Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+from PIL import Image
+from transformers import pipeline
+
+def dog_classifier(dog_image):
+    image = Image.fromarray(dog_image)
+    dog_classifier = pipeline("image-classification", model='raffaelsiregar/dog-breeds-classification')
+    output = dog_classifier(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()