app.py

import numpy as np
import gradio as gr
import tensorflow as tf
import matplotlib.pyplot as plt
from matplotlib import cm
from PIL import Image
import pandas as pd

def normalise_data(data, min_val, max_val, low=0, high=1):
    '''
    Normalises the data to the range [low, high]

    Parameters
    ----------
    data: numpy array, data to normalise
    low: float, minimum value of the range
    high: float, maximum value of the range

    Returns
    -------
    normalised_data: float, normalised data
    '''
    normalised_data = (data - min_val)/(max_val - min_val)
    normalised_data = (high - low)*normalised_data + low
    return normalised_data

data = np.load('./data.npy')
data_DTW = np.load('./data_DTW.npy')
model = tf.keras.models.load_model('./model.h5',compile = False)

def predict(Cell_number, Duty_Cycle, Cycle_number):
  # ------------------------ Prediction ------------------------
  # select cell data
  # data = x_test_1 #if Cell_number == '1' else x_test_2 if Cell_number == '2' else x_test_3
  # data_DTW = x_test_DTW_1 #if Cell_number == '1' else x_test_DTW_2 if Cell_number == '2' else x_test_DTW_3
  # select cycle number
  cycle = 0 if Cycle_number == '10' else 1 if Cycle_number == '50' else 2 if Cycle_number == '100' else 3 if Cycle_number == '200'else 4 if Cycle_number == '400' else 5

  IC_reference = data[0][0]
  sample = data[Duty_Cycle-1][cycle]
  sample_DTW = data_DTW[Duty_Cycle-1][cycle] #getDTWImage(IC_reference, sample, size)
  prediction = model.predict(np.expand_dims(sample_DTW, axis=0))
  pred = {"LLI ": str(prediction[0][0]), "LAMPE ": str(prediction[0][1]), "LAMNE ": str(prediction[0][2])}
  
  # --------------------------- IC + image----------------------------
  d = {' ': np.linspace(1, len(IC_reference), len(IC_reference)), 'pristine': IC_reference, 'degraded': sample}
  df = pd.DataFrame(data=d)

  image_array=sample_DTW.reshape(sample_DTW.shape[0], sample_DTW.shape[1])
  image_array = normalise_data(image_array, np.min(image_array), np.max(image_array))
  im = Image.fromarray(np.uint8(cm.inferno(image_array)*255))
  
  return pred, df, im
  
iface = gr.Interface(
	fn=predict,
	inputs=[gr.inputs.Radio(["Cell #1", "Cell #2", "Cell #3"]), gr.inputs.Slider(1, 1000, step=1), gr.inputs.Radio(["10", "50", "100", "200", "400", "1000"])],
	title="LFP degradation diagnosis",
	description="Enter cell number, duty cycle and cycle number to predict the percentage of LLI, LAMPE and LAMNE",
	outputs=[gr.outputs.Label(label="Prediction"), gr.outputs.Timeseries(x=" ", y=["pristine", "degraded"], label="IC curves"), gr.outputs.Image(type='pil', label="DTW image")],
	allow_screenshot=False,
  theme="darkpeach",
	layout="unaligned")
iface.launch()