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 = "
This application serves to classify Medicinal Plants
" # 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()