Create app.py

app.py

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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, Dropout
+import matplotlib.pyplot as plt
+from tensorflow.keras.applications import MobileNetV2
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Dropout, GlobalAveragePooling2D
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from sklearn.metrics import classification_report, roc_auc_score, roc_curve, auc
+from sklearn.utils.class_weight import compute_class_weight
+from sklearn.metrics import roc_auc_score
+from tensorflow.keras.callbacks import EarlyStopping
+from tensorflow.keras import regularizers
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
+import numpy as np
+import cv2
+
+
+def predict_pneumonia(img_path, model, class_labels):
+    # Preprocess the image
+    img = image.load_img(img_path, target_size=(224, 224))  
+    img_array = image.img_to_array(img)  
+    img_array = np.expand_dims(img_array, axis=0)  
+    img_array = img_array / 255.0  
+
+    # Make a prediction
+    predictions = model.predict(img_array)
+    predicted_class_idx = np.argmax(predictions, axis=1)[0]  
+    predicted_class = class_labels[predicted_class_idx]  
+    print(f"Prediction: {predicted_class} (Confidence: {predictions[0][predicted_class_idx]:.2f})")
+    return predicted_class
+
+
+    class_labels = {0: 'Normal', 1: 'Pneumonia'}
+
+    import os
+
+base_path = "/kaggle/input/chest-xray-pneumonia/chest_xray"
+print(os.listdir(base_path)) 
+
+train_dir = "/kaggle/input/chest-xray-pneumonia/chest_xray/train"
+val_dir = "/kaggle/input/chest-xray-pneumonia/chest_xray/val"
+test_dir = "/kaggle/input/chest-xray-pneumonia/chest_xray/test"
+
+train_datagen = ImageDataGenerator(rescale=1.0/255, rotation_range=20, zoom_range=0.2, horizontal_flip=True,shear_range=0.2
+    )
+val_datagen = ImageDataGenerator(rescale=1.0/255)
+test_datagen = ImageDataGenerator(rescale=1.0/255)
+
+train_generator = train_datagen.flow_from_directory(
+    train_dir, target_size=(224, 224), batch_size=32, class_mode='binary'
+)
+val_generator = val_datagen.flow_from_directory(
+    val_dir, target_size=(224, 224), batch_size=32, class_mode='binary'
+)
+test_generator = test_datagen.flow_from_directory(
+    test_dir, target_size=(224, 224), batch_size=32, class_mode='binary'
+)
+
+from tensorflow.keras.applications import MobileNetV2
+
+
+base_model = MobileNetV2(weights='/kaggle/input/mobile-v2-1-0-224/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5', 
+                         include_top=False, input_shape=(224, 224, 3))
+base_model.trainable = False
+
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Flatten, Dense, Dropout
+
+
+model = Sequential([
+    base_model,
+    Flatten(),
+    Dense(128, activation='relu'),  
+    Dropout(0.5),  
+    Dense(1, activation='sigmoid')  
+])
+
+model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+
+import os
+
+
+print("Train directory content:", os.listdir(train_dir))
+print("Validation directory content:", os.listdir(val_dir))
+print("Test directory content:", os.listdir(test_dir))
+
+class_weights = compute_class_weight(
+    class_weight='balanced',
+    classes=np.unique(train_generator.classes),
+    y=train_generator.classes
+)
+
+
+class_weights = {i: weight for i, weight in enumerate(class_weights)}
+
+
+if 1 in class_weights:
+    class_weights[1] = class_weights[1] * 2  
+
+
+history = model.fit(
+    train_generator,
+    steps_per_epoch=train_generator.samples // train_generator.batch_size,
+    validation_data=val_generator,
+    validation_steps=val_generator.samples // val_generator.batch_size,
+    epochs=10,  #
+    class_weight=class_weights  
+)
+
+test_loss, test_acc = model.evaluate(test_generator)
+print(f"Test Accuracy: {test_acc:.2f}")
+
+plt.figure(figsize=(10, 5))
+plt.subplot(1, 2, 1)
+plt.plot(history.history['accuracy'], label='Train Accuracy')
+plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
+plt.legend()
+plt.title('Accuracy')
+
+plt.subplot(1, 2, 2)
+plt.plot(history.history['loss'], label='Train Loss')
+plt.plot(history.history['val_loss'], label='Validation Loss')
+plt.legend()
+plt.title('Loss')
+
+plt.show()
+
+true_labels = test_generator.classes
+predicted_probs = model.predict(test_generator, steps=test_generator.samples // test_generator.batch_size + 1)
+predicted_labels = np.argmax(predicted_probs, axis=1)
+
+import numpy as np
+from sklearn.metrics import classification_report
+
+
+test_generator.reset()  
+predictions = model.predict(test_generator, steps=test_generator.samples // test_generator.batch_size + 1)
+
+
+predicted_labels = (predictions > 0.5).astype(int)
+
+
+true_labels = test_generator.classes
+
+# Step 3: Classification report
+report = classification_report(true_labels, predicted_labels, target_names=['Normal', 'Pneumonia'])
+
+print("Classification Report:")
+print(report)
+
+true_labels = test_generator.classes
+
+
+predicted_probs = model.predict(test_generator)
+
+
+roc_auc = roc_auc_score(true_labels, predicted_probs)
+print(f"ROC-AUC Score: {roc_auc:.2f}")
+
+true_labels = test_generator.classes
+
+
+predicted_probs = model.predict(test_generator)
+
+
+
+fpr, tpr, _ = roc_curve(true_labels, predicted_probs)
+
+
+roc_auc = auc(fpr, tpr)
+
+
+plt.figure()
+plt.plot(fpr, tpr, color='darkorange', lw=2, label=f"ROC curve (area = {roc_auc:.2f})")
+plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
+plt.xlabel('False Positive Rate')
+plt.ylabel('True Positive Rate')
+plt.title('Receiver Operating Characteristic')
+plt.legend(loc="lower right")
+plt.show()
+
+img_path = '/kaggle/input/chest-xray-pneumonia/chest_xray/val/PNEUMONIA/person1951_bacteria_4882.jpeg'  # Path to the X-ray image
+predicted_class = predict_pneumonia(img_path, model, class_labels)
+print(f"The X-ray image is classified as: {predicted_class}")