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| import gradio as gr | |
| # import argparse | |
| # import os | |
| # import re | |
| # import time | |
| # import torch | |
| # import pandas as pd | |
| # # import os, sys | |
| # # root_folder = os.path.abspath( | |
| # # os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) | |
| # # ) | |
| # # sys.path.append(root_folder) | |
| # from kernel_utils import VideoReader, FaceExtractor, confident_strategy, predict_on_video_set | |
| # from classifiers import DeepFakeClassifier | |
| # import gradio as gr | |
| # def predict(video): | |
| # # video_index = int(video_index) | |
| # frames_per_video = 32 | |
| # video_reader = VideoReader() | |
| # video_read_fn = lambda x: video_reader.read_frames(x, num_frames=frames_per_video) | |
| # face_extractor = FaceExtractor(video_read_fn) | |
| # input_size = 380 | |
| # strategy = confident_strategy | |
| # # test_videos = sorted([x for x in os.listdir(args.test_dir) if x[-4:] == ".mp4"])[video_index] | |
| # # print(f"Predicting {video_index} videos") | |
| # predictions = predict_on_video_set(face_extractor=face_extractor, input_size=input_size, models=models, | |
| # strategy=strategy, frames_per_video=frames_per_video, videos=video, | |
| # num_workers=6, test_dir=args.test_dir) | |
| # return predictions | |
| # def get_args_models(): | |
| # parser = argparse.ArgumentParser("Predict test videos") | |
| # arg = parser.add_argument | |
| # arg('--weights-dir', type=str, default="weights", help="path to directory with checkpoints") | |
| # arg('--models', type=str, default='classifier_DeepFakeClassifier_tf_efficientnet_b7_ns_1_best_dice', help="checkpoint files") # nargs='+', | |
| # arg('--test-dir', type=str, default='test_dataset', help="path to directory with videos") | |
| # arg('--output', type=str, required=False, help="path to output csv", default="submission.csv") | |
| # args = parser.parse_args() | |
| # models = [] | |
| # # model_paths = [os.path.join(args.weights_dir, model) for model in args.models] | |
| # model_paths = [os.path.join(args.weights_dir, args.models)] | |
| # for path in model_paths: | |
| # model = DeepFakeClassifier(encoder="tf_efficientnet_b7_ns").to("cpu") | |
| # print("loading state dict {}".format(path)) | |
| # checkpoint = torch.load(path, map_location="cpu") | |
| # state_dict = checkpoint.get("state_dict", checkpoint) | |
| # model.load_state_dict({re.sub("^module.", "", k): v for k, v in state_dict.items()}, strict=True) | |
| # model.eval() | |
| # del checkpoint | |
| # models.append(model.half()) | |
| # return args, models | |
| def greet(name): | |
| return "Hello " + name + "!!" | |
| if __name__ == '__main__': | |
| # global args, models | |
| # args, models = get_args_models() | |
| # stime = time.time() | |
| # print("Elapsed:", time.time() - stime) | |
| demo = gr.Interface(fn=greet, inputs="video", outputs="text") | |
| demo.launch() | |