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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+dog-photo-3.png filter=lfs diff=lfs merge=lfs -text

app.py

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+import gradio as gr
+import tensorflow as tf 
+
+# Load model
+model_save_path = "dog_vision_model.h5"
+loaded_model_for_demo = tf.keras.models.load_model(model_save_path)
+
+# Load labels
+with open("stanford_dogs_class_names.txt", "r") as f:
+  class_names = [line.strip() for line in f.readlines()]
+
+# Create prediction function
+def pred_on_custom_image(image, # input image (preprocessed by Gradio's Image input to be numpy.array)
+                         model: tf.keras.Model =loaded_model_for_demo,  # Trained TensorFlow model for prediction
+                         target_size: int = 224,  # Desired size of the image for input to the model
+                         class_names: list = class_names): # List of class names
+  """
+  Loads an image, preprocesses it, makes a prediction using a provided model,
+  and returns a dictionary of prediction probabilities per class name.
+
+  Args:
+      image: Input image.
+      model: Trained TensorFlow model for prediction.
+      target_size (int, optional): Desired size of the image for input to the model. Defaults to 224.
+      class_names (list, optional): List of class names for plotting. Defaults to None.
+
+  Returns:
+     Dict[str: float]: A dictionary of string class names and their respective prediction probability.
+  """
+
+  # Note: gradio.inputs.Image handles opening the image
+  # # Prepare and load image
+  # custom_image = tf.keras.utils.load_img(
+  #   path=image_path,
+  #   color_mode="rgb",
+  #   target_size=target_size,
+  # )
+
+  # Create resizing layer to resize the image
+  resize = tf.keras.layers.Resizing(height=target_size,
+                                    width=target_size)
+
+  # Turn the image into a tensor and resize it
+  custom_image_tensor = resize(tf.keras.utils.img_to_array(image))
+
+  # Add a batch dimension to the target tensor (e.g. (224, 224, 3) -> (1, 224, 224, 3))
+  custom_image_tensor = tf.expand_dims(custom_image_tensor, axis=0)
+
+  # Make a prediction with the target model
+  pred_probs = model.predict(custom_image_tensor)[0]
+
+  # Predictions get returned as a dictionary of {label: pred_prob}
+  pred_probs_dict = {class_names[i]: float(pred_probs[i]) for i in range(len(class_names))}
+
+  return pred_probs_dict
+
+# Create Gradio interface
+interface_title = "Dog Vision 🐶"
+interface_description = """
+Identify different dog's breeds in images with Deep Learning. Model trained with TensorFlow/Keras.
+
+## Links
+
+* Original dataset: http://vision.stanford.edu/aditya86/ImageNetDogs/
+"""
+interface = gr.Interface(fn=pred_on_custom_image,
+                         inputs=gr.Image(),
+                         outputs=gr.Label(num_top_classes=3),
+                         examples=["dog-photo-1.jpeg", 
+                                   "dog-photo-2.jpeg",
+                                   "dog-photo-3.jpg"],
+                         title=interface_title,
+                         description=interface_description)
+interface.launch(debug=True)

dog_vision_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a34d2636e20596a4fcf23bcc00315d81af01ab87fde43bbf8e7bf249b7d4f2f5
+size 23434684

requirements.txt

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+tensorflow==2.15.0

stanford_dogs_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