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import torch
import gradio as gr
import json
import os
from phate import PHATEAE
from funcs.som import ClusterSOM
from funcs.tools import numpy_to_native
from funcs.processor import process_data
from funcs.plot_func import plot_sensor_data_from_json
from funcs.dataloader import BaseDataset2, read_json_files
DEVICE = torch.device("cpu")
reducer10d = PHATEAE(epochs=30, n_components=10, lr=.0001, batch_size=128, t='auto', knn=8, relax=True, metric='euclidean')
reducer10d.load('models/r10d_2.pth')
cluster_som = ClusterSOM()
cluster_som.load("models/cluster_som2.pkl")
# ml inference
def get_som_mp4(file, slice_select=1, reducer=reducer10d, cluster=cluster_som):
try:
train_x, train_y = read_json_files(file)
except:
train_x, train_y = read_json_files(file.name)
# Convert tensors to numpy arrays if necessary
if isinstance(train_x, torch.Tensor):
train_x = train_x.numpy()
if isinstance(train_y, torch.Tensor):
train_y = train_y.numpy()
# load the time series slices of the data 4*3*2*64 (feeds+axis*sensor*samples) + 5 for time diff
data = BaseDataset2(train_x.reshape(len(train_x), -1) / 32768, train_y)
#compute the 10 dimensional embeding vector
embedding10d = reducer.transform(data)
# prediction = cluster_som.predict(embedding10d)
fig = cluster.plot_activation_v2(embedding10d, slice_select)
return fig
def attach_label_to_json(json_file, label_text):
# Read the JSON file
try:
with open(json_file, "r") as f:
slices = json.load(f)
except:
with open(json_file.name, "r") as f:
slices = json.load(f)
slices['label'] = label_text
with open(f'manual_labelled_{os.path.basename(json_file.name)}', "w") as f:
json.dump(numpy_to_native(slices), f, indent=2)
return f'manual_labelled_{os.path.basename(json_file.name)}'
with gr.Blocks(title='Cabasus') as cabasus_sensor:
title = gr.Markdown("<h2><center>Data gathering and processing</center></h2>")
with gr.Tab("Convert"):
with gr.Row():
csv_file_box = gr.File(label='Upload CSV File')
with gr.Column():
processed_file_box = gr.File(label='Processed CSV File')
json_file_box = gr.File(label='Generated Json file')
with gr.Row():
slice_size_slider = gr.Slider(minimum=16, maximum=512, step=1, value=64, label="Slice Size", visible=False)
sample_rate = gr.Slider(minimum=1, maximum=199, step=1, value=20, label="Sample rate", visible=False)
with gr.Row():
window_size_slider = gr.Slider(minimum=0, maximum=100, step=2, value=10, label="Window Size", visible=False)
repeat_process = gr.Button('Restart process', visible=False)
with gr.Row():
leg_dropdown = gr.Dropdown(choices=['GZ1', 'GZ2', 'GZ3', 'GZ4'], label='select leg', value='GZ1')
slice_slider = gr.Slider(minimum=1, maximum=300, label='Current slice', step=1)
with gr.Row():
plot_box_leg = gr.Plot(label="Filtered Signal Plot")
plot_box_overlay = gr.Plot(label="Overlay Signal Plot")
with gr.Row():
plot_slice_leg = gr.Plot(label="Sliced Signal Plot")
get_all_slice = gr.Plot(label="Real Signal Plot")
som_create = gr.Button('generate som')
som_figures = gr.Plot(label="som activations")
with gr.Row():
slice_json_box = gr.File(label='Slice json file')
with gr.Column():
label_name = gr.Textbox(label="enter the label name")
button_label_Add = gr.Button('attach label')
slice_json_label_box = gr.File(label='Slice json labelled file')
slices_per_leg = gr.Textbox(label="Number of slices found per LEG")
csv_file_box.change(process_data, inputs=[csv_file_box, slice_size_slider, sample_rate, window_size_slider],
outputs=[processed_file_box, json_file_box, slices_per_leg, plot_box_leg, plot_box_overlay, slice_slider, plot_slice_leg, get_all_slice, slice_json_box])
leg_dropdown.change(plot_sensor_data_from_json, inputs=[json_file_box, leg_dropdown, slice_slider],
outputs=[plot_box_leg, plot_slice_leg, get_all_slice, slice_json_box])
repeat_process.click(process_data, inputs=[csv_file_box, slice_size_slider, sample_rate, window_size_slider],
outputs=[processed_file_box, json_file_box, slices_per_leg, plot_box_leg, plot_box_overlay, slice_slider, plot_slice_leg, get_all_slice, slice_json_box])
slice_slider.change(plot_sensor_data_from_json, inputs=[json_file_box, leg_dropdown, slice_slider],
outputs=[plot_box_leg, plot_slice_leg, get_all_slice, slice_json_box])
som_create.click(get_som_mp4, inputs=[json_file_box, slice_slider], outputs=[som_figures])
button_label_Add.click(attach_label_to_json, inputs=[slice_json_box, label_name], outputs=[slice_json_label_box])
cabasus_sensor.queue(concurrency_count=2).launch(debug=True)
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