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app.py

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+import gradio as gr
+import os
+import tensorflow
+
+from typing import Tuple, Dict
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+from timeit import default_timer as timer
+
+
+# Setup class names
+with open('class_names.txt') as f:
+    class_names = [breed.strip() for breed in f.readlines()]
+
+# Load the pre-trained EfficientNetV2 model
+effnet = tensorflow.keras.models.load_model('demo/dog_breed_classifier/dog_breed_effnet_augmentation.h5')
+
+# Get the preprocessing function for EfficientNetV2
+effnet_preprocess_input = tensorflow.keras.applications.efficientnet_v2.preprocess_input
+
+# Create examples list from "examples/" directory
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+## `predict` function
+def predict(img) -> Tuple[Dict, float]:
+    """
+    Transforms and performs a prediction on an image and returns prediction and time taken.
+
+    Args:
+        image_path (str): Path to the input image.
+
+    Returns:
+        Tuple[Dict, float]: A tuple containing a dictionary of class labels and prediction probabilities
+        and the prediction time.
+    """
+    # Start the timer
+    start_time = timer()
+
+    # Open the image using PIL
+    img = img.resize((224, 224))  # Resize the image to the model's expected input size
+
+    # Convert the image to a NumPy array
+    x = np.array(img)
+    x = effnet_preprocess_input(x)
+
+    # Add a batch dimension
+    x = tf.expand_dims(x, axis=0)
+
+    # Pass the image through the model
+    predictions = effnet(x)
+
+    top_classes_indices = np.argsort(predictions[0])[::-1][:3]  # Get the indices of top 3 classes
+    top_classes = [class_names[i] for i in top_classes_indices]  # Get the class names of top 3 classes
+    top_probabilities = [predictions[0][index] for index in top_classes_indices] * 100  # Get the probabilities of top 3 classes
+
+    # Create a dictionary of class labels and prediction probabilities
+    pred_labels_and_probs = {top_classes[i]: float(top_probabilities[i]) for i in range(len(top_classes_indices))}
+
+    # Calculate the prediction time
+    pred_time = round(timer() - start_time, 5)
+
+    # Return the prediction dictionary and prediction time
+    return pred_labels_and_probs, pred_time
+
+
+# Create title, description, and article strings
+title = "🐶 Dog Breeds Classifier 🐾"
+description = "🚀 An EfficientNetV2S feature extractor computer vision model to classify images of 120 different breeds. 📸"
+article = "🌟 Created at [GitHub](https://github.com/adinmg/dog_breed_classifier)."
+
+# Create the Gradio demo
+demo = gr.Interface(
+    fn=predict,  # mapping function from input to output
+    inputs=gr.Image(type="pil"),
+    outputs=[
+        gr.Label(num_top_classes=3, label="Predictions"),  # what are the outputs?
+        gr.Number(label="Prediction time (s)"),
+    ],
+    examples=example_list,
+    title=title,
+    description=description,
+    article=article,
+)
+
+# Launch the demo!
+demo.launch()

class_names.txt

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+Affenpinscher
+Afghan_hound
+African_hunting_dog
+Airedale
+American_staffordshire_terrier
+Appenzeller
+Australian_terrier
+Basenji
+Basset
+Beagle
+Bedlington_terrier
+Bernese_mountain_dog
+Black-and-tan_coonhound
+Blenheim_spaniel
+Bloodhound
+Bluetick
+Border_collie
+Border_terrier
+Borzoi
+Boston_bull
+Bouvier_des_flandres
+Boxer
+Brabancon_griffon
+Briard
+Brittany_spaniel
+Bull_mastiff
+Cairn
+Cardigan
+Chesapeake_bay_retriever
+Chihuahua
+Chow
+Clumber
+Cocker_spaniel
+Collie
+Curly-coated_retriever
+Dandie_dinmont
+Dhole
+Dingo
+Doberman
+English_foxhound
+English_setter
+English_springer
+Entlebucher
+Eskimo_dog
+Flat-coated_retriever
+French_bulldog
+German_shepherd
+German_short-haired_pointer
+Giant_schnauzer
+Golden_retriever
+Gordon_setter
+Great_dane
+Great_pyrenees
+Greater_swiss_mountain_dog
+Groenendael
+Ibizan_hound
+Irish_setter
+Irish_terrier
+Irish_water_spaniel
+Irish_wolfhound
+Italian_greyhound
+Japanese_spaniel
+Keeshond
+Kelpie
+Kerry_blue_terrier
+Komondor
+Kuvasz
+Labrador_retriever
+Lakeland_terrier
+Leonberg
+Lhasa
+Malamute
+Malinois
+Maltese_dog
+Mexican_hairless
+Miniature_pinscher
+Miniature_poodle
+Miniature_schnauzer
+Newfoundland
+Norfolk_terrier
+Norwegian_elkhound
+Norwich_terrier
+Old_english_sheepdog
+Otterhound
+Papillon
+Pekinese
+Pembroke
+Pomeranian
+Pug
+Redbone
+Rhodesian_ridgeback
+Rottweiler
+Saint_bernard
+Saluki
+Samoyed
+Schipperke
+Scotch_terrier
+Scottish_deerhound
+Sealyham_terrier
+Shetland_sheepdog
+Shih-tzu
+Siberian_husky
+Silky_terrier
+Soft-coated_wheaten_terrier
+Staffordshire_bullterrier
+Standard_poodle
+Standard_schnauzer
+Sussex_spaniel
+Tibetan_mastiff
+Tibetan_terrier
+Toy_poodle
+Toy_terrier
+Vizsla
+Walker_hound
+Weimaraner
+Welsh_springer_spaniel
+West_highland_white_terrier
+Whippet
+Wire-haired_fox_terrier
+Yorkshire_terrier

requirements.txt

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+gradio==3.43.2
+tensorflow==2.13.0