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| import os | |
| import gradio as gr | |
| import uuid | |
| import shutil | |
| from openvideo import push_file_to_hf | |
| try: | |
| import ml4co_kit | |
| except: | |
| os.system("pip install ml4co-kit-0.0.2a1.tar.gz") | |
| import ml4co_kit | |
| from ml4co_kit import CVRPPyVRPSolver | |
| from ml4co_kit import CVRPDataGenerator | |
| FILEPATH = "data/cvrp/uniform/cvrp100_uniform.txt" | |
| def handle( | |
| hf_token: str | |
| ): | |
| cur_iter = 0 | |
| max_iter = 1000 | |
| while(cur_iter < max_iter): | |
| solver = CVRPPyVRPSolver(time_limit=60) | |
| gen = CVRPDataGenerator( | |
| num_threads=8, | |
| nodes_num=100, | |
| min_capacity=50, | |
| max_capacity=50, | |
| solver=solver, | |
| train_samples_num=160, | |
| val_samples_num=0, | |
| test_samples_num=0 | |
| ) | |
| gen.generate() | |
| filename = uuid.uuid4().hex[:9] + ".txt" | |
| push_file_to_hf( | |
| hf_token=hf_token, | |
| hf_repo_id="ML4CO/ML4VRP", | |
| file_path=FILEPATH, | |
| path_in_repo=filename | |
| ) | |
| shutil.rmtree("data/cvrp") | |
| cur_iter = cur_iter + 1 | |
| with gr.Blocks() as demo: | |
| gr.Markdown( | |
| ''' | |
| VRP Data Generating | |
| ''' | |
| ) | |
| hf_token = gr.Textbox(label="HuggingFace Token") | |
| with gr.Row(): | |
| button = gr.Button("Submit", variant="primary") | |
| clear = gr.Button("Clear") | |
| button.click( | |
| handle, | |
| [hf_token], | |
| outputs=None | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch(debug = True) |