Upload 4 files

pyproject.toml

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+[tool.poetry]
+name = "xray-image-classifier"
+version = "0.1.0"
+description = ""
+authors = ["Your Name <your.email@example.com>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "3.11.8"
+tensorflow = "2.15.1"
+keras = "^2.15.0"
+numpy = "^1.23.5"
+pandas = "^2.2.2"
+matplotlib = "^3.9.2"
+jupyter = "^1.0.0"
+scipy = "^1.14.1"
+[tool.poetry.group.dev.dependencies]
+pytest = "^8.3.2"
+ipython = "^8.26.0"
+autopep8 = "^2.3.1"
+jupyter = "^1.0.0"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

requirements.txt

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+tensorflow==2.15.1
+keras==2.15.0
+numpy==1.23.5
+pandas==2.2.2
+matplotlib==3.9.2
+jupyter==1.0.0
+scipy==1.14.1
+pytest==8.3.2
+ipython==8.26.0
+autopep8==2.3.1

train_model.py

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+import os
+import numpy as np
+import tensorflow as tf
+import matplotlib.pyplot as plt
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from model import create_model 
+
+base_dir = 'data/chest_xray'
+train_dir = os.path.join(base_dir, 'train')
+val_dir = os.path.join(base_dir, 'val')
+
+train_datagen = ImageDataGenerator(
+    rescale=1./255,
+    rotation_range=20,
+    width_shift_range=0.2,
+    height_shift_range=0.2,
+    shear_range=0.2,
+    zoom_range=0.2,
+    horizontal_flip=True,
+    fill_mode='nearest'
+)
+val_datagen = ImageDataGenerator(rescale=1./255)
+
+train_generator = train_datagen.flow_from_directory(
+    train_dir,
+    target_size=(150, 150),
+    batch_size=32,
+    class_mode='binary'
+)
+
+val_generator = val_datagen.flow_from_directory(
+    val_dir,
+    target_size=(150, 150),
+    batch_size=32,
+    class_mode='binary'
+)
+
+sample_images, _ = next(train_generator)
+for i in range(5):
+    plt.subplot(1, 5, i+1)
+    plt.imshow(sample_images[i])
+    plt.axis('off')
+plt.show()
+
+model = create_model()
+
+history = model.fit(
+    train_generator,
+    steps_per_epoch=243,
+    epochs=10,
+    validation_data=val_generator,
+    validation_steps=280,
+    callbacks=[tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)]
+)
+
+model.save('xray_image_classifier_model.keras')