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import gradio as gr | |
from transformers import pipeline | |
# Load the image classification pipeline | |
pipe = pipeline("image-classification", model="imfarzanansari/skintelligent-acne") | |
# Define the prediction function | |
def classify_acne(image_url): | |
# Use the pipeline to classify the image from the URL | |
predictions = pipe(image_url) | |
# Extract the label and score from the predictions | |
label = predictions[0]['label'] | |
score = predictions[0]['score'] | |
return f"Grade: {label}, Confidence: {score:.2f}" | |
# Create a Gradio interface | |
interface = gr.Interface( | |
fn=classify_acne, # Prediction function | |
inputs=gr.Textbox(label="Image URL"), # Input type: text (for image URL) | |
outputs="text", # Output type: text | |
title="Acne Level Classifier", # Title of the app | |
description="Enter the URL of an image to classify the acne levels." # Description | |
) | |
# Launch the app | |
if __name__ == "__main__": | |
interface.launch() | |