File size: 2,855 Bytes
4e3dfa8 61d26d3 c29fb43 61d26d3 e3a0648 fb7313e e3a0648 fb7313e e3a0648 fb7313e 61d26d3 ae53435 4e3dfa8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 |
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"]]
css = """
footer {display:none !important}
.output-markdown{display:none !important}
.gr-button-primary {
z-index: 14;
height: 43px;
width: 130px;
left: 0px;
top: 0px;
padding: 0px;
cursor: pointer !important;
background: none rgb(17, 20, 45) !important;
border: none !important;
text-align: center !important;
font-family: Poppins !important;
font-size: 14px !important;
font-weight: 500 !important;
color: rgb(255, 255, 255) !important;
line-height: 1 !important;
border-radius: 12px !important;
transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important;
box-shadow: none !important;
}
.gr-button-primary:hover{
z-index: 14;
height: 43px;
width: 130px;
left: 0px;
top: 0px;
padding: 0px;
cursor: pointer !important;
background: none rgb(66, 133, 244) !important;
border: none !important;
text-align: center !important;
font-family: Poppins !important;
font-size: 14px !important;
font-weight: 500 !important;
color: rgb(255, 255, 255) !important;
line-height: 1 !important;
border-radius: 12px !important;
transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important;
box-shadow: rgb(0 0 0 / 23%) 0px 1px 7px 0px !important;
}
.hover\:bg-orange-50:hover {
--tw-bg-opacity: 1 !important;
background-color: rgb(229,225,255) !important;
}
.to-orange-200 {
--tw-gradient-to: rgb(37 56 133 / 37%) !important;
}
.from-orange-400 {
--tw-gradient-from: rgb(17, 20, 45) !important;
--tw-gradient-to: rgb(255 150 51 / 0);
--tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to) !important;
}
.group-hover\:from-orange-500{
--tw-gradient-from:rgb(17, 20, 45) !important;
--tw-gradient-to: rgb(37 56 133 / 37%);
--tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to) !important;
}
.group:hover .group-hover\:text-orange-500{
--tw-text-opacity: 1 !important;
color:rgb(37 56 133 / var(--tw-text-opacity)) !important;
}
"""
demo = gr.Interface(fn=zeroShotClassification, inputs=[gr.Textbox(label="Input"), gr.Textbox(label="Candidate Labels")], outputs=gr.Label(label="Classification"), title="Zero Shot Text Classification | Data Science Dojo", examples=examples, css=css)
demo.launch() |