lfs untrack non-binary files

.gitattributes

@@ -1 +1,3 @@
 *.h5 filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text

app.py

@@ -1,3 +1,88 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ac02a544c47ad74193e7d3cb2b8e3fe6f1ebd10db27e0b5bf9c1171248fa75ca
-size 2922
+### 1. Imports and class names setup ###
+import gradio as gr
+import os
+import tensorflow as tf
+
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+
+from helper import load_model
+
+# Setup class names
+with open("class_names.txt", "r") as f:
+    unique_breeds = [l.strip() for l in f.readlines()]
+
+# 2. Model generation and weight
+model = load_model("models/20230727-13521690480331-all-images.h5")
+
+
+# 3. Prefict function
+# Define image size
+IMG_SIZE = 224
+
+
+def process_image(image_path):
+    """
+    Takes an image file path and turns it into a Tensor.
+    """
+    # Read in image file
+    image = tf.io.read_file(image_path)
+    # Turn the jpeg image into numerical Tensor with 3 colour channels (Red, Green, Blue)
+    image = tf.image.decode_jpeg(image, channels=3)
+    # Convert the colour channel values from 0-225 values to 0-1 values
+    image = tf.image.convert_image_dtype(image, tf.float32)
+    # Resize the image to our desired size (224, 244)
+    image = tf.image.resize(image, size=[IMG_SIZE, IMG_SIZE])
+    return image
+
+
+def predict(img) -> Tuple[Dict, float]:
+    """Transforms and performs a prediction on img and returns prediction and time taken."""
+    # Start the timer
+    start_time = timer()
+
+    # Transform the target image and add a batch dimension
+    img = process_image(img)
+
+    img = tf.expand_dims(img, axis=0)
+
+    # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
+    pred_probs = model(img)
+
+    # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
+    pred_labels_and_probs = {
+        unique_breeds[i]: float(pred_probs[0][i]) for i in range(len(unique_breeds))
+    }
+
+    # 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
+
+
+# 4. Gradio app
+# Create title, description and article strings
+title = "120 Dog Breed Vision classifier 🐶🐩🐕🐕‍🦺"
+description = (
+    "An mobilenet feature extractor computer vision model to classify 120 dog breeds."
+)
+article = "Modele from [mobilenet](https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5)."
+example_list = example_list = [["examples/" + str(p)] for p in os.listdir("examples/")]
+
+# Create the Gradio demo
+demo = gr.Interface(
+    fn=predict,  # mapping function from input to output
+    inputs=gr.Image(type="filepath"),  # what are the inputs?
+    outputs=[
+        gr.Label(num_top_classes=3, label="Predictions"),  # what are the outputs?
+        gr.Number(label="Prediction time (s)"),
+    ],  # our fn has two outputs, therefore we have two outputs
+    examples=example_list,
+    title=title,
+    description=description,
+    article=article,
+)
+
+# Launch the demo!
+demo.launch(debug=False)

class_names.txt

@@ -1,3 +1,120 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9759a5ab8be6fb12c18da34f80c9286ec99a0e15fb0daef47295e57892e3f880
-size 1710
+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

conda/environment.yml

@@ -1,3 +1,34 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:726520e749db9592238db1348673aed1eeff031c0719949ed809ce5da3363695
-size 613
+name: tf
+channels:
+  - apple
+  - defaults
+  - conda-forge
+dependencies:
+  - pip=23.1.2
+  - python=3.10.12
+
+  - pip:
+      - gradio==3.39.0
+      - gradio-client==0.3.0
+
+      - huggingface-hub==0.16.4
+
+      - keras==2.13.1
+
+      - matplotlib==3.7.2
+      - numpy==1.24.3
+
+      - pandas==2.0.3
+      - requests==2.31.0
+
+      - tensorboard==2.13.0
+      - tensorboard-data-server==0.7.1
+      - tensorflow==2.13.0
+      - tensorflow-estimator==2.13.0
+      - tensorflow-hub==0.13.0
+      - tensorflow-macos==2.13.0
+      - tensorflow-metal==1.0.1
+
+      - tqdm==4.65.0
+
+prefix: /Users/yusali/miniforge3/envs/tf

helper.py

@@ -1,3 +1,31 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7055ce210b45cfc78c78fa5a7d3d1b937ea9bf825cc94314b14b8e1b9e514aab
-size 831
+import os
+import datetime
+
+import tensorflow as tf
+import tensorflow_hub as hub
+
+
+def save_model(model, suffix=None):
+    """
+    Saves a given model in a models directory and appends a suffix (str)
+    for clarity and reuse.
+    """
+    # Create model directory with current time
+    modeldir = os.path.join(
+        "../models", datetime.datetime.now().strftime("%Y%m%d-%H%M%s")
+    )
+    model_path = modeldir + "-" + suffix + ".h5"  # save format of model
+    print(f"Saving model to: {model_path}...")
+    model.save(model_path)
+    return model_path
+
+
+def load_model(model_path):
+    """
+    Loads a saved model from a specified path.
+    """
+    print(f"Loading saved model from: {model_path}")
+    model = tf.keras.models.load_model(
+        model_path, custom_objects={"KerasLayer": hub.KerasLayer}
+    )
+    return model