| 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 = [["I made education free for students", "education, cost, needs, future"]] |
|
|
| demo = gr.Interface(fn=zeroShotClassification, inputs=[gr.Textbox(label="Input"), gr.Textbox(label="Candidate Labels")], outputs=gr.Label(label="Classification"), title="Umer Text Classification", examples=examples,) |
| demo.launch(True) |
