Get app functional locally

src/huggingface/handler.py

@@ -0,0 +1,38 @@
+from skimage.io import imread
+from io import BytesIO
+import numpy as np
+import urllib
+from tensorflow.keras.utils import array_to_img, img_to_array
+
+from PIL import Image
+
+def preprocess(img: np.ndarray) -> np.ndarray:
+    img = array_to_img(img, scale=False)
+    img = img.resize((224, 224))
+    img = img_to_array(img)
+    return img / 255.0
+
+
+def handle_url(url: str) -> np.ndarray:
+    try:
+        img_data = imread(url)
+    except Exception:
+        req = urllib.request.Request(url, headers={"User-Agent": "Magic Browser"})
+        con = urllib.request.urlopen(req)
+        img_data = imread(con)
+    processed_img = preprocess(img_data)
+    img_array = np.array([processed_img])
+    return img_array
+
+
+def read_imagefile(file):
+    file_bytes = BytesIO(file)
+    image = Image.open(file_bytes)
+    return image
+
+
+def handle_file(file) -> np.ndarray:
+    img_data = read_imagefile(file)
+    processed_img = preprocess(img_data)
+    img_array = np.array([processed_img])
+    return img_array

src/huggingface/labels.py

@@ -0,0 +1,103 @@
+CLASS_LABELS = [
+    "Apple pie",
+    "Baby back ribs",
+    "Baklava",
+    "Beef carpaccio",
+    "Beef tartare",
+    "Beet salad",
+    "Beignets",
+    "Bibimbap",
+    "Bread pudding",
+    "Breakfast burrito",
+    "Bruschetta",
+    "Caesar salad",
+    "Cannoli",
+    "Caprese salad",
+    "Carrot cake",
+    "Ceviche",
+    "Cheesecake",
+    "Cheese plate",
+    "Chicken curry",
+    "Chicken quesadilla",
+    "Chicken wings",
+    "Chocolate cake",
+    "Chocolate mousse",
+    "Churros",
+    "Clam chowder",
+    "Club sandwich",
+    "Crab cakes",
+    "Creme brulee",
+    "Croque madame",
+    "Cup cakes",
+    "Deviled eggs",
+    "Donuts",
+    "Dumplings",
+    "Edamame",
+    "Eggs benedict",
+    "Escargots",
+    "Falafel",
+    "Filet mignon",
+    "Fish and chips",
+    "Foie gras",
+    "French fries",
+    "French onion soup",
+    "French toast",
+    "Fried calamari",
+    "Fried rice",
+    "Frozen yogurt",
+    "Garlic bread",
+    "Gnocchi",
+    "Greek salad",
+    "Grilled cheese sandwich",
+    "Grilled salmon",
+    "Guacamole",
+    "Gyoza",
+    "Hamburger",
+    "Hot and sour soup",
+    "Hot dog",
+    "Huevos rancheros",
+    "Hummus",
+    "Ice cream",
+    "Lasagna",
+    "Lobster bisque",
+    "Lobster roll sandwich",
+    "Macaroni and cheese",
+    "Macarons",
+    "Miso soup",
+    "Mussels",
+    "Nachos",
+    "Omelette",
+    "Onion rings",
+    "Oysters",
+    "Pad thai",
+    "Paella",
+    "Pancakes",
+    "Panna cotta",
+    "Peking duck",
+    "Pho",
+    "Pizza",
+    "Pork chop",
+    "Poutine",
+    "Prime rib",
+    "Pulled pork sandwich",
+    "Ramen",
+    "Ravioli",
+    "Red velvet cake",
+    "Risotto",
+    "Samosa",
+    "Sashimi",
+    "Scallops",
+    "Seaweed salad",
+    "Shrimp and grits",
+    "Spaghetti bolognese",
+    "Spaghetti carbonara",
+    "Spring rolls",
+    "Steak",
+    "Strawberry shortcake",
+    "Sushi",
+    "Tacos",
+    "Takoyaki",
+    "Tiramisu",
+    "Tuna tartare",
+    "Waffles",
+    ]

src/huggingface/model.py

@@ -0,0 +1,4 @@
+from huggingface_hub import from_pretrained_keras
+
+
+MODEL = from_pretrained_keras("jsolow/grubguesser")

src/huggingface/predict.py

@@ -0,0 +1,23 @@
+from .model import MODEL
+from .handler import handle_file, handle_url
+from .labels import CLASS_LABELS
+
+
+
+def predict_model(img_array, n_top_guesses:int = 10):
+    class_prob = MODEL.predict(img_array)
+    top_values_index = (-class_prob).argsort()[0][:n_top_guesses]
+    top_guesses = [CLASS_LABELS[i].title() for i in top_values_index]
+    return top_guesses
+
+
+def predict_file(file):
+    handled_array = handle_file(file)
+    return predict_model(handled_array)
+
+
+
+def predict_url(url: str):
+    handled_array = handle_url(url)
+    return predict_model(handled_array)
+

src/labels.txt

@@ -1,101 +0,0 @@
-Apple pie
-Baby back ribs
-Baklava
-Beef carpaccio
-Beef tartare
-Beet salad
-Beignets
-Bibimbap
-Bread pudding
-Breakfast burrito
-Bruschetta
-Caesar salad
-Cannoli
-Caprese salad
-Carrot cake
-Ceviche
-Cheesecake
-Cheese plate
-Chicken curry
-Chicken quesadilla
-Chicken wings
-Chocolate cake
-Chocolate mousse
-Churros
-Clam chowder
-Club sandwich
-Crab cakes
-Creme brulee
-Croque madame
-Cup cakes
-Deviled eggs
-Donuts
-Dumplings
-Edamame
-Eggs benedict
-Escargots
-Falafel
-Filet mignon
-Fish and chips
-Foie gras
-French fries
-French onion soup
-French toast
-Fried calamari
-Fried rice
-Frozen yogurt
-Garlic bread
-Gnocchi
-Greek salad
-Grilled cheese sandwich
-Grilled salmon
-Guacamole
-Gyoza
-Hamburger
-Hot and sour soup
-Hot dog
-Huevos rancheros
-Hummus
-Ice cream
-Lasagna
-Lobster bisque
-Lobster roll sandwich
-Macaroni and cheese
-Macarons
-Miso soup
-Mussels
-Nachos
-Omelette
-Onion rings
-Oysters
-Pad thai
-Paella
-Pancakes
-Panna cotta
-Peking duck
-Pho
-Pizza
-Pork chop
-Poutine
-Prime rib
-Pulled pork sandwich
-Ramen
-Ravioli
-Red velvet cake
-Risotto
-Samosa
-Sashimi
-Scallops
-Seaweed salad
-Shrimp and grits
-Spaghetti bolognese
-Spaghetti carbonara
-Spring rolls
-Steak
-Strawberry shortcake
-Sushi
-Tacos
-Takoyaki
-Tiramisu
-Tuna tartare
-Waffles

src/model_client.py

@@ -9,45 +9,8 @@ from django.core.files.uploadedfile import InMemoryUploadedFile, TemporaryUpload
 
 import logging
 
+from huggingface.predict import predict_file, predict_url
 
-MODEL_ENDPOINT_URL = os.getenv("MODEL_ENDPOINT_URL", "https://0.0.0.0:2000")
-
-
-def try_make_request(request_kwargs, request_type: str):
-    try:
-        request_kwargs["timeout"] = 3
-        if request_type.lower() == "get":
-            response = requests.get(**request_kwargs)
-        elif request_type.lower() == "post":
-            response = requests.post(**request_kwargs)
-        else:
-            raise Exception("Request Type not Supported.  Only get, post supported.")
-
-        return json.loads(response.content)
-    except requests.exceptions.ConnectionError:
-        logging.warning("Failed Model prediction", exc_info=True)
-        return ["Image Failed to Predict", "Try Another Image", "", "", ""]
-    except Exception:
-        logging.warning("Failed Model prediction", exc_info=True)
-        return ["Image Failed to Predict", "Try Another Image", "", "", ""]
-
-
-def predict_url(url: str) -> List[str]:
-    params = {"url": url}
-    headers = {"content-type": "application/json", "Accept-Charset": "UTF-8"}
-    request_url = urljoin(MODEL_ENDPOINT_URL, "predict_url")
-    request_kwargs = dict(url=request_url, params=params, headers=headers)
-    return try_make_request(request_kwargs, "get")
-
-
-def predict_file(image_file) -> List[str]:
-    image_file.seek(0)
-    file_ob = {
-        "upload_file": (image_file.name, image_file.read(), image_file.content_type)
-    }
-    request_url = urljoin(MODEL_ENDPOINT_URL, "predict_file")
-    request_kwargs = dict(url=request_url, files=file_ob)
-    return try_make_request(request_kwargs, "post")
 
 
 def get_color_labels(guesses: List[str], actual_label: Optional[str]) -> List[str]:
@@ -66,7 +29,8 @@ def url_image_vars(
     elif isinstance(input_img, str):
         top_guesses = predict_url(input_img)
     elif isinstance(input_img, (InMemoryUploadedFile, TemporaryUploadedFile)):
-        top_guesses = predict_file(input_img)
+        input_img.seek(0)
+        top_guesses = predict_file(input_img.read())
     else:
         logging.error(f"Unknown input type: {type(input_img)=}")
         top_guesses = ["Unknown Input Type", "", "", "", ""]
@@ -75,18 +39,4 @@ def url_image_vars(
 
 
 def is_healthy() -> bool:
-    request_url = urljoin(MODEL_ENDPOINT_URL, "healthcheck")
-    try:
-        response = requests.get(url=request_url, timeout=1)
-    except Exception:
-        logging.error("Failed to make healthcheck request")
-        return False
-    if response.status_code == 200:
-        try:
-            response_content = json.loads(response.content)
-        except Exception:
-            logging.error("Failed to load healthcheck content")
-            return False
-        if response_content == {"status": "alive"}:
-            return True
-    return False
+    return True

src/requirements.in

@@ -6,3 +6,5 @@ joblib
 whitenoise
 pillow
 transformers
+tensorflow
+scikit-image

src/requirements.txt

@@ -4,10 +4,18 @@
 #
 #    pip-compile --resolver=backtracking requirements.in
 #
+absl-py==1.4.0
+    # via
+    #   tensorboard
+    #   tensorflow
 asgiref==3.7.1
     # via django
+astunparse==1.6.3
+    # via tensorflow
 backports-zoneinfo==0.2.1
     # via django
+cachetools==5.3.0
+    # via google-auth
 certifi==2023.5.7
     # via requests
 charset-normalizer==3.1.0
@@ -18,26 +26,96 @@ filelock==3.12.0
     # via
     #   huggingface-hub
     #   transformers
+flatbuffers==23.5.26
+    # via tensorflow
 fsspec==2023.5.0
     # via huggingface-hub
+gast==0.4.0
+    # via tensorflow
+google-auth==2.19.0
+    # via
+    #   google-auth-oauthlib
+    #   tensorboard
+google-auth-oauthlib==1.0.0
+    # via tensorboard
+google-pasta==0.2.0
+    # via tensorflow
+grpcio==1.54.2
+    # via
+    #   tensorboard
+    #   tensorflow
 gunicorn==20.1.0
     # via -r requirements.in
+h5py==3.8.0
+    # via tensorflow
 huggingface-hub==0.14.1
     # via transformers
 idna==3.4
     # via requests
+imageio==2.29.0
+    # via scikit-image
+importlib-metadata==6.6.0
+    # via markdown
+jax==0.4.10
+    # via tensorflow
 joblib==1.2.0
     # via -r requirements.in
+keras==2.12.0
+    # via tensorflow
+libclang==16.0.0
+    # via tensorflow
+markdown==3.4.3
+    # via tensorboard
+markupsafe==2.1.2
+    # via werkzeug
+ml-dtypes==0.1.0
+    # via jax
+networkx==3.1
+    # via scikit-image
 numpy==1.23.5
     # via
     #   -r requirements.in
+    #   h5py
+    #   imageio
+    #   jax
+    #   ml-dtypes
+    #   opt-einsum
+    #   pywavelets
+    #   scikit-image
+    #   scipy
+    #   tensorboard
+    #   tensorflow
+    #   tifffile
     #   transformers
+oauthlib==3.2.2
+    # via requests-oauthlib
+opt-einsum==3.3.0
+    # via
+    #   jax
+    #   tensorflow
 packaging==23.1
     # via
     #   huggingface-hub
+    #   scikit-image
+    #   tensorflow
     #   transformers
 pillow==9.5.0
-    # via -r requirements.in
+    # via
+    #   -r requirements.in
+    #   imageio
+    #   scikit-image
+protobuf==4.23.2
+    # via
+    #   tensorboard
+    #   tensorflow
+pyasn1==0.5.0
+    # via
+    #   pyasn1-modules
+    #   rsa
+pyasn1-modules==0.3.0
+    # via google-auth
+pywavelets==1.4.1
+    # via scikit-image
 pyyaml==6.0
     # via
     #   huggingface-hub
@@ -48,9 +126,41 @@ requests==2.30.0
     # via
     #   -r requirements.in
     #   huggingface-hub
+    #   requests-oauthlib
+    #   tensorboard
     #   transformers
+requests-oauthlib==1.3.1
+    # via google-auth-oauthlib
+rsa==4.9
+    # via google-auth
+scikit-image==0.19.3
+    # via -r requirements.in
+scipy==1.10.1
+    # via
+    #   jax
+    #   scikit-image
+six==1.16.0
+    # via
+    #   astunparse
+    #   google-auth
+    #   google-pasta
+    #   tensorflow
 sqlparse==0.4.4
     # via django
+tensorboard==2.12.3
+    # via tensorflow
+tensorboard-data-server==0.7.0
+    # via tensorboard
+tensorflow==2.12.0
+    # via -r requirements.in
+tensorflow-estimator==2.12.0
+    # via tensorflow
+tensorflow-io-gcs-filesystem==0.32.0
+    # via tensorflow
+termcolor==2.3.0
+    # via tensorflow
+tifffile==2023.4.12
+    # via scikit-image
 tokenizers==0.13.3
     # via transformers
 tqdm==4.65.0
@@ -63,10 +173,23 @@ typing-extensions==4.5.0
     # via
     #   asgiref
     #   huggingface-hub
+    #   tensorflow
 urllib3==1.26.15
-    # via requests
+    # via
+    #   google-auth
+    #   requests
+werkzeug==2.3.4
+    # via tensorboard
+wheel==0.40.0
+    # via
+    #   astunparse
+    #   tensorboard
 whitenoise==6.4.0
     # via -r requirements.in
+wrapt==1.14.1
+    # via tensorflow
+zipp==3.15.0
+    # via importlib-metadata
 
 # The following packages are considered to be unsafe in a requirements file:
 # setuptools

src/start.sh

@@ -1 +0,0 @@
-python3 -m uvicorn main:app --workers 1 --host 0.0.0.0 --port 7860