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import gradio as gr | |
from transformers import pipeline | |
classifier = pipeline("text-classification",model='bhadresh-savani/distilbert-base-uncased-emotion', return_all_scores=True) | |
def detect_emotions(emotion_input): | |
prediction = classifier(emotion_input,) | |
output = {} | |
for emotion in prediction[0]: | |
output[emotion["label"]] = emotion["score"] | |
return output | |
examples = [["I am excited to announce that I have been promoted"], ["Sorry for the late reply"]] | |
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=detect_emotions, inputs=gr.Textbox(placeholder="Enter text here", label="Input"), outputs=gr.Label(label="Emotion"), title="Emotion Detector | Data Science Dojo", examples=examples, css=css) | |
demo.launch() |