import tensorflow as tf
import numpy as np
import cv2
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' 

# This imports our pre-trained Keras model.
# To troubleshoot model loading error, restart the kernel. Ensure Tensorflow 2.12 is installed. 
from tensorflow.keras.models import load_model
model = load_model("/kaggle/input/muzzlecmpd268/tensorflow2/default/1/MuzzleCMPD268.keras", safe_mode=False)

data_generator1 = tf.keras.preprocessing.image.ImageDataGenerator(
)
test_ds = data_generator1.flow_from_directory(
    directory = "/kaggle/input/cmpd268/Split/test",
    target_size=(224, 224),
    color_mode="rgb",
    batch_size=10,
    class_mode="sparse",
    shuffle=True
)

class_names = list(test_ds.class_indices.keys())

test_accuracy = model.evaluate(test_ds, verbose=1)
print(test_accuracy)

y_true = []
y_pred = []
confidences = []

test_root_directory = "/kaggle/input/cmpd268/Split/test"
output_mismatch_path = f'/kaggle/working/misclassified.txt'
output_match_path = f'/kaggle/working/classified.txt'

# Ensure the Reports/SavedOutputs directory exists
os.makedirs(os.path.dirname(output_mismatch_path), exist_ok=True)

# Open both files for writing
with open(output_mismatch_path, 'w') as mismatch_file, open(output_match_path, 'w') as match_file:
    for root, dirs, files in os.walk(test_root_directory):
        folder_name = os.path.basename(root)
        for file in files:
            if file.lower().endswith(('.jpg', '.jpeg', '.png', '.JPG')):  # Case insensitive
                image_path = os.path.join(root, file)

                # Load and preprocess image
                img = tf.keras.utils.load_img(image_path)
                img = tf.image.resize(img, (224, 224))  # Adjust as needed
                img = tf.expand_dims(img, 0)  # Add batch dimension

                # Get predictions
                predictions = model.predict(img)
                score = tf.nn.softmax(predictions[0])
                predicted_class_index = np.argmax(score)
                predicted_class = class_names[predicted_class_index]

                # Store results
                y_true.append(folder_name)
                y_pred.append(predicted_class)
                confidence = 100 * np.max(score)
                confidences.append(confidence)

                log_entry = f"File: {image_path}, Folder: {folder_name}, Predicted: {predicted_class}, Confidence: {confidence:.2f}%\n"

                # Write to respective files
                if folder_name != predicted_class:
                    mismatch_file.write(log_entry)
                    print("Mismatch:", log_entry.strip())
                else:
                    match_file.write(log_entry)
                    print("Match:", log_entry.strip())

print(f"Mismatched outputs saved to: {output_mismatch_path}")
print(f"Matched outputs saved to: {output_match_path}")


from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score

accuracy = accuracy_score(y_true, y_pred)
precision = precision_score(y_true, y_pred, average='weighted')
recall = recall_score(y_true, y_pred, average='weighted')
f1 = f1_score(y_true, y_pred, average='weighted')

print(f"Accuracy: {accuracy}")
print(f"Precision: {precision}")
print(f"Recall: {recall}")
print(f"F1 Score: {f1}")