Create README.md

README.md

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+import tensorflow as tf
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
+from google.colab import drive
+drive.mount('/content/drive')
+# Define constants
+image_size = (150, 150)
+batch_size = 32
+
+# Data augmentation for the training set
+train_datagen = ImageDataGenerator(
+    rescale=1./255,
+    shear_range=0.2,
+    zoom_range=0.2,
+    horizontal_flip=True
+)
+# Rescaling for the testing set
+test_datagen = ImageDataGenerator(rescale=1./255)
+
+# Load the training set
+train_set = train_datagen.flow_from_directory(
+    '/content/drive/MyDrive/chest_xray/train',
+    target_size=image_size,
+    batch_size=batch_size,
+    class_mode='binary'
+)
+
+# Load the testing set
+test_set = test_datagen.flow_from_directory(
+    '/content/drive/MyDrive/chest_xray/test',
+    target_size=image_size,
+    batch_size=batch_size,
+    class_mode='binary'
+)
+# Build the CNN model
+model = Sequential()
+model.add(Conv2D(32, (3, 3), input_shape=(image_size[0], image_size[1], 3), activation='relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Conv2D(64, (3, 3), activation='relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Flatten())
+model.add(Dense(units=128, activation='relu'))
+model.add(Dense(units=1, activation='sigmoid'))
+# Compile the model
+model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+# Train the model
+model.fit(train_set, epochs=10, validation_data=test_set)
+
+# Save the model
+model.save('pneumonia_model.h5')
+# Evaluate the model on the testing set
+accuracy = model.evaluate(test_set)[1]
+print(f'Test Accuracy: {accuracy}')
+# Make predictions on new images
+def predict_image(file_path):
+    img = tf.keras.preprocessing.image.load_img(file_path, target_size=image_size)
+    img_array = tf.keras.preprocessing.image.img_to_array(img)
+    img_array = tf.expand_dims(img_array, 0)  # Create a batch
+
+    predictions = model.predict(img_array)
+    if predictions[0] > 0.5:
+        print("Prediction: Pneumonia")
+    else:
+        print("Prediction: Normal")
+
+# Example usage:
+image_path = "/content/drive/MyDrive/chest_xray/train/PNEUMONIA/BACTERIA-1033441-0001.jpeg"
+predict_image(image_path)