Deleted unnecessary files, updated Dockerfile

Dockerfile

@@ -7,7 +7,8 @@ RUN useradd -m -u 1000 user
 WORKDIR /app
 
 COPY --chown=user ./requirements.txt requirements.txt
+RUN python -m pip install --upgrade pip
 RUN pip install --no-cache-dir --upgrade -r requirements.txt
 
 COPY --chown=user ./src /app
-CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", $APP_PORT]
+CMD ["python", "-m", "uvicorn", "main:app", "--host", "0.0.0.0", "--port", $APP_PORT]

src/backup_services.py

@@ -1,91 +0,0 @@
-import logging
-
-from pathlib import Path
-
-import torch
-
-from fastapi import HTTPException, status
-from PIL import Image
-from torchvision import models
-from typing import Tuple
-
-import src.config as config
-
-
-logger = logging.getLogger(__name__)
-
-
-async def classify_mushroom_in_image_svc(img: Image.Image) -> Tuple[str, str, str]:
-    """Service used to classify a mushroom shown in an image.
-    The mushroom is classified to one of many well known mushroom classes/types,
-    as well as according to its toxicity profile (i.e. edible or poisonous).
-    Additionally, a probability is returned showing confidence of classification.
-
-    :param img: the image of the mushroom to be classified
-    :type img: Image.Image
-    :return: mushroom_type, toxicity_profile, classification_confidence
-    :rtype: Tuple[str, str, str]
-    """
-
-    try:
-        # Device agnostic
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-
-        logger.debug("Loading classification model.")
-
-        model_path = config.MODEL_PATH
-
-        # Load saved model checkpoint
-        model_state_dict = torch.load(model_path, map_location=device)
-
-        # Get class_names from saved model checkpoint
-        model_dirname = Path(model_path).resolve().parent
-        with open(model_dirname / "labels.txt", "r") as labels_fp:
-            class_names = [line.strip() for line in labels_fp]
-
-        model = models.get_model(config.BASE_MODEL_NAME, num_classes=len(class_names))
-
-        # Load state_dict of saved model
-        model.load_state_dict(model_state_dict)
-
-        weights_enum = models.get_model_weights(config.BASE_MODEL_NAME)
-
-        # Get the model's default transforms
-        image_transform = weights_enum.DEFAULT.transforms()
-
-        # Make sure the model is on the target device
-        model.to(device)
-
-        # Turn on model evaluation mode and inference mode
-        model.eval()
-        with torch.inference_mode():
-            logger.debug("Adapting input image by applying necessary transforms!")
-            # Transform and add an extra dimension to image (model requires samples in [batch_size, color_channels, height, width])
-            transformed_image = image_transform(img).unsqueeze(dim=0)
-
-            # Make a prediction on image with an extra dimension and send it to the target device
-            target_image_pred = model(transformed_image.to(device))
-
-        logger.debug("Starting classification process...")
-        # Convert logits -> prediction probabilities (using torch.softmax() for multi-class classification)
-        target_image_pred_probs = torch.softmax(target_image_pred, dim=1)
-
-        # Convert prediction probabilities -> prediction labels
-        target_image_pred_label = torch.argmax(target_image_pred_probs, dim=1)
-
-        class_name = class_names[target_image_pred_label]
-
-        # Split class_name to mushroom type and toxicity profile
-        class_type, toxicity = class_name.rsplit("_", 1)
-
-        # 4 decimal points precision
-        prob = round(target_image_pred_probs.max().item(), 4)
-
-        return class_type, toxicity, prob
-
-    except Exception as e:
-        logger.error("Classification process error: {e}")
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail="Classification process failed due to an internal error. Contact support if this persists.",
-        )