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app.py

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+import gradio as gr
+from transformers import pipeline
+
+classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
+
+def zeroShotClassification(text_input, candidate_labels):
+  labels = [label.strip(' ') for label in candidate_labels.split(',')]
+  output = {}
+  prediction = classifier(text_input, labels)
+  for i in range(len(prediction['labels'])):
+    output[prediction['labels'][i]] = prediction['scores'][i]
+  return output
+
+examples = [["One day I will see the world", "travel, live, die, future"]]
+
+demo = gr.Interface(fn=zeroShotClassification, inputs=[gr.Textbox(label="Input"), gr.Textbox(label="Candidate Labels")], outputs=gr.Label(label="Classification"), examples=examples)
+demo.launch()

requirements.txt

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+gradio
+torch
+timm
+sentencepiece
+transformers