add dog photos and app.py

app.py

@@ -0,0 +1,69 @@
+import gradio as gr
+import tensorflow as tf 
+
+# Load model
+model_save_path = "dog_vision_model_demo.keras"
+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 dogs in images with deep learning. Model trained with TensorFlow/Keras."
+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.jpeg",
+                                    "dog-photo-4.jpeg"],
+                         title=interface_title,
+                         description=interface_description)
+interface.launch(debug=True)