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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ *.safetensors filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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.gitignore ADDED
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+ # Byte-compiled / optimized / DLL files
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+ __pycache__/
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+ *.py[cod]
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+ *$py.class
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+
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+ # C extensions
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+ *.so
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+
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+ # Distribution / packaging
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+ .Python
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+ build/
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+ develop-eggs/
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+ dist/
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+ downloads/
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+ eggs/
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+ .eggs/
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+ lib/
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+ lib64/
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+ parts/
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+ var/
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+ wheels/
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+ share/python-wheels/
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+ *.egg-info/
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+ .installed.cfg
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+ *.egg
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+ MANIFEST
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+
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+ # PyInstaller
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+ # Usually these files are written by a python script from a template
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+ # before PyInstaller builds the exe, so as to inject date/other infos into it.
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+ *.manifest
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+ *.spec
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+
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+ # Installer logs
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+ pip-log.txt
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+ pip-delete-this-directory.txt
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+
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+ # Unit test / coverage reports
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+ htmlcov/
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+ .tox/
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+ .nox/
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+ .coverage
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+ .coverage.*
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+ .cache
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+ coverage.xml
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+ *.cover
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+ *.py,cover
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+ .hypothesis/
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+ .pytest_cache/
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+ cover/
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+
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+ # Translations
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+ *.mo
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+ *.pot
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+
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+ # Django stuff:
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+ *.log
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+ local_settings.py
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+ db.sqlite3
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+ db.sqlite3-journal
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+
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+ # Flask stuff:
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+ instance/
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+ .webassets-cache
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+ # Scrapy stuff:
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+ .scrapy
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+
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+ # Sphinx documentation
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+ # PyBuilder
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+ target/
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+ # Jupyter Notebook
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+ .ipynb_checkpoints
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+ # IPython
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+ ipython_config.py
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+
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+ # pyenv
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+ # For a library or package, you might want to ignore these files since the code is
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+ # intended to run in multiple environments; otherwise, check them in:
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+ # .python-version
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+
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+ # pipenv
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+ # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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+ # However, in case of collaboration, if having platform-specific dependencies or dependencies
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+ # having no cross-platform support, pipenv may install dependencies that don't work, or not
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+ # install all needed dependencies.
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+ #Pipfile.lock
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+ # poetry
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+ # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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+ # This is especially recommended for binary packages to ensure reproducibility, and is more
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+ # commonly ignored for libraries.
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+ # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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+ #poetry.lock
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+
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+ # pdm
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+ # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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+ #pdm.lock
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+ # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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+ # in version control.
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+ # https://pdm.fming.dev/latest/usage/project/#working-with-version-control
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+ .pdm.toml
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+ .pdm-build/
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+
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+ # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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+ __pypackages__/
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+
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+ # Celery stuff
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+ celerybeat-schedule
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+ celerybeat.pid
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+
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+ # SageMath parsed files
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+ *.sage.py
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+
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+ # Environments
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+ .env
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+ env/
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+ ENV/
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+ # Spyder project settings
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+ # Rope project settings
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+ /site
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+ # mypy
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+ .mypy_cache/
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+ dmypy.json
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+ # Pyre type checker
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+ .pyre/
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+ # pytype static type analyzer
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+ # Cython debug symbols
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+ cython_debug/
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+ # PyCharm
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+ # JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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+ # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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+ # and can be added to the global gitignore or merged into this file. For a more nuclear
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+ # option (not recommended) you can uncomment the following to ignore the entire idea folder.
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+ .idea/
classification/__init__.py ADDED
File without changes
classification/classifier.py ADDED
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+ from sklearn.ensemble import AdaBoostClassifier
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+ from sklearn.datasets import load_iris
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+ from sklearn.model_selection import train_test_split
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+ import joblib
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+ import pandas as pd
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+ import os
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+
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+
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+ class Classifier:
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+ def __init__(self):
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+ pass
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+
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+ def train_and_save(self):
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+ print("\nIRIS model training...")
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+ iris = load_iris()
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+ ada = AdaBoostClassifier(n_estimators=5)
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+
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+ X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.1, random_state=42)
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+ model = ada.fit(X_train, y_train)
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+
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+ print(f"Model score: {ada.score(X_train, y_train):.3f}")
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+ print(f"Test Accuracy: {ada.score(X_test, y_test):.3f}")
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+
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+ current_dir = os.path.dirname(os.path.abspath(__file__))
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+ parent_dir = os.path.dirname(current_dir)
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+ test_data_csv_path = os.path.join(parent_dir, "data", "test_data.csv")
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+
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+ pd.concat([pd.DataFrame(X_test), pd.DataFrame(y_test, columns=['4'])], axis=1).to_csv(test_data_csv_path,
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+ index=False)
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+
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+ model_path = os.path.join(parent_dir, "models", "model.pkl")
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+ joblib.dump(model, model_path)
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+ print(f"Model saved to {model_path}")
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+
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+
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+ def load_and_test(self, data):
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+ print("\nIRIS model prediction...")
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+
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+ current_dir = os.path.dirname(os.path.abspath(__file__))
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+ parent_dir = os.path.dirname(current_dir)
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+ model_path = os.path.join(parent_dir, "models", "model.pkl")
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+ model = joblib.load(model_path)
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+
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+ features = data.get('features', [])
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+
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+ if not isinstance(features, list) or not all(isinstance(sample, list) for sample in features):
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+ raise ValueError("Input data should be a list of feature vectors.")
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+
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+ # Predict the class
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+ predictions = model.predict(features).tolist()
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+ probabilities = model.predict_proba(features).tolist()
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+
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+ # Map predictions to class labels
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+ iris_types = {0: 'setosa', 1: 'versicolor', 2: 'virginica'}
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+ prediction_labels = [iris_types[pred] for pred in predictions]
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+
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+ return {"predictions": prediction_labels, "probabilities": probabilities}
main.py ADDED
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+ from classification.classifier import Classifier
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+ import json
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+
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+
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+ if __name__ == "__main__":
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+ cls = Classifier()
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+
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+ # Training
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+ cls.train_and_save()
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+
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+ # Testing
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+ data = { "features": [
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+ [6.5, 3.0, 5.8, 2.2],
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+ [6.1, 2.8, 4.7, 1.2]
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+ ]}
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+ results = cls.load_and_test(data)
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+ print("results:", results)
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+
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+ # Response similar to HTTP call
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+ response = {
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+ 'statusCode': 200,
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+ 'body': json.dumps({
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+ 'predictions': results["predictions"],
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+ 'probabilities': results["probabilities"]
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+ })
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+ }
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+ print("Example Full API response: ", response)