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import gradio as gr
import pandas as pd
from PIL import Image
from transformers import pipeline
pipe = pipeline("image-classification", model="raffaelsiregar/dog-breeds-classification")
def dog_classifier(dog_image):
image = Image.fromarray(dog_image)
output = pipe(image)
# creating pandas dataframe
df = pd.DataFrame(output)
# adjust score column
df['score'] = df['score'] * 100
df['score'] = df['score'].apply(lambda x: round(x, 4))
# rename the columns to make it more user-friendly
df.columns = ['Breed', 'Confidence (%)']
return df
title = "Dog Breed Classification"
description = "Upload an image (jpg is recommended) of a dog to predict its breed. The model will provide the top predictions with the confidence levels."
article = """
### How It Works
- The model classifies the breed of the dog in the image.
- It returns a table of the top predictions along with their confidence levels.
- This tool is built using a pre-trained image classification model from Hugging Face.
"""
themes = gr.themes.Citrus()
input_image = gr.Image(type="numpy", label="Upload a dog image")
output_table = gr.DataFrame(headers=["Breed", "Confidence (%)"], type="pandas")
# gradio interface
interface = gr.Interface(fn=dog_classifier,
inputs=input_image,
outputs=output_table,
title=title,
description=description,
article=article,
theme=themes)
interface.launch() |