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| import numpy as np | |
| import gradio as gr | |
| import io | |
| from FcgEngine.engine import PredictorCls | |
| from FcgEngine.utils import plot_result, plot_spectra, plot_self_attention_map | |
| import os | |
| from huggingface_hub import login | |
| model_path = os.getenv("API_MODEL") | |
| auth_token = os.getenv("FCG_TOKEN") | |
| login(token=auth_token) | |
| engine = PredictorCls(model_path=model_path) | |
| def process(array_binary, th, option): | |
| try: | |
| spectra = np.load(io.BytesIO(array_binary[0])) | |
| outputs = engine(spectra) | |
| if option == 'Positive only': | |
| fcn_groups, probabilities = engine.get_result(outputs, th=th, pos_only=True) | |
| else: | |
| fcn_groups, probabilities = engine.get_result(outputs, th=th) | |
| prediction_fg = plot_result(fcn_groups, probabilities) | |
| spectra_fg = plot_spectra(spectra) | |
| att = engine.model.get_self_attention(layer_value=1) | |
| att = np.sum(att[0], axis=0) | |
| attention_fg = plot_self_attention_map(spectra, att) | |
| return spectra_fg, prediction_fg, attention_fg | |
| except: | |
| return None, None, None | |
| def clear(): | |
| return None, None, None | |
| examples_signal = [ | |
| ['examples/2373797.npy'], | |
| ['examples/2459054.npy'], | |
| ['examples/17451182.npy'], | |
| ['examples/19013106.npy'], | |
| ['examples/168833805.npy'], | |
| ] | |
| block = gr.Blocks(title="FcgFormer APP - Ohlabs") | |
| with block: | |
| with gr.Row(): | |
| gr.Markdown("## Transformer based for Spectra Characterization") | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| spectra_input = gr.File(file_count="multiple", | |
| label="Select File (*.npy)-signal with shape [1, n], n: signal length", | |
| type="binary", file_types=['.npy']) | |
| with gr.Row(): | |
| with gr.Accordion("Advanced options", open=False): | |
| threshold = gr.Slider(label="Control Confidence", minimum=0.05, maximum=0.95, value=0.5, step=0.05) | |
| draw_option = gr.Radio(["Positive only", "All classes"], value="Positive only", label="Draw Prediction option") | |
| with gr.Row(): | |
| with gr.Column(): | |
| run_button = gr.Button(value="Run", variant="primary") | |
| with gr.Column(): | |
| clear_button = gr.Button(value="Clear") | |
| with gr.Row(): | |
| download_example = gr.File(file_count="multiple", | |
| label="Please select an example file below and then click on the file size, " | |
| "which is typically highlighted in blue, to download the example signal " | |
| "for testing purposes.", | |
| type="binary", file_types=['.npy']) | |
| with gr.Row(): | |
| gr.Examples(examples=examples_signal, | |
| inputs=download_example, label="Download example file") | |
| with gr.Column(): | |
| with gr.Tab("Prediction"): | |
| predicted_plot = gr.Plot(label="Result") | |
| with gr.Tab("Attention map"): | |
| attention_plot = gr.Plot(label="Attention Map") | |
| with gr.Tab("Input Spectra"): | |
| signal_plot = gr.Plot(label="Spectra Signal") | |
| ips = [spectra_input, threshold, draw_option] | |
| ops = [signal_plot, predicted_plot, attention_plot] | |
| run_button.click(fn=process, inputs=ips, outputs=ops) | |
| clear_button.click(fn=clear, inputs=[], outputs=ops) | |
| block.launch(server_name='0.0.0.0', share=False) | |