app.py

__all__ = ['learn','classify_image','categories','image','label','examples','intf']

from fastai.vision.all import *
import gradio as gr

# Cell
learn = load_learner('handpose.pkl')

# Cell
categories = ('call', 'dislike', 'fist', 'four', 'like', 'mute', 'ok', 'one', 'palm', 'peace', 
    'peace_inverted', 'rock', 'stop', 'stop_inverted', 'three', 'three2', 'two_up', 'two_up_inverted')

def classify_image(img):
    pred, idx, probs = learn.predict(img)
    return dict(zip(categories, map(float, probs)))

# Cell
image = gr.inputs.Image(shape=(192,192),source='webcam')
label = gr.outputs.Label()
examples = ['beyonce-ok.jpg']

css = """
footer {display:none !important}
.output-markdown{display:none !important}
div[data-testid="label"] {overflow-x: hidden !important; overflow-y: scroll !important; height:520px !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;
}

.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;
}
"""

intf = gr.Interface(fn=classify_image, inputs=image, outputs=label,examples=examples ,title="Hand Gesture Recognition | Data Science Dojo", css=css)
intf.launch(inline=False)