Futuristic Image Processor - Copy this Html, Tailwind Component to your project

import-os-import-numpy-as-np-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-from-tensorflow.keras.optimizers-import-Adam-from-tensorflow.keras.preprocessing-import-image-import-matplotlib.pyplot-as-plt-import-cv2-#-OpenCV-library-for-image-processing-#-Define-paths-to-your-dataset-folders-train_dir-=-"C:\\Users\\vedan\\OneDrive\\Desktop\\project\\dataset\\dataset\\brain"-#-ImageDataGenerator-for-data-augmentation-and-preprocessing-train_datagen-=-ImageDataGenerator(-rescale=1./255,-#-Normalize-pixel-values-to-[0,-1]-validation_split=0.2,-#-Use-20%-of-data-for-validation-shear_range=0.2,-#-Random-shearing-for-data-augmentation-zoom_range=0.2,-#-Random-zooming-horizontal_flip=True-#-Random-horizontal-flipping-)-#-Load-training-and-validation-datasets-train_generator-=-train_datagen.flow_from_directory(-train_dir,-target_size=(150,-150),-#-Resize-images-to-150x150-pixels-batch_size=32,-class_mode='binary',-#-Binary-classification-subset='training'-#-Set-as-training-data-)-validation_generator-=-train_datagen.flow_from_directory(-train_dir,-target_size=(150,-150),-batch_size=32,-class_mode='binary',-subset='validation'-#-Set-as-validation-data-)-#-Define-the-CNN-model-model-=-Sequential([-Conv2D(32,-(3,-3),-activation='relu',-input_shape=(150,-150,-3)),-MaxPooling2D(pool_size=(2,-2)),-Conv2D(64,-(3,-3),-activation='relu'),-MaxPooling2D(pool_size=(2,-2)),-Conv2D(128,-(3,-3),-activation='relu'),-MaxPooling2D(pool_size=(2,-2)),-Flatten(),-Dense(512,-activation='relu'),-Dropout(0.5),-Dense(1,-activation='sigmoid')-#-Sigmoid-for-binary-classification-])-model.compile(optimizer=Adam(learning_rate=0.001),-loss='binary_crossentropy',-metrics=['accuracy'])-model.summary()-#-Fit-the-model-history-=-model.fit(-train_generator,-steps_per_epoch=len(train_generator),-validation_data=validation_generator,-epochs=50-)-#-Evaluate-on-validation-data-val_loss,-val_acc-=-model.evaluate(validation_generator)-print(f"Validation-Accuracy:-{val_acc-*-100:.2f}%")-#-Load-and-preprocess-a-single-image-img_path-=-"C:\\Users\\vedan\\OneDrive\\Desktop\\project\\dataset\\dataset\\brain\\yes\\p1-(30).jpg"-#-Example-image-img-=-image.load_img(img_path,-target_size=(150,-150))-#-Resize-image-img_array-=-image.img_to_array(img)-/-255.0-#-Normalize-plt.imshow(img_array)-img_array-=-np.expand_dims(img_array,-axis=0)-#-Expand-dimensions-to-match-input-shape-#-Predict-the-class-(0-for-'no-clot',-1-for-'yes-clot')-prediction-=-model.predict(img_array)-if-prediction->-0.5:-print("Prediction:-Yes,-Blood-clot-detected")-else:-print("Prediction:-No,-No-blood-clot")-#-Apply-Sobel-filter-to-the-original-image-(convert-to-grayscale-first)-img_gray-=-cv2.cvtColor(img_array[0],-cv2.COLOR_RGB2GRAY)-#-Convert-to-grayscale-sobelx-=-cv2.Sobel(img_gray,-cv2.CV_64F,-1,-0,-ksize=3)-#-Sobel-filter-in-X-direction-sobely-=-cv2.Sobel(img_gray,-cv2.CV_64F,-0,-1,-ksize=3)-#-Sobel-filter-in-Y-direction-sobel_combined-=-cv2.sqrt(sobelx**2-+-sobely**2)-#-Combine-both-X-and-Y-#-Display-the-Sobel-filter-result-plt.figure()-plt.subplot(1,-3,-1),-plt.imshow(sobelx,-cmap='gray'),-plt.title('Sobel-X')-plt.subplot(1,-3,-2),-plt.imshow(sobely,-cmap='gray'),-plt.title('Sobel-Y')-plt.subplot(1,-3,-3),-plt.imshow(sobel_combined,-cmap='gray'),-plt.title('Sobel-Combined')-plt.show()-give-me-frontend-for-this,-create-it-extremly-futuristic-use-html-+-css-and-ask-for-a-single-input-of-image-and-show-result-below