 Can you tell me the breed of my dog if I send you a picture of it? \n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'Optional: Set to True if you want to fine-tune the model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0% top-1 accuracy\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\'}', metadata={})]


 I need to sort my photos into different folders, based on what the main object is in each photo.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'faster_rcnn_resnet50_v1_640x640\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/faster_rcnn/resnet50_v1_640x640/1\')", \'api_arguments\': {\'url\': \'https://tfhub.dev/tensorflow/faster_rcnn/resnet50_v1_640x640/1\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.6.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\ndetector = hub.load(\'https://tfhub.dev/tensorflow/faster_rcnn/resnet50_v1_640x640/1\')\\nimage_tensor = tf.image.decode_image(tf.io.read_file(\'path/to/image.jpg\'))\\nresult = detector(image_tensor[tf.newaxis, ...])", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'mAP@0.5IOU: 37.1%\'}, \'description\': \'A pre-trained Faster R-CNN model with ResNet-50 as the backbone for object detection in images. The model is trained on the COCO dataset and can detect objects from 80 different classes.\'}', metadata={})]


 I want to make a recommendation system for similar images, can you recommend an API for image feature extraction?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'Optional: Set to True if you want to fine-tune the model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0% top-1 accuracy\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\'}', metadata={})]


 Analyze the following list of tweets and create a vector to find if two of them have similar meaning.\n###Input: [\"I love going to the beach\", \"The beach is my favorite place to visit\", \"What a good day for ice cream\"]\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image Frame Interpolation\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Generate intermediate frames between two input frames\', \'api_name\': \'image-frame-interpolation\', \'api_call\': "hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\')", \'api_arguments\': {\'inputs\': \'A list of two input frames\'}, \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\ninputs = [frame1, frame2]\\ninterpolated_frame = hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\').call(inputs)", \'performance\': {\'dataset\': \'Adobe240fps\', \'accuracy\': \'PSNR: 29.8 dB, SSIM: 0.902\'}, \'description\': \'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\'}', metadata={})]


 I need to analyze numerous article headlines and sort them based on their similarity.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Object detection\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\'\\nloaded_model = hub.load(model_id)\\n\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\')\\nimage_np = np.array(image)\\n\\ninput_tensor = tf.convert_to_tensor(image_np)\\ninput_tensor = input_tensor[tf.newaxis, ...]\\n\\noutput_dict = loaded_model(input_tensor)\\n\\nboxes = output_dict[\'detection_boxes\'][0].numpy()\\nscores = output_dict[\'detection_scores\'][0].numpy()\\nclasses = output_dict[\'detection_classes\'][0].numpy().astype(np.int32)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'0.320\'}, \'description\': \'This API provides a pre-trained object detection model using TensorFlow Hub. The model is based on the SSD MobileNet V2 architecture and is trained on the COCO dataset. It can be used to detect multiple objects in an image and returns their class, bounding box coordinates, and confidence scores.\'}', metadata={})]


 We want to know which animal we will find in the picture taken during our vacation.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'Optional: Set to True if you want to fine-tune the model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0% top-1 accuracy\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\'}', metadata={})]


 Summarize an article by representing its content in a high-dimensional vector format.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\')", \'api_arguments\': {\'model_id\': \'A string representing the TensorFlow Hub model URL\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.6.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\'\\nmodel = hub.load(model_id)\\n\\n# Load an image\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\')\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\n\\n# Detect objects in the image\\nresult = model(image)\\n\\n# Print detected objects\\nfor obj in result[\'detection_boxes\']:\\n    print(obj)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'mAP@0.5IOU\'}, \'description\': \'A pre-trained TensorFlow Hub model for detecting objects in images using the Single Shot MultiBox Detector (SSD) with MobileNetV2 backbone.\'}', metadata={})]


 Show me how I can convert this sentence \"I enjoy machine learning and natural language processing\" into a high-dimensional vector.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embed text data for machine learning models\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\')", \'api_arguments\': {\'model_id\': \'A string representing the URL or path to the TensorFlow Hub model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\'\\nembed = hub.load(model_id)\\nembeddings = embed([\'Hello, world!\'])\\nprint(embeddings)", \'performance\': {\'dataset\': \'Wikipedia\', \'accuracy\': \'Not specified\'}, \'description\': \'Load a pre-trained text embedding model from TensorFlow Hub, and use it to convert text data into fixed-size vectors that can be used as input for machine learning models.\'}', metadata={})]


 Provide a solution to recognize different types of fruits through image processing.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Pre-trained image classification models\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\'\\nmodel = hub.load(model_id)\\n\\nimage = tf.keras.preprocessing.image.load_img(\'image_path\', target_size=(224, 224))\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\nimage_array = tf.expand_dims(image_array, 0)\\n\\npredictions = model(image_array)\\nclass_index = tf.argmax(predictions, axis=-1).numpy()[0]", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image classification model using MobileNetV2 architecture, which can recognize 1000 different classes from the ImageNet dataset.\'}', metadata={})]


 I have recorded some animal sounds and I want to analyze them to identify the animal species.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'Optional: Set to True if you want to fine-tune the model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0% top-1 accuracy\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\'}', metadata={})]


 Can you provide me with the semantic similarity of two sentences \"I love pizza\" and \"Pizza is my favorite food\"?\n###Input: \"I love pizza\", \"Pizza is my favorite food\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embedding text into a high-dimensional vector\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': \'input: A tensor of strings, output: A tensor of shape (num_examples, 512)\', \'python_environment_requirements\': \'tensorflow, tensorflow_hub\', \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello world!\', \'I love machine learning.\'])\\nprint(embeddings)", \'performance\': {\'dataset\': \'SNLI, MultiNLI, SICK\', \'accuracy\': \'86.3%, 76.6%, 88.0%\'}, \'description\': \'A pre-trained model for encoding text into a 512-dimensional vector. Useful for various NLP tasks like semantic similarity, sentiment analysis, and text classification.\'}', metadata={})]


 My friend sent me a picture of an animal and I want to find out the type of animal in the picture.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\')", \'api_arguments\': [{\'name\': \'model_id\', \'type\': \'string\', \'description\': \'The ID of the model to load from TensorFlow Hub\'}], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\'\\ndetector = hub.load(model_id)\\n\\n# Load an image, preprocess it, and run the detector\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\')\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\ninput_image = tf.image.resize(input_image, (300, 300))\\ninput_image = tf.expand_dims(input_image, 0)\\n\\n# Run the detector\\noutput = detector(input_image)\\n\\n# Process the output\\nfor i in range(output[\'num_detections\']):\\n    if output[\'detection_scores\'][i] > 0.5:\\n        print(\'Detected object:\', output[\'detection_class_entities\'][i], \'with confidence:\', output[\'detection_scores\'][i])", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'mAP@0.5IOU\'}, \'description\': \'A pre-trained object detection model that can detect objects in images using TensorFlow Hub\'}', metadata={})]


 Summarize the article and identify the key topics discussed.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image pose detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detects human poses in images\', \'api_name\': \'movenet\', \'api_call\': "hub.load(\'https://tfhub.dev/google/movenet/singlepose/lightning/3\')", \'api_arguments\': \'image_tensor\', \'python_environment_requirements\': \'tensorflow, tensorflow_hub\', \'example_code\': "import tensorflow_hub as hub\\nmodel = hub.load(\'https://tfhub.dev/google/movenet/singlepose/lightning/3\')\\nimage_tensor = ... # Load image tensor\\noutputs = model(image_tensor)\\nkeypoints = outputs[\'output_0\'].numpy()", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'70.4 mAP\'}, \'description\': \'MoveNet is a family of efficient and accurate pose detection models designed for real-time applications. This model detects 17 key points of a single person in an image.\'}', metadata={})]


 Help me identify what's in a photo using a model that is pre-trained on the ImageNet dataset.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'URL: https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow_hub as hub\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])\\nmodel.build([None, 224, 224, 3])\\nmodel.summary()", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.9%\'}, \'description\': \'This API provides a pre-trained MobileNetV2 model for extracting feature vectors from images. The model is trained on the ImageNet dataset and achieves 71.9% accuracy in classification tasks.\'}', metadata={})]


 Translate the following sentence into every possible language and create a high-dimensional vector representation for each translated sentence.\n###Input: \"The current world population is 7.9 billion.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Image feature extraction\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'url\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   input_shape=(224, 224, 3),\\n                   trainable=False)\\n])\\n\\nx = tf.keras.layers.Input(shape=(224, 224, 3))\\nfeatures = model(x)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.9%\'}, \'description\': \'This module uses MobileNet V2 architecture for image feature vector extraction. It accepts a batch of images of shape (224, 224, 3) and returns a 1280-dimensional feature vector for each image.\'}', metadata={})]


 I am trying to find objects in an image with high accuracy. Help me use a pre-trained model to detect objects.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Object detection\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/efficientdet/lite0/detection/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'import tensorflow as tf\', \'import tensorflow_hub as hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/tensorflow/efficientdet/lite0/detection/1\'\\nloaded_model = hub.load(model_id)\\n\\nimage = tf.keras.preprocessing.image.load_img(\'image_path\')\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\nimage = image / 255.0\\nimage = tf.expand_dims(image, 0)\\n\\noutput = loaded_model(image)", \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'Load a pre-trained TensorFlow Hub model for image object detection and use it to detect objects in an input image.\'}', metadata={})]


 I have a fashion catalog and I want to find visually similar clothes from the catalog using the images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'[224, 224, 3]\', \'output_shape\': \'[1280]\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n  hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', input_shape=[224, 224, 3], output_shape=[1280]),\\n  tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.9%\'}, \'description\': \'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\'}', metadata={})]


 I want to identify what objects are in the image I took on my vacation. I want a lightweight and fast model for this task.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'Optional: Set to True if you want to fine-tune the model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0% top-1 accuracy\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\'}', metadata={})]


 Could you identify the object in an image? Which pre-trained model can I use?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in an image\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\'\\ndetector = hub.load(model_id)\\n\\nimage = tf.image.decode_jpeg(tf.io.read_file(\'image.jpg\'))\\nresult = detector([image])", \'performance\': {\'dataset\': \'OpenImages_v4\', \'accuracy\': \'N/A\'}, \'description\': \'This API allows you to detect objects in an image using TensorFlow Hub and a pre-trained model.\'}', metadata={})]


 Find out what an image is about by using a pre-trained image classification model, considering its contents.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Pre-trained image classification models\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\'\\nmodel = hub.load(model_id)\\n\\n# Load an image and preprocess it\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/your/image.jpg\', target_size=(224, 224))\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\nimage_array = tf.expand_dims(image_array, 0)\\n\\n# Make predictions\\npredictions = model(image_array)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'This API provides pre-trained image classification models from TensorFlow Hub. Users can load a model using its model_id and make predictions on images.\'}', metadata={})]


 Develop a model for product categories from images I provide.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading pre-trained models for image classification\', \'api_name\': \'mobilenet_v2\', \'api_call\': "tf.keras.Sequential([hub.KerasLayer(\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\')])", \'api_arguments\': {\'url\': \'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\', \'input_shape\': \'(224, 224, 3)\'}, \'python_environment_requirements\': {\'tensorflow\': \'2.x\', \'tensorflow_hub\': \'0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nmodel = tf.keras.Sequential([hub.KerasLayer(\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\', input_shape=(224,224,3))])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image classification model using MobileNet V2 architecture from TensorFlow Hub. It is capable of classifying images into 1000 categories with 71% accuracy on the ImageNet dataset.\'}', metadata={})]


 Can you please provide an API that will help us find semantic similarity between multiple sentences?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Generate embeddings for audio files\', \'api_name\': \'audio_embedding_model\', \'api_call\': "hub.load(\'https://tfhub.dev/google/yamnet/1\')", \'api_arguments\': {\'audio_inputs\': \'Tensor of shape [batch_size, num_samples] containing audio signals\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = hub.load(\'https://tfhub.dev/google/yamnet/1\')\\n\\n# Load an audio file\\naudio_data = ... # Load audio data as a tensor\\n\\n# Generate embeddings\\nembeddings = model(audio_data)", \'performance\': {\'dataset\': \'YAMNet\', \'accuracy\': \'81.6% top-1 accuracy on AudioSet\'}, \'description\': \'This API allows you to generate embeddings for audio files using a pre-trained YAMNet model from TensorFlow Hub. The embeddings can be used for various tasks such as audio classification, similarity search, and more.\'}', metadata={})]


 Describe an object in an image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in an image\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nmodel_id = \'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\'\\ndetector = hub.load(model_id)\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\')\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\ninput_image = tf.expand_dims(input_image, 0)\\ndetections = detector(input_image)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'Not specified\'}, \'description\': \'A pre-trained object detection model that can detect multiple objects in an image using TensorFlow Hub.\'}', metadata={})]


 I have a collection of paintings and I'm looking for a way to generate a list of their most common features.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading and using pre-trained models for image classification\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': {\'tensorflow\': \'>=2.0\', \'tensorflow_hub\': \'>=0.12\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\'\\nmodel = hub.load(model_id)\\n\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\', target_size=(224, 224))\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\nimage_array = tf.expand_dims(image_array, 0)\\n\\npredictions = model(image_array)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'94.1%\'}, \'description\': \'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\'}', metadata={})]


 We have new images containing scenes from different locations. Let's classify these locations by implementing a pre-trained image classification model based on MobileNet V2.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Image classification model\', \'api_name\': \'imagenet-mobilenet_v2_130_224-classification\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4\', \'output_shape\': \'[1001]\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4\')\\n])\\n\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\', target_size=(224, 224))\\ninput_array = tf.keras.preprocessing.image.img_to_array(image)\\ninput_array = tf.expand_dims(input_array, 0)\\n\\npredictions = model.predict(input_array)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'0.856\'}, \'description\': \'This model is a pre-trained image classification model based on MobileNetV2 architecture. It can classify images into 1000 different categories.\'}', metadata={})]


 Can you help extract meaning from a list of sentences by converting them into high-dimensional vectors?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text preprocessing\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Text preprocessing\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': \'text_input\', \'python_environment_requirements\': \'tensorflow, tensorflow_hub\', \'example_code\': "import tensorflow_hub as hub; embed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\'); embeddings = embed([\'Hello world!\'])", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'0.78\'}, \'description\': \'This module uses the Universal Sentence Encoder model to preprocess text by converting it into embeddings.\'}', metadata={})]


 We have a list of YouTube video descriptions, and we want to find similar videos based on the descriptions, so start with getting the embeddings of each description.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Video classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Video classification model\', \'api_name\': \'Video_classification_model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/Video_classification_model_id/1\')", \'api_arguments\': [], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'Kinetics-400\', \'accuracy\': \'\'}, \'description\': \'A pre-trained video classification model available on TensorFlow Hub for classifying videos based on their content.\'}', metadata={})]


 I want a tool to help me count how many cars are parked in our parking lot.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'Optional: Set to True if you want to fine-tune the model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0% top-1 accuracy\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\'}', metadata={})]


 Can you help me classify the species of a bird that I have in my photo? \n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'[224, 224, 3]\', \'output_shape\': \'[1280]\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n  hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', input_shape=[224, 224, 3], output_shape=[1280]),\\n  tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.9%\'}, \'description\': \'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\'}', metadata={})]


 Can you help me analyze the contents of a photograph?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Extract image features\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'False\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.5.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),  # Can be True, see below.\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A compact representation of MobileNetV2 model trained on ImageNet dataset for extracting image features using TensorFlow Hub.\'}', metadata={})]


 I have some random objects lying around my home office desk. Could you please create a model that can classify these objects for me?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nmodel_id = \'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\'\\ndetector = hub.load(model_id)\\nimage = tf.image.decode_jpeg(tf.io.read_file(\'path/to/image.jpg\'))\\nresult = detector(image[tf.newaxis, ...])", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'mAP\'}, \'description\': \'This API allows you to detect objects in images using a pre-trained model from TensorFlow Hub. The model is based on the SSD MobileNet V2 architecture and has been trained on the COCO dataset.\'}', metadata={})]


 Analyze various photograps and determine what objects each one contains.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Image feature extraction\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'url\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   input_shape=(224, 224, 3),\\n                   trainable=False)\\n])\\n\\nx = tf.keras.layers.Input(shape=(224, 224, 3))\\nfeatures = model(x)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.9%\'}, \'description\': \'This module uses MobileNet V2 architecture for image feature vector extraction. It accepts a batch of images of shape (224, 224, 3) and returns a 1280-dimensional feature vector for each image.\'}', metadata={})]


 Determine the type of animal we found during the hike. It's important to know if it could be dangerous.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'Optional: Set to True if you want to fine-tune the model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0% top-1 accuracy\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\'}', metadata={})]


 Expedia wants to recommend hotels based on the image of their rooms. They need a way to extract features from these images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Extract features from images\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'[batch_size, 224, 224, 3]\', \'output_shape\': \'[batch_size, 1280]\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.8.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                    input_shape=(224,224,3),\\n                    trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained image feature vector model that can be used to extract features from images. The model is based on the MobileNetV2 architecture and is trained on the ImageNet dataset.\'}', metadata={})]


 Automate the process of training a deep learning model to distinguish between images of cats and dogs.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image Frame Interpolation\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Generate intermediate frames between two input frames\', \'api_name\': \'image-frame-interpolation\', \'api_call\': "hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\')", \'api_arguments\': {\'inputs\': \'A list of two input frames\'}, \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\ninputs = [frame1, frame2]\\ninterpolated_frame = hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\').call(inputs)", \'performance\': {\'dataset\': \'Adobe240fps\', \'accuracy\': \'PSNR: 29.8 dB, SSIM: 0.902\'}, \'description\': \'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\'}', metadata={})]


 We need to detect broken objects in an assembly line from images. Suggest an appropriate API call that can help us generate feature vectors for this purpose.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in an image\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\'\\ndetector = hub.load(model_id)\\n\\nimage = tf.image.decode_jpeg(tf.io.read_file(\'image.jpg\'))\\nresult = detector([image])", \'performance\': {\'dataset\': \'OpenImages_v4\', \'accuracy\': \'N/A\'}, \'description\': \'This API allows you to detect objects in an image using TensorFlow Hub and a pre-trained model.\'}', metadata={})]


 I need a model that can classify different architectural styles in images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Image classification model\', \'api_name\': \'imagenet-mobilenet_v2_130_224-classification\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4\', \'output_shape\': \'[1001]\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4\')\\n])\\n\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\', target_size=(224, 224))\\ninput_array = tf.keras.preprocessing.image.img_to_array(image)\\ninput_array = tf.expand_dims(input_array, 0)\\n\\npredictions = model.predict(input_array)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'0.856\'}, \'description\': \'This model is a pre-trained image classification model based on MobileNetV2 architecture. It can classify images into 1000 different categories.\'}', metadata={})]


 Provide me a pre-trained model to classify imported images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading pre-trained models\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/inception_v3/classification/5\'\\nmodel = hub.load(model_id)\\n\\n# Example: Load an image and classify it\\n# image = ...  # Load an image\\n# result = model(image)\\n# print(result)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Not provided\'}, \'description\': \'Load a pre-trained image classification model from TensorFlow Hub and use it to classify images.\'}', metadata={})]


 I'd like to automatically classify food images at a grocery store to add labels to the products.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image segmentation\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Image segmentation model\', \'api_name\': \'deeplabv3_257_mv_gpu\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\')", \'api_arguments\': {\'input\': \'Image tensor\', \'output\': \'Segmentation mask tensor\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.6.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nmodel = hub.load(\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\')\\nimage_tensor = tf.image.resize(image, (257, 257))\\nsegmentation_mask = model(image_tensor)", \'performance\': {\'dataset\': \'PASCAL VOC 2012\', \'accuracy\': \'0.899\'}, \'description\': \'DeepLab is a state-of-the-art deep learning model for semantic image segmentation, where the goal is to assign semantic labels (e.g., person, dog, cat and so on) to every pixel in the input image. This model is trained on the PASCAL VOC 2012 dataset.\'}', metadata={})]


 Analyze a collection of images to help me recognize my dog's breed.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Pre-trained image classification models\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\'\\nmodel = hub.load(model_id)\\nimage = tf.random.normal([1, 224, 224, 3])\\npredictions = model(image)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Varies depending on the model\'}, \'description\': \'A collection of pre-trained image classification models available on TensorFlow Hub, which can be used to classify images into various categories.\'}', metadata={})]


 Create a feature vector from an image using a pre-trained model for use in an image similarity search.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Extract image features\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1\'\\nmodel = hub.load(model_id)\\nimage = tf.random.normal([1, 299, 299, 3])\\nfeatures = model(image)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Not specified\'}, \'description\': \'Load a pre-trained image feature vector model from TensorFlow Hub and use it to extract features from an image.\'}', metadata={})]


 I have an app that allows users to take pictures of objects with their phones, and I want to know what those objects are.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading and using pre-trained models for image classification\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': {\'tensorflow\': \'>=2.0\', \'tensorflow_hub\': \'>=0.12\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\'\\nmodel = hub.load(model_id)\\n\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\', target_size=(224, 224))\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\nimage_array = tf.expand_dims(image_array, 0)\\n\\npredictions = model(image_array)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'94.1%\'}, \'description\': \'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\'}', metadata={})]


 What is the similarity between two sentences? First encode them as vectors, and then calculate the cosine similarity.\n###Input: {\"sentence1\": \"The quick brown fox jumps over the lazy dog.\", \"sentence2\": \"A sly red vixen quickly leaps above the dozing hound.\"}\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image Frame Interpolation\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Generate intermediate frames between two input frames\', \'api_name\': \'image-frame-interpolation\', \'api_call\': "hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\')", \'api_arguments\': {\'inputs\': \'A list of two input frames\'}, \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\ninputs = [frame1, frame2]\\ninterpolated_frame = hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\').call(inputs)", \'performance\': {\'dataset\': \'Adobe240fps\', \'accuracy\': \'PSNR: 29.8 dB, SSIM: 0.902\'}, \'description\': \'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\'}', metadata={})]


 My CEO sent me an email, and he is asking me to analyze the emails' tone to see whether it is positive or negative. We need to get an embedding for this email before classifying the sentiment.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embed text data for machine learning models\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\')", \'api_arguments\': {\'model_id\': \'A string representing the URL or path to the TensorFlow Hub model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\'\\nembed = hub.load(model_id)\\nembeddings = embed([\'Hello, world!\'])\\nprint(embeddings)", \'performance\': {\'dataset\': \'Wikipedia\', \'accuracy\': \'Not specified\'}, \'description\': \'Load a pre-trained text embedding model from TensorFlow Hub, and use it to convert text data into fixed-size vectors that can be used as input for machine learning models.\'}', metadata={})]


 We need recommendations for similar restaurants. Calculate the embedding of restaurant descriptions to create a list of the most similar establishments.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embedding text data\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': \'Text input\', \'python_environment_requirements\': \'TensorFlow, TensorFlow Hub\', \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello, world!\', \'How are you?\'])", \'performance\': {\'dataset\': \'Various text sources\', \'accuracy\': \'High similarity scores for semantically similar sentences\'}, \'description\': \'A text embedding model that converts text data into high-dimensional vectors, capturing semantic meaning and enabling various natural language processing tasks.\'}', metadata={})]


 Convert a book summary into a high-dimensional vector for determining similarity to another book's summary.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embedding text into a high-dimensional vector\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': \'input_text\', \'python_environment_requirements\': \'tensorflow, tensorflow_hub\', \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello world\'])\\nprint(embeddings)", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'Pearson correlation coefficient of 0.803521\'}, \'description\': \'This API provides a method to convert text into a high-dimensional vector using the Universal Sentence Encoder from TensorFlow Hub. It can be used for tasks such as semantic similarity and clustering.\'}', metadata={})]


 Tell me how to convert post titles into numerical vectors so that I can find similar titles later on.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embedding text using pre-trained models\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello world!\'])", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'85.1%\'}, \'description\': \'A pre-trained text embedding model that can be used to convert text into high-dimensional vectors for various natural language processing tasks.\'}', metadata={})]


 We want to analyze security camera footage and determine if there are any people in the frame.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'Optional: Set to True if you want to fine-tune the model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0% top-1 accuracy\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\'}', metadata={})]


 I took a picture of my park today. I want to know what kinds of objects I captured in the picture.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\')", \'api_arguments\': [{\'name\': \'model_id\', \'type\': \'string\', \'description\': \'The ID of the model to load from TensorFlow Hub\'}], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\'\\ndetector = hub.load(model_id)\\n\\n# Load an image, preprocess it, and run the detector\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\')\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\ninput_image = tf.image.resize(input_image, (300, 300))\\ninput_image = tf.expand_dims(input_image, 0)\\n\\n# Run the detector\\noutput = detector(input_image)\\n\\n# Process the output\\nfor i in range(output[\'num_detections\']):\\n    if output[\'detection_scores\'][i] > 0.5:\\n        print(\'Detected object:\', output[\'detection_class_entities\'][i], \'with confidence:\', output[\'detection_scores\'][i])", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'mAP@0.5IOU\'}, \'description\': \'A pre-trained object detection model that can detect objects in images using TensorFlow Hub\'}', metadata={})]


 Our application needs to identify objects in an image. Extract them using pre-trained model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Object detection in images\', \'api_name\': \'ssd_mobilenet_v2\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/fpnlite_640x640/1\')", \'api_arguments\': {\'image\': \'Input image tensor\', \'num_detections\': \'Number of detected objects\', \'detection_boxes\': \'Bounding boxes for detected objects\', \'detection_classes\': \'Class labels for detected objects\', \'detection_scores\': \'Confidence scores for detected objects\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.6.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\ndetector = hub.load(\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/fpnlite_640x640/1\')\\n\\n# Preprocess image\\ninput_image = tf.image.resize(image, (640, 640))\\ninput_image = tf.expand_dims(input_image, axis=0)\\n\\n# Run detector\\noutput = detector(input_image)\\n\\n# Extract results\\nnum_detections = output[\'num_detections\'].numpy()\\ndetection_boxes = output[\'detection_boxes\'].numpy()\\ndetection_classes = output[\'detection_classes\'].numpy()\\ndetection_scores = output[\'detection_scores\'].numpy()", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'mAP 0.32\'}, \'description\': \'A pre-trained object detection model based on the SSD Mobilenet V2 architecture, capable of detecting multiple objects in an image. The model is trained on the COCO dataset and achieves a mean Average Precision (mAP) of 0.32.\'}', metadata={})]


 Compare the similarity of two customer reviews for a product and see if they are positive or negative.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image Frame Interpolation\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Generate intermediate frames between two input frames\', \'api_name\': \'image-frame-interpolation\', \'api_call\': "hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\')", \'api_arguments\': {\'inputs\': \'A list of two input frames\'}, \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\ninputs = [frame1, frame2]\\ninterpolated_frame = hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\').call(inputs)", \'performance\': {\'dataset\': \'Adobe240fps\', \'accuracy\': \'PSNR: 29.8 dB, SSIM: 0.902\'}, \'description\': \'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\'}', metadata={})]


 I took a photo of my backyard and want to find similar images online. Please extract features from the photo so I can search for similar photos.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embedding text data\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': \'Text input\', \'python_environment_requirements\': \'TensorFlow, TensorFlow Hub\', \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello, world!\', \'How are you?\'])", \'performance\': {\'dataset\': \'Various text sources\', \'accuracy\': \'High similarity scores for semantically similar sentences\'}, \'description\': \'A text embedding model that converts text data into high-dimensional vectors, capturing semantic meaning and enabling various natural language processing tasks.\'}', metadata={})]


 I want to compare the meaning similarity of two sentences. Find a way to encode the sentences and calculate their similarity.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image Frame Interpolation\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Generate intermediate frames between two input frames\', \'api_name\': \'image-frame-interpolation\', \'api_call\': "hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\')", \'api_arguments\': {\'inputs\': \'A list of two input frames\'}, \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\ninputs = [frame1, frame2]\\ninterpolated_frame = hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\').call(inputs)", \'performance\': {\'dataset\': \'Adobe240fps\', \'accuracy\': \'PSNR: 29.8 dB, SSIM: 0.902\'}, \'description\': \'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\'}', metadata={})]


 My retail client wishes to recognize when a doorbell is rang in audio files of their store.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Speech-to-Text\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Speech Recognition\', \'api_name\': \'wav2vec2_large_960h\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/vasudevgupta7/wav2vec2/1\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'tensorflow, tensorflow_hub\', \'example_code\': \'\', \'performance\': {\'dataset\': \'LibriSpeech\', \'accuracy\': \'0.960\'}, \'description\': \'A pre-trained model for converting spoken language in audio files into written text using TensorFlow Hub.\'}', metadata={})]


 For a building project, we need to analyze pictures of different rocks and determine their structures. Obtain the feature vector of a rock image first.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading pre-trained models for image classification\', \'api_name\': \'imagenet_mobilenet_v2_100_224_classification\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': \'URL of the pre-trained model\', \'python_environment_requirements\': \'TensorFlow 2.x, TensorFlow Hub\', \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')\\n\\n# Load an image and preprocess it\\ndef load_image(file_path):\\n    img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\n    img_array = tf.keras.preprocessing.image.img_to_array(img)\\n    img_array = tf.expand_dims(img_array, 0)  # Create a batch\\n    return img_array\\n\\n# Predict the class of the image\\nimg_array = load_image(\'path/to/your/image.jpg\')\\npredictions = model(img_array)\\n\\n# Get the class with the highest probability\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\'}', metadata={})]


 I need to know what is in this photo I took. Can you tell me?\n###Input: {\"image\": \"path/to/image.jpg\"}\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Object detection in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\'\\ndetector = hub.load(model_id)\\n\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\')\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\nimage = tf.expand_dims(image, axis=0)\\n\\nresult = detector(image)\\n", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'0.320\'}, \'description\': \'This API allows for object detection in images using a pre-trained model from TensorFlow Hub. The model used in this example is the SSD MobileNet V2, which has been trained on the COCO dataset.\'}', metadata={})]


 Identify objects in an image to let me know what objects are in the image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in an image\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\'\\ndetector = hub.load(model_id)\\n\\nimage = tf.image.decode_jpeg(tf.io.read_file(\'image.jpg\'))\\nresult = detector([image])", \'performance\': {\'dataset\': \'OpenImages_v4\', \'accuracy\': \'N/A\'}, \'description\': \'This API allows you to detect objects in an image using TensorFlow Hub and a pre-trained model.\'}', metadata={})]


 My friend sent me an image of an animal, and I am not sure what kind of animal it is. Can you tell me what animal is in the image?\n###Input: \"<path_to_image>\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading a pre-trained model\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/inception_v3/classification/5\'\\nmodel = hub.load(model_id)\\n\\n# Load image and preprocess\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\', target_size=(299, 299))\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\nimage = tf.keras.applications.inception_v3.preprocess_input(image)\\n\\n# Make prediction\\npredictions = model(tf.expand_dims(image, 0))\\n\\n# Decode predictions\\nclass_names = tf.keras.applications.inception_v3.decode_predictions(predictions.numpy(), top=5)\\nfor class_name in class_names[0]:\\n    print(class_name)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'0.779\'}, \'description\': \'This API allows you to load a pre-trained Inception V3 model from TensorFlow Hub for image classification tasks. The model is trained on the ImageNet dataset and achieves an accuracy of 77.9%.\'}', metadata={})]


 For an image classification task, we need a model capable of recognizing and differentiating between various object types. Please load a pre-trained model for this purpose.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading a pre-trained model\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/inception_v3/classification/5\'\\nmodel = hub.load(model_id)\\n\\n# Load image and preprocess\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\', target_size=(299, 299))\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\nimage = tf.keras.applications.inception_v3.preprocess_input(image)\\n\\n# Make prediction\\npredictions = model(tf.expand_dims(image, 0))\\n\\n# Decode predictions\\nclass_names = tf.keras.applications.inception_v3.decode_predictions(predictions.numpy(), top=5)\\nfor class_name in class_names[0]:\\n    print(class_name)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'0.779\'}, \'description\': \'This API allows you to load a pre-trained Inception V3 model from TensorFlow Hub for image classification tasks. The model is trained on the ImageNet dataset and achieves an accuracy of 77.9%.\'}', metadata={})]


 In order to analyze customer feedback from my online store, I need a way to convert their feedback into a numeric format that can be processed by my algorithms.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text preprocessing\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading\', \'api_name\': \'tensorflow_hub.KerasLayer\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/nnlm-en-dim128/2\')", \'api_arguments\': {\'handle\': \'A string, the handle of the Hub module to load\', \'kwargs\': \'Optional keyword arguments\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.8.0\'}, \'example_code\': \'import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(handle="https://tfhub.dev/google/nnlm-en-dim128/2")\\n])\', \'performance\': {\'dataset\': \'NNLM\', \'accuracy\': \'N/A\'}, \'description\': \'Loads a TensorFlow Hub module as a Keras Layer, allowing users to incorporate pre-trained models into their own custom models.\'}', metadata={})]


 I'm working on an app for smart parking stations, and I would like to detect and recognize the car plates from our CCTV footages.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Extract features from images\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'[None, 224, 224, 3]\', \'output_shape\': \'[None, 1280]\', \'trainable\': \'False\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.7.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                    input_shape=(224,224,3), trainable=False)\\n])\\n\\n# Load an image and preprocess it\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\', target_size=(224, 224))\\ninput_array = tf.keras.preprocessing.image.img_to_array(image)\\ninput_array = tf.expand_dims(input_array, 0)\\n\\n# Get the feature vector\\nfeatures = model.predict(input_array)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image feature vector model using MobileNetV2 architecture on ImageNet dataset. It can be used to extract features from images for various tasks like image classification, object detection, and image similarity.\'}', metadata={})]


 We need to build a car and bike image classifier. First, we need to extract features from the images to use for training the model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'(224, 224, 3)\', \'output_shape\': \'(1280,)\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   input_shape=(224, 224, 3),\\n                   output_shape=(1280,))\\n])\\n\\n# Use the model to extract features from an image\\nfeature_vector = model.predict(image)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\'}', metadata={})]


 I want to compare the semantic similarity between two sentences. Give me the embeddings using pre-trained models.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image Frame Interpolation\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Generate intermediate frames between two input frames\', \'api_name\': \'image-frame-interpolation\', \'api_call\': "hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\')", \'api_arguments\': {\'inputs\': \'A list of two input frames\'}, \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\ninputs = [frame1, frame2]\\ninterpolated_frame = hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\').call(inputs)", \'performance\': {\'dataset\': \'Adobe240fps\', \'accuracy\': \'PSNR: 29.8 dB, SSIM: 0.902\'}, \'description\': \'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\'}', metadata={})]


 Sniff out the odd one among the following sentences: \"The party was amazing.\", \"Call a doctor.\", \"The conference was well organized.\"\n###Input: ['The party was amazing.', 'Call a doctor.', 'The conference was well organized.']\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Image classification using a pre-trained model\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\')", \'api_arguments\': [\'model_id: The ID of the pre-trained model to be used for image classification\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/inception_v3/classification/5\'\\nmodel = hub.load(model_id)\\n\\n# Load an image and preprocess it\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\', target_size=(299, 299))\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\nimage = tf.keras.applications.inception_v3.preprocess_input(image)\\n\\n# Make a prediction\\npredictions = model(tf.expand_dims(image, 0))", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'0.779\'}, \'description\': \'This API allows users to perform image classification using a pre-trained TensorFlow Hub model. The model can be loaded using the model_id, and the API provides functionality for loading and preprocessing images, as well as making predictions.\'}', metadata={})]


 We want to recommend similar fashion items based on a user's uploaded image. Extract image features and compare them with our fashion database.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'Optional: Set to True if you want to fine-tune the model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0% top-1 accuracy\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\'}', metadata={})]


 When creating a mobile application that uses image recognition, it should be able to determine what items are in the photo.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading pre-trained models\', \'api_name\': \'imagenet_mobilenet_v2_100_224_classification\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': {\'url\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.6.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')\\n\\ndef predict(image):\\n    logits = model(image)\\n    return logits", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained MobileNetV2 model for image classification on the ImageNet dataset, with a 224x224 input size and 100% depth multiplier. This model is optimized for low-latency and small size, making it suitable for mobile and edge devices.\'}', metadata={})]


 I have some images and I want to compare them efficiently. Extract the features of the images so I can perform similarity analysis.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'[224, 224, 3]\', \'output_shape\': \'[1280]\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n  hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', input_shape=[224, 224, 3], output_shape=[1280]),\\n  tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.9%\'}, \'description\': \'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\'}', metadata={})]


 Manage the camera in the smartphone to recognize multiple objects during a forest walk.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'Model ID\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\')", \'api_arguments\': {\'model_id\': \'The model ID of the TensorFlow Hub object detection model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\'\\ndetector = hub.load(model_id)\\n\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\')\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\ninput_tensor = tf.convert_to_tensor(image_array)\\ninput_tensor = input_tensor[tf.newaxis, ...]\\n\\ndetections = detector(input_tensor)\\n", \'performance\': {\'dataset\': \'COCO2017\', \'accuracy\': \'mAP@0.5IOU = 0.244\'}, \'description\': \'A pre-trained TensorFlow Hub object detection model that can detect multiple objects in an image, based on the SSD MobileNet V2 architecture.\'}', metadata={})]


 A tourist application wants to give real-time object recognition to help tourists when they take a picture of an object in the city.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Load and use an object detection model\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\')", \'api_arguments\': {\'model_id\': \'The id of the model to be loaded from TensorFlow Hub\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\'\\nmodel = hub.load(model_id)\\n\\nimage = tf.image.decode_jpeg(tf.io.read_file(\'path/to/image.jpg\'))\\ndetected_objects = model(image[tf.newaxis, ...])", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'Unknown\'}, \'description\': \'Load and use an object detection model from TensorFlow Hub for detecting objects in images.\'}', metadata={})]


 We want to help users identify the species of a bird in a given picture.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\'\\ndetector = hub.load(model_id)\\n\\nimage = tf.keras.preprocessing.image.load_img(\'image.jpg\')\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\ninput_image = tf.expand_dims(input_image, 0)\\n\\nresult = detector(input_image)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'mAP\'}, \'description\': \'This API allows for object detection in images using TensorFlow Hub and the EfficientDet model. It can be used to load a pre-trained model and detect objects in a given image.\'}', metadata={})]


 I want to analyze a list of customer reviews to gain insight into their sentiment.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text preprocessing\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading\', \'api_name\': \'tensorflow_hub.KerasLayer\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/nnlm-en-dim128/2\')", \'api_arguments\': {\'handle\': \'A string, the handle of the Hub module to load\', \'kwargs\': \'Optional keyword arguments\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.8.0\'}, \'example_code\': \'import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(handle="https://tfhub.dev/google/nnlm-en-dim128/2")\\n])\', \'performance\': {\'dataset\': \'NNLM\', \'accuracy\': \'N/A\'}, \'description\': \'Loads a TensorFlow Hub module as a Keras Layer, allowing users to incorporate pre-trained models into their own custom models.\'}', metadata={})]


 Gather high-dimensional embeddings of some scientific abstracts to make a similarity-based network analysis.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embedding text into high-dimensional space\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': \'input_text\', \'python_environment_requirements\': \'tensorflow, tensorflow_hub\', \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello world\'])", \'performance\': {\'dataset\': \'various\', \'accuracy\': \'not specified\'}, \'description\': \'A pre-trained model that encodes text into 512-dimensional embeddings using a deep averaging network (DAN). Useful for a variety of natural language processing tasks.\'}', metadata={})]


 Use a pre-trained model to find out traits that are similar to buildings in images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'(224, 224, 3)\', \'output_shape\': \'(1280,)\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   input_shape=(224, 224, 3),\\n                   output_shape=(1280,))\\n])\\n\\n# Use the model to extract features from an image\\nfeature_vector = model.predict(image)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\'}', metadata={})]


 I need to measure the semantic similarity between pairs of sentences. Help me to get the embeddings of the sentences.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Generate embeddings for audio files\', \'api_name\': \'audio_embedding_model\', \'api_call\': "hub.load(\'https://tfhub.dev/google/yamnet/1\')", \'api_arguments\': {\'audio_inputs\': \'Tensor of shape [batch_size, num_samples] containing audio signals\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = hub.load(\'https://tfhub.dev/google/yamnet/1\')\\n\\n# Load an audio file\\naudio_data = ... # Load audio data as a tensor\\n\\n# Generate embeddings\\nembeddings = model(audio_data)", \'performance\': {\'dataset\': \'YAMNet\', \'accuracy\': \'81.6% top-1 accuracy on AudioSet\'}, \'description\': \'This API allows you to generate embeddings for audio files using a pre-trained YAMNet model from TensorFlow Hub. The embeddings can be used for various tasks such as audio classification, similarity search, and more.\'}', metadata={})]


 I work in a post office, and need to classify bills and documents from a mixed package. Please extract image features so that we can use these features for classification.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Extract image features\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1\'\\nmodel = hub.load(model_id)\\nimage = tf.random.normal([1, 299, 299, 3])\\nfeatures = model(image)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Not specified\'}, \'description\': \'Load a pre-trained image feature vector model from TensorFlow Hub and use it to extract features from an image.\'}', metadata={})]


 We are building an AI app that can help us determine what kind of food is in a picture.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image pose detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detects human poses in images\', \'api_name\': \'movenet\', \'api_call\': "hub.load(\'https://tfhub.dev/google/movenet/singlepose/lightning/3\')", \'api_arguments\': \'image_tensor\', \'python_environment_requirements\': \'tensorflow, tensorflow_hub\', \'example_code\': "import tensorflow_hub as hub\\nmodel = hub.load(\'https://tfhub.dev/google/movenet/singlepose/lightning/3\')\\nimage_tensor = ... # Load image tensor\\noutputs = model(image_tensor)\\nkeypoints = outputs[\'output_0\'].numpy()", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'70.4 mAP\'}, \'description\': \'MoveNet is a family of efficient and accurate pose detection models designed for real-time applications. This model detects 17 key points of a single person in an image.\'}', metadata={})]


 Categorize images captured from a drone in different landscapes like forests, rivers, and mountains.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Extract high-level features from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'[None, 224, 224, 3]\', \'output_shape\': \'[None, 1280]\', \'trainable\': \'False\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   input_shape=(224, 224, 3), trainable=False),\\n    tf.keras.layers.Dense(1, activation=\'sigmoid\')\\n])\\n\\nmodel.compile(optimizer=\'adam\', loss=\'binary_crossentropy\', metrics=[\'accuracy\'])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image feature vector model using MobileNetV2 architecture on the ImageNet dataset. It extracts high-level features from images and can be used for tasks like image classification, object detection, and transfer learning.\'}', metadata={})]


 I am planning to sort and label my photo collection. Help me identify different objects in my master album.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'faster_rcnn_resnet50_v1_640x640\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/faster_rcnn/resnet50_v1_640x640/1\')", \'api_arguments\': {\'url\': \'https://tfhub.dev/tensorflow/faster_rcnn/resnet50_v1_640x640/1\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.6.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\ndetector = hub.load(\'https://tfhub.dev/tensorflow/faster_rcnn/resnet50_v1_640x640/1\')\\nimage_tensor = tf.image.decode_image(tf.io.read_file(\'path/to/image.jpg\'))\\nresult = detector(image_tensor[tf.newaxis, ...])", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'mAP@0.5IOU: 37.1%\'}, \'description\': \'A pre-trained Faster R-CNN model with ResNet-50 as the backbone for object detection in images. The model is trained on the COCO dataset and can detect objects from 80 different classes.\'}', metadata={})]


 Determine the dominant object in an image by extracting features from the image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'[224, 224, 3]\', \'output_shape\': \'[1280]\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', input_shape=[224, 224, 3], output_shape=[1280])\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image feature vector model using MobileNetV2 architecture on the ImageNet dataset. It can be used for various image recognition tasks by extracting meaningful features from input images.\'}', metadata={})]


 Estimate the similarity between two news headlines using pre-trained text embedding.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image Frame Interpolation\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Generate intermediate frames between two input frames\', \'api_name\': \'image-frame-interpolation\', \'api_call\': "hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\')", \'api_arguments\': {\'inputs\': \'A list of two input frames\'}, \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\ninputs = [frame1, frame2]\\ninterpolated_frame = hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\').call(inputs)", \'performance\': {\'dataset\': \'Adobe240fps\', \'accuracy\': \'PSNR: 29.8 dB, SSIM: 0.902\'}, \'description\': \'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\'}', metadata={})]