Update app.py

app.py

@@ -16,4 +16,118 @@ from sklearn import metrics
 from sklearn.svm import SVC
 dim = 100
 from imutils import paths
-import cv2
+import cv2
+!unzip /content/drive/MyDrive/Tomato.zip -d MTP
+import os
+
+# Define the paths for the train and test datasets
+train_base_dir = '/content/MTP/dataset/train'
+test_base_dir = '/content/MTP/dataset/val'
+
+# List of class names to keep
+class_names_to_keep = [
+    "Late_blight", "Tomato_mosaic_virus", "healthy",
+    "Septoria_leaf_spot", "Bacterial_spot", "Tomato_Yellow_Leaf_Curl_Virus"
+]
+
+# Create lists to store the file paths for train and test images
+train_image_paths = []
+test_image_paths = []
+
+# Populate the train and test image paths based on the specified classes
+for class_name in class_names_to_keep:
+    train_image_paths.extend([os.path.join(train_base_dir, class_name, filename) for filename in os.listdir(os.path.join(train_base_dir, class_name))])
+    test_image_paths.extend([os.path.join(test_base_dir, class_name, filename) for filename in os.listdir(os.path.join(test_base_dir, class_name))])
+import tensorflow as tf
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+# Define image dimensions and batch size
+image_height, image_width = 224, 224
+batch_size = 32
+# Define a function to load and preprocess the images, including labels
+def load_and_preprocess_image(image_path, label):
+    image = tf.io.read_file(image_path)
+    image = tf.image.decode_jpeg(image, channels=3)
+    image = tf.image.resize(image, [image_height, image_width])
+    image = image / 255.0
+    return image, label
+
+# Create TensorFlow Datasets with labels
+train_labels = [0 if "healthy" in path else 1 for path in train_image_paths]
+test_labels = [0 if "healthy" in path else 1 for path in test_image_paths]
+
+train_dataset = tf.data.Dataset.from_tensor_slices((train_image_paths, train_labels))
+train_dataset = train_dataset.map(load_and_preprocess_image)
+train_dataset = train_dataset.batch(batch_size)
+
+test_dataset = tf.data.Dataset.from_tensor_slices((test_image_paths, test_labels))
+test_dataset = test_dataset.map(load_and_preprocess_image)
+test_dataset = test_dataset.batch(batch_size)
+
+# Define and compile the CNN model as before
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
+
+# Define the CNN model
+model = Sequential([
+    Conv2D(32, (3, 3), activation='relu', input_shape=(image_height, image_width, 3)),
+    MaxPooling2D((2, 2)),
+    Conv2D(64, (3, 3), activation='relu'),
+    MaxPooling2D((2, 2)),
+    Conv2D(128, (3, 3), activation='relu'),
+    MaxPooling2D((2, 2)),
+    Flatten(),
+    Dense(128, activation='relu'),
+    Dense(1, activation='sigmoid')  # Binary classification, so using sigmoid activation
+])
+
+# Compile the model
+model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+
+# Train the model on the training dataset
+model.fit(train_dataset, epochs=10)
+
+# Call the function to plot the training histo
+
+
+# Evaluate the model on the test dataset
+test_loss, test_accuracy = model.evaluate(test_dataset)
+print(f'Test Accuracy: {test_accuracy}')
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Assuming train_dataset and test_dataset are BatchDataset objects
+
+# Function to get a batch of random images and labels
+def get_random_batch(dataset, batch_size=5):
+    dataset_iter = iter(dataset)
+    images, labels = [], []
+    for _ in range(batch_size):
+        batch = next(dataset_iter)
+        images.append(batch[0][0])
+        labels.append(batch[1][0])
+    return np.array(images), np.array(labels)
+
+# Get random images and labels from the test dataset
+random_images, random_labels = get_random_batch(test_dataset)
+
+# Predict the labels using the trained model
+predictions = model.predict(random_images)
+
+# Convert the predicted probabilities to binary predictions
+binary_predictions = [1 if p > 0.5 else 0 for p in predictions]
+
+# Map binary labels and predictions to their respective classes
+class_labels = {0: 'Healthy', 1: 'Defective'}
+true_labels = [class_labels[label] for label in random_labels]
+predicted_labels = [class_labels[prediction] for prediction in binary_predictions]
+
+# Display the images along with their true and predicted labels
+plt.figure(figsize=(15, 5))
+for i in range(5):
+    plt.subplot(1, 5, i+1)
+    plt.imshow(random_images[i])
+    plt.title(f'True: {true_labels[i]}\nPredicted: {predicted_labels[i]}')
+    plt.axis('off')
+plt.show()