Initial commit

.github/workflows/CI.yml

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+name: CI
+on:
+  push:
+    branches: [ "main" ]
+  pull_request:
+    branches: [ "main" ]
+  workflow_dispatch:
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - name: install packages
+        run: make install
+      - name: format
+        run: make format
+      - name: lint
+        run: make lint
+      - name: test
+        run: make test

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[codz]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py.cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+#poetry.toml
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#   pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python.
+#   https://pdm-project.org/en/latest/usage/project/#working-with-version-control
+#pdm.lock
+#pdm.toml
+.pdm-python
+.pdm-build/
+
+# pixi
+#   Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control.
+#pixi.lock
+#   Pixi creates a virtual environment in the .pixi directory, just like venv module creates one
+#   in the .venv directory. It is recommended not to include this directory in version control.
+.pixi
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.envrc
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+# Abstra
+# Abstra is an AI-powered process automation framework.
+# Ignore directories containing user credentials, local state, and settings.
+# Learn more at https://abstra.io/docs
+.abstra/
+
+# Visual Studio Code
+#  Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore 
+#  that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore
+#  and can be added to the global gitignore or merged into this file. However, if you prefer, 
+#  you could uncomment the following to ignore the entire vscode folder
+# .vscode/
+
+# Ruff stuff:
+.ruff_cache/
+
+# PyPI configuration file
+.pypirc
+
+# Cursor
+#  Cursor is an AI-powered code editor. `.cursorignore` specifies files/directories to
+#  exclude from AI features like autocomplete and code analysis. Recommended for sensitive data
+#  refer to https://docs.cursor.com/context/ignore-files
+.cursorignore
+.cursorindexingignore
+
+# Marimo
+marimo/_static/
+marimo/_lsp/
+__marimo__/

Dockerfile

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+# Base image with Python 3.13
+FROM python:3.13-slim AS base
+
+# Recommended environment variables
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+ENV UV_SYSTEM_PYTHON=1
+
+WORKDIR /app
+
+# Intall the requiered dependencies of the system 
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+    libjpeg-dev \
+    zlib1g-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install uv and the dependencies of the project
+FROM base AS builder
+# Install uv
+RUN pip install --no-cache-dir uv
+# Copy the dependencies file
+COPY pyproject.toml .
+# Copy the lock file if exists
+COPY uv.lock* .
+# Install the dependencies of the project in the system's environment
+RUN uv pip install --system --no-cache .
+
+# Copy the source code and prepare the execution environment
+FROM base AS runtime
+# Copy the installed dependencies
+COPY --from=builder /usr/local /usr/local
+# Copy the source code of the API, logic and home.html
+COPY api ./api
+COPY mylib ./mylib
+COPY templates ./templates
+# Expose the port associated with the API created with FastAPI
+EXPOSE 8000
+# Default command: it starts the API with uvicorn
+CMD ["uvicorn", "api.api:app", "--host", "0.0.0.0", "--port", "8000"]

LICENSE

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+MIT License
+
+Copyright (c) 2025 Iker-Jauregui
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Makefile

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+install:
+	pip install uv &&\
+	uv sync
+
+test:
+	uv run python -m pytest tests/ -vv --cov=mylib --cov=api --cov=cli 
+
+format:	
+	uv run black mylib/*.py cli/*.py api/*.py #*.py
+
+lint:
+	uv run pylint --disable=R,C --ignore-patterns=test_.*\.py mylib/*.py cli/*.py api/*.py 
+
+refactor: format lint
+
+all: install format lint test

README.md

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+[![CI](https://github.com/Iker-Jauregui/MLOps-Lab2/actions/workflows/CI.yml/badge.svg)](https://github.com/Iker-Jauregui/MLOps-Lab2/actions/workflows/CI.yml)
+
+# MLOps-Lab2
+Repo for Lab2 of MLOps subject

api/api.py

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+"""FastAPI application for image classification."""
+
+import io
+import uvicorn
+from PIL import Image
+from fastapi import FastAPI, Form, HTTPException, UploadFile, File
+from fastapi.templating import Jinja2Templates
+from fastapi.requests import Request
+from fastapi.responses import HTMLResponse
+
+from mylib.classifier import predict as predict_func
+from mylib.classifier import resize as resize_func
+
+# Create an instance of FastAPI
+app = FastAPI(
+    title="API of the Image Classifier using FastAPI",
+    description="API to perform image predictions and transforms using mylib.classifier",
+    version="1.0.0",
+)
+
+# We use the templates folder to obtain HTML files
+templates = Jinja2Templates(directory="templates")
+
+
+# Initial endpoint
+@app.get("/", response_class=HTMLResponse)
+def home(request: Request):
+    """Home endpoint returning HTML template."""
+    return templates.TemplateResponse(request=request, name="home.html")
+
+
+# Main endpoint to perform the image prediction
+@app.post("/predict")
+async def predict_endpoint(
+    file: UploadFile = File(...),
+    class_names: str = Form(default="cardboard,paper,plastic,metal,trash,glass"),
+):
+    """
+    Predict the class of the input image.
+
+    Parameters
+    ----------
+    file : UploadFile
+        Image file to classify
+    class_names : str
+        Comma-separated class names (default: "cardboard,paper,plastic,metal,trash,glass")
+
+    Returns
+    -------
+    dict
+        Dictionary with predicted class
+    """
+    try:
+        # Read image from upload
+        contents = await file.read()
+        image = Image.open(io.BytesIO(contents))
+
+        # Convert class_names string to list
+        class_list = [c.strip() for c in class_names.split(",")]
+
+        # Get prediction
+        prediction = predict_func(image, class_list)
+
+        return {"predicted_class": prediction}
+
+    except (FileNotFoundError, IOError, ValueError) as e:
+        raise HTTPException(
+            status_code=400, detail=f"Error processing image: {str(e)}"
+        ) from e
+
+
+# Main endpoint to perform the image resize
+@app.post("/resize")
+async def resize_endpoint(
+    file: UploadFile = File(...), width: int = Form(...), height: int = Form(...)
+):
+    """
+    Resize the input image.
+
+    Parameters
+    ----------
+    file : UploadFile
+        Image file to resize
+    width : int
+        Target width (must be positive)
+    height : int
+        Target height (must be positive)
+
+    Returns
+    -------
+    dict
+        Dictionary with new image dimensions
+    """
+    if width <= 0:
+        raise HTTPException(status_code=400, detail="'width' must be a positive value")
+    if height <= 0:
+        raise HTTPException(status_code=400, detail="'height' must be a positive value")
+
+    try:
+        # Read image from upload
+        contents = await file.read()
+        image = Image.open(io.BytesIO(contents))
+
+        # Resize image
+        new_size = resize_func(image, width, height)
+
+        return {"resized_dimensions": new_size}
+
+    except (FileNotFoundError, IOError, ValueError) as e:
+        raise HTTPException(
+            status_code=400, detail=f"Error resizing image: {str(e)}"
+        ) from e
+
+
+# Entry point (for direct execution only)
+if __name__ == "__main__":
+    uvicorn.run("api.api:app", host="0.0.0.0", port=8000, reload=True)

cli/cli.py

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+"""
+Main CLI or app entry point
+"""
+
+import click
+from mylib.classifier import predict, resize
+
+
+# We create a group of commands
+@click.group()
+def cli():
+    """Main CLI to perform image operations."""
+
+
+# ============================================================================
+# INFERENCE GROUP - Image inference operations
+# ============================================================================
+@cli.group()
+def inference():
+    """Commands for image inference operations."""
+
+
+# We create a command, named predict, associated with the previous group
+@inference.command("predict")
+@click.argument("image-path", type=str)
+@click.option(
+    "--class-names",
+    default="cardboard,paper,plastic,metal,trash,glass",
+    type=str,
+    help="Comma-separated class names (e.g., 'cat,dog,bird').",
+)
+def predict_cli(image_path, class_names):
+    """Predict image class.
+
+    Example:
+        uv run python -m cli.cli inference predict 'sample.jpg'
+    """
+    try:
+        # Convert comma-separated string to list
+        class_list = [c.strip() for c in class_names.split(",")]
+        result = predict(image_path, class_list)
+        click.echo(click.style(f"Predicted class: {result}", fg="green"))
+    except (FileNotFoundError, IOError, ValueError) as e:
+        click.echo(click.style(f"Error: {str(e)}", fg="red"), err=True)
+
+
+# ============================================================================
+# TRANSFORM GROUP - Image transform operations
+# ============================================================================
+
+
+@cli.group()
+def transform():
+    """Commands for image transform operations."""
+
+
+@transform.command("resize")
+@click.argument("image-path", type=str)
+@click.argument("width", type=int)
+@click.argument("height", type=int)
+def resize_cli(image_path, width, height):
+    """Resize image.
+
+    Example:
+        uv run python -m cli.cli transform resize 'sample.jpg' 224 224
+    """
+    try:
+        if width <= 0:
+            raise ValueError("'width' must be a positive value")
+        if height <= 0:
+            raise ValueError("'height' must be a positive value")
+
+        result = resize(image_path, width, height)
+        click.echo(click.style(f"Resized to: {result}", fg="green"))
+    except (FileNotFoundError, IOError, ValueError) as e:
+        click.echo(click.style(f"Error: {str(e)}", fg="red"), err=True)
+
+
+# Main entry point
+if __name__ == "__main__":
+    # pylint: disable=no-value-for-parameter
+    cli()

mylib/__init__.py

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+"""mlops_practice package."""
+
+__version__ = "0.1.0"

mylib/classifier.py

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+"""
+Image classifier library
+"""
+
+import random
+from pathlib import Path
+from PIL import Image
+
+
+def predict(image_path, class_names=None):
+    """
+    Predict the class of an image.
+
+    Loads image from file or PIL Image object and returns predicted class.
+
+    Parameters
+    ----------
+    image_path : str, Path, or PIL.Image
+        Path to image file (str or Path object) or PIL Image object directly.
+        Supported formats: JPG, PNG, BMP, GIF, TIFF.
+    class_names : list of str, optional
+        List of class names. Default: ['cardboard', 'paper', 'plastic', 'metal', 'trash', 'glass']
+
+    Returns
+    -------
+    str
+        Predicted class name (randomly selected from class_names).
+
+    Raises
+    ------
+    FileNotFoundError
+        If image file path does not exist.
+    IOError
+        If image file cannot be read.
+    ValueError
+        If image format is not supported or class_names is empty.
+
+    Examples
+    --------
+    >>> predicted_class = predict("sample.jpg", ['cat', 'dog'])
+    """
+    if class_names is None:
+        class_names = ["cardboard", "paper", "plastic", "metal", "trash", "glass"]
+
+    if not class_names:
+        raise ValueError("class_names cannot be empty")
+
+    try:
+        # Handle both file paths and PIL Images
+        if isinstance(image_path, (str, Path)):
+            if not Path(image_path).exists():
+                raise FileNotFoundError(f"Image file not found: {image_path}")
+            Image.open(image_path).convert("RGB")
+        elif isinstance(image_path, Image.Image):
+            image_path.convert("RGB")
+        else:
+            raise ValueError(f"Unsupported image_path type: {type(image_path)}")
+
+        # For Lab1: randomly select a class
+        predicted_class = random.choice(class_names)
+        return predicted_class
+
+    except FileNotFoundError:
+        raise
+    except Exception as e:
+        raise IOError(f"Error loading image: {str(e)}") from e
+
+
+def resize(image_path, width, height):
+    """
+    Resize an image to specified dimensions.
+
+    Parameters
+    ----------
+    image_path : str, Path, or PIL.Image
+        Path to image file or PIL Image object.
+    width : int
+        Target width in pixels. Must be positive.
+    height : int
+        Target height in pixels. Must be positive.
+
+    Returns
+    -------
+    tuple of (int, int)
+        New dimensions (width, height) of the resized image.
+
+    Raises
+    ------
+    FileNotFoundError
+        If image file path does not exist.
+    ValueError
+        If width or height are not positive integers.
+    IOError
+        If image file cannot be read.
+
+    Examples
+    --------
+    >>> new_size = resize("sample.jpg", 224, 224)
+    >>> print(new_size)
+    (224, 224)
+    """
+    if width <= 0:
+        raise ValueError("'width' must be a positive integer")
+    if height <= 0:
+        raise ValueError("'height' must be a positive integer")
+
+    try:
+        # Handle both file paths and PIL Images
+        if isinstance(image_path, (str, Path)):
+            if not Path(image_path).exists():
+                raise FileNotFoundError(f"Image file not found: {image_path}")
+            image = Image.open(image_path).convert("RGB")
+        elif isinstance(image_path, Image.Image):
+            image = image_path.convert("RGB")
+        else:
+            raise ValueError(f"Unsupported image_path type: {type(image_path)}")
+
+        # Resize the image
+        resized_image = image.resize((width, height))
+
+        return resized_image.size  # Returns (width, height)
+
+    except FileNotFoundError:
+        raise
+    except ValueError:
+        raise
+    except Exception as e:
+        raise IOError(f"Error resizing image: {str(e)}") from e

pyproject.toml

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+[project]
+name = "mlops-lab1"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "click>=8.3.0",
+    "fastapi>=0.121.2",
+    "httpx>=0.28.1",
+    "jinja2>=3.1.6",
+    "numpy>=2.3.4",
+    "pillow>=12.0.0",
+    "python-multipart>=0.0.20",
+    "uvicorn>=0.38.0",
+]
+
+[dependency-groups]
+dev = [
+    "black>=25.11.0",
+    "pylint>=4.0.3",
+    "pytest>=9.0.1",
+    "pytest-cov>=7.0.0",
+]

templates/home.html

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+<!DOCTYPE html>
+<html>
+<head>
+    <title>Image Classifier API</title>
+</head>
+<body>
+    <h1>Welcome to the API of the image classifier</h1>
+    <p>Add <code>/docs</code> to the URL to test the endpoints of the application.</p>
+</body>
+</html>

tests/test_api.py

@@ -0,0 +1,106 @@
+"""
+Integration testing with the API
+"""
+import io
+import pytest
+from pathlib import Path
+from PIL import Image
+from fastapi.testclient import TestClient
+from api.api import app
+
+
+@pytest.fixture
+def client():
+    """Testing client from FastAPI."""
+    return TestClient(app)
+
+
+@pytest.fixture
+def sample_image_bytes():
+    """Create a sample image in memory for testing."""
+    img = Image.new('RGB', (100, 100), color='red')
+    img_bytes = io.BytesIO()
+    img.save(img_bytes, format='JPEG')
+    img_bytes.seek(0)
+    return img_bytes
+
+
+def test_home_endpoint(client):
+    """Verify that the endpoint / returns the right message."""
+    response = client.get("/")
+    assert response.status_code == 200
+    assert "text/html" in response.headers["content-type"]
+
+
+def test_predict(client, sample_image_bytes):
+    """Verify that the endpoint /predict performs the class prediction correctly."""
+    response = client.post(
+        "/predict",
+        files={"file": ("test.jpg", sample_image_bytes, "image/jpeg")},
+        data={"class_names": "cat,dog,bird"}
+    )
+    assert response.status_code == 200
+    data = response.json()
+    assert "predicted_class" in data
+    assert data["predicted_class"] in ["cat", "dog", "bird"]
+
+
+def test_predict_invalid_file(client):
+    """Verify that the endpoint /predict manages correctly invalid files."""
+    response = client.post(
+        "/predict",
+        files={"file": ("test.txt", b"not an image", "text/plain")}
+    )
+    assert response.status_code == 400
+    data = response.json()
+    assert "detail" in data
+
+
+def test_resize(client, sample_image_bytes):
+    """Verify that the endpoint /resize performs the image resize correctly."""
+    response = client.post(
+        "/resize",
+        files={"file": ("test.jpg", sample_image_bytes, "image/jpeg")},
+        data={"width": "32", "height": "32"}
+    )
+    assert response.status_code == 200
+    data = response.json()
+    assert "resized_dimensions" in data
+    assert data["resized_dimensions"] == [32, 32]
+
+
+def test_resize_invalid_width(client, sample_image_bytes):
+    """Verify that the endpoint /resize manages correctly invalid widths."""
+    response = client.post(
+        "/resize",
+        files={"file": ("test.jpg", sample_image_bytes, "image/jpeg")},
+        data={"width": "0", "height": "32"}
+    )
+    assert response.status_code == 400
+    data = response.json()
+    assert "detail" in data
+    assert "'width' must be a positive value" in data["detail"]
+
+
+def test_resize_invalid_height(client, sample_image_bytes):
+    """Verify that the endpoint /resize manages correctly invalid heights."""
+    response = client.post(
+        "/resize",
+        files={"file": ("test.jpg", sample_image_bytes, "image/jpeg")},
+        data={"width": "32", "height": "0"}
+    )
+    assert response.status_code == 400
+    data = response.json()
+    assert "detail" in data
+    assert "'height' must be a positive value" in data["detail"]
+
+
+def test_resize_invalid_parameters(client):
+    """Verify that the endpoint /resize manages correctly missing parameters."""
+    response = client.post(
+        "/resize",
+        data={"width": "32", "height": "32"}
+    )
+    assert response.status_code == 422  # FastAPI returns 422 for validation errors
+    data = response.json()
+    assert "detail" in data

tests/test_cli.py

@@ -0,0 +1,60 @@
+"""
+Integration testing with the CLI
+"""
+import pytest
+from pathlib import Path
+from click.testing import CliRunner
+from cli.cli import cli
+
+
+# Fixture
+@pytest.fixture
+def runner():
+    """Fixture to provide a CliRunner instance for all CLI tests."""
+    return CliRunner()
+
+
+@pytest.fixture
+def sample_image_path():
+    """Provide path to test image."""
+    return str(Path(__file__).parent / "sample.jpg")
+
+
+def test_help(runner):
+    """Tests the command-line interface help message."""
+    result = runner.invoke(cli, ["--help"])
+    assert result.exit_code == 0
+    assert "Show this message and exit." in result.output
+
+
+# Testing of the predict_cli of the inference group
+def test_predict_cli(runner, sample_image_path):
+    """Tests the command-line interface predict command."""
+    result = runner.invoke(cli, ["inference", "predict", sample_image_path])
+    assert result.exit_code == 0
+    assert "Predicted class:" in result.output
+
+
+def test_predict_cli_with_custom_classes(runner, sample_image_path):
+    """Tests predict with custom class names."""
+    result = runner.invoke(
+        cli,
+        ["inference", "predict", sample_image_path, "--class-names", "cat,dog"],
+    )
+    assert result.exit_code == 0
+    assert "Predicted class:" in result.output
+
+
+# Testing of the resize_cli of the transform group
+def test_resize_cli(runner, sample_image_path):
+    """Tests the command-line interface resize command."""
+    result = runner.invoke(cli, ["transform", "resize", sample_image_path, "32", "32"])
+    assert result.exit_code == 0
+    assert "32" in result.output
+
+
+def test_resize_cli_invalid_width(runner, sample_image_path):
+    """Tests resize with invalid width."""
+    result = runner.invoke(cli, ["transform", "resize", sample_image_path, "0", "32"])
+    assert result.exit_code == 0
+    assert "Error" in result.output

tests/test_logic.py

@@ -0,0 +1,73 @@
+"""
+Unit Testing of the application's logic
+"""
+import pytest
+from pathlib import Path
+from PIL import Image
+from mylib.classifier import predict, resize
+
+
+@pytest.fixture
+def sample_image_path(tmp_path):
+    """Create a temporary sample image for testing."""
+    img = Image.new('RGB', (100, 100), color='blue')
+    img_path = tmp_path / "sample.jpg"
+    img.save(img_path)
+    return str(img_path)
+
+
+def test_predict_with_file_path(sample_image_path):
+    """Test predict function with file path."""
+    result = predict(sample_image_path, ['cat', 'dog'])
+    assert result in ['cat', 'dog']
+
+
+def test_predict_with_pil_image():
+    """Test predict function with PIL Image."""
+    img = Image.new('RGB', (100, 100), color='green')
+    result = predict(img, ['cat', 'dog', 'bird'])
+    assert result in ['cat', 'dog', 'bird']
+
+
+def test_predict_default_classes(sample_image_path):
+    """Test predict with default class names."""
+    result = predict(sample_image_path)
+    default_classes = ['cardboard', 'paper', 'plastic', 'metal', 'trash', 'glass']
+    assert result in default_classes
+
+
+def test_predict_file_not_found():
+    """Test predict with non-existent file."""
+    with pytest.raises(FileNotFoundError):
+        predict("nonexistent.jpg", ['cat'])
+
+
+def test_resize_with_file_path(sample_image_path):
+    """Test resize function with file path."""
+    result = resize(sample_image_path, 32, 32)
+    assert result == (32, 32)
+
+
+def test_resize_with_pil_image():
+    """Test resize function with PIL Image."""
+    img = Image.new('RGB', (100, 100), color='yellow')
+    result = resize(img, 64, 64)
+    assert result == (64, 64)
+
+
+def test_resize_invalid_width(sample_image_path):
+    """Test resize with invalid width."""
+    with pytest.raises(ValueError, match="'width' must be a positive integer"):
+        resize(sample_image_path, 0, 32)
+
+
+def test_resize_invalid_height(sample_image_path):
+    """Test resize with invalid height."""
+    with pytest.raises(ValueError, match="'height' must be a positive integer"):
+        resize(sample_image_path, 32, -5)
+
+
+def test_resize_file_not_found():
+    """Test resize with non-existent file."""
+    with pytest.raises(FileNotFoundError):
+        resize("nonexistent.jpg", 32, 32)