],
+    interface_ner,
+    interface_zero_shot],
     ["Text Summary ",
-     "Named Entity Recognition"
+     "Named Entity Recognition",
+     "Zero Shot Classifications"
     ])
 
 if __name__ == "__main__":