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| import numpy as np | |
| import pandas as pd | |
| import pickle | |
| import datasets | |
| from datasets import Dataset, DatasetDict | |
| from six import BytesIO | |
| import gradio as gr | |
| from huggingface_hub import snapshot_download | |
| import os | |
| from matplotlib import pyplot as plt | |
| import seaborn as sns | |
| import torch | |
| from neuralforecast import NeuralForecast | |
| from neuralforecast.models import NBEATS | |
| #PATH_TO_LABELS = 'label_map.pbtxt' | |
| #category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True) | |
| #def load_image_into_numpy_array(path): | |
| # image = None | |
| # image_data = tf.io.gfile.GFile(path, 'rb').read() | |
| # image = Image.open(BytesIO(image_data)) | |
| # return pil_image_as_numpy_array(image) | |
| def load_model(): | |
| download_dir = snapshot_download(REPO_ID) | |
| saved_model_dir = os.path.join(download_dir, "saved_model") | |
| futr_df = Dataset.from_pandas(futr_df) | |
| prediction_model = NeuralForecast.load(saved_model_dir) | |
| return prediction_model | |
| # samples_folder = 'test_samples | |
| # image_path = 'test_samples/sample_balloon.jpeg | |
| # | |
| #def predict(pilimg): | |
| # image_np = pil_image_as_numpy_array(pilimg) | |
| # return predict2(image_np) | |
| #def predict2(image_np): | |
| # results = detection_model(image_np) | |
| # different object detection models have additional results | |
| # result = {key:value.numpy() for key,value in results.items()} | |
| # label_id_offset = 0 | |
| # image_np_with_detections = image_np.copy() | |
| # viz_utils.visualize_boxes_and_labels_on_image_array( | |
| # image_np_with_detections[0], | |
| # result['detection_boxes'][0], | |
| # (result['detection_classes'][0] + label_id_offset).astype(int), | |
| # result['detection_scores'][0], | |
| # category_index, | |
| # use_normalized_coordinates=True, | |
| # max_boxes_to_draw=200, | |
| # min_score_thresh=.60, | |
| # agnostic_mode=False, | |
| # line_thickness=2) | |
| # result_pil_img = tf.keras.utils.array_to_img(image_np_with_detections[0]) | |
| # return result_pil_img | |
| REPO_ID = "magcheong/ITI110_Energy_Prediction" | |
| prediction_model = load_model() | |
| title = "testing" | |
| description = "This is an app to detect burger and fries." | |
| css_code = ".gradio-container {background: rgb(250, 250, 210)}" | |
| # css_code='body{background-size: 25% 25%; background-repeat: no-repeat; background-image:url("https://static.vecteezy.com/system/resources/previews/022/787/250/original/illustration-of-french-fries-transparent-background-generative-ai-png.png");}' | |
| gr.Interface(fn=predict, | |
| title = title, | |
| description = description, | |
| css=css_code, | |
| #inputs=gr.Image(type="pil", height=309), | |
| #outputs=gr.Image(type="pil", height=350) | |
| ).launch(share=True) |