# -*- coding: utf-8 -*-
"""Untitled20.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1O_tHcmidNGKAgxAiG7Su44auJSRFR1xA
"""

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from tensorflow.keras.preprocessing.image import ImageDataGenerator

image_size = (128, 128)
batch_size = 32
train_datagen = ImageDataGenerator(
    rescale=1./255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True
)

test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
    '/content/drive/MyDrive/training',
    target_size=image_size,
    batch_size=batch_size,
    class_mode='binary'
)

test_generator = test_datagen.flow_from_directory(
    '/content/drive/MyDrive/testing',
    target_size=image_size,
    batch_size=batch_size,
    class_mode='binary'
)

from tensorflow.keras.models import Sequential

model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(image_size[0], image_size[1], 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(train_generator, epochs=10, validation_data=test_generator)
evaluation = model.evaluate(test_generator)
print(f"Test Accuracy: {evaluation[1] * 100:.2f}%")

predictions = model.predict(test_generator)
predicted_labels = (predictions > 0.5).astype(int)

from sklearn.metrics import confusion_matrix, classification_report
true_labels = test_generator.classes
conf_matrix = confusion_matrix(true_labels, predicted_labels)
print("Confusion Matrix:")
print(conf_matrix)
class_report = classification_report(true_labels, predicted_labels, target_names=['not_fractured', 'fractured'])
print("Classification Report:")
print(class_report)

import matplotlib.pyplot as plt
import random
test_images, true_labels = next(test_generator)
predicted_labels = (model.predict(test_images) > 0.5).astype(int)
plt.figure(figsize=(12, 8))
for i in range(10):
    plt.subplot(2, 5, i+1)
    plt.imshow(test_images[i])
    plt.title(f"True: {true_labels[i]}, Predicted: {predicted_labels[i]}")
    plt.axis('off')
plt.show()

import cv2

image = cv2.imread('/content/drive/MyDrive/testing/fractured/1-rotated1-rotated1-rotated2.jpg')

plt.imshow(image)

image.shape

image = cv2.resize(image,(256,256))

test_input = image.reshape((1,256,256,3))

image.shape

plt.imshow(image)

test_input = image.reshape((1,256,256,3))

!pip install keras
import keras
model = keras.Sequential([
    keras.layers.Dense(128, activation="relu"),
    keras.layers.Dense(64, activation="relu"),
    keras.layers.Dense(10, activation="softmax")
])

!ls -l model

!stat model

!file model

!pip show tensorflow

model.predict(test_input)