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import gradio as gr | |
from transformers import pipeline | |
# Load the model pipeline | |
pipe = pipeline("image-classification", "dima806/medicinal_plants_image_detection") | |
# Define the image classification function | |
def image_classifier(image): | |
# Perform image classification | |
outputs = pipe(image) | |
# Get the label of the first result | |
output_text = outputs[0]['label'] | |
return output_text | |
# Define app title and description with HTML formatting | |
title = "<h1 style='text-align: center; color: #4CAF50;'>Image Classification</h1>" | |
description = "<p style='text-align: center; font-size: 18px;'>This application serves to classify Medicinal Plants </p>" | |
# Define custom CSS styles for the Gradio app | |
custom_css = """ | |
.gradio-interface { | |
max-width: 600px; | |
margin: auto; | |
border-radius: 10px; | |
box-shadow: 0px 0px 10px rgba(0, 0, 0, 0.1); | |
} | |
.title-container { | |
padding: 20px; | |
background-color: #f0f0f0; | |
border-top-left-radius: 10px; | |
border-top-right-radius: 10px; | |
} | |
.description-container { | |
padding: 20px; | |
} | |
""" | |
# Launch the Gradio interface with custom HTML and CSS | |
demo = gr.Interface(fn=image_classifier, inputs=gr.Image(type="pil"), outputs="textbox", title=title, description=description, | |
theme="gstaff/sketch", css=custom_css, | |
) | |
demo.launch() | |