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b1582cb b05c932 4d36e92 b05c932 b1582cb b05c932 4d36e92 b05c932 4d36e92 b05c932 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | import altair as alt
import numpy as np
import pandas as pd
import streamlit as st
import streamlit.components.v1 as components
import shap
import pickle
import matplotlib.pyplot as plt
from huggingface_hub import HfFileSystem
fs = HfFileSystem()
#import datetime
arquivos_processados_bb=[]
arquivos_processados_bb.append('spaces/marcossuzuki/TCC_PoliUSPPro/transcrição audio RI/BB/valores_shap-bb1t24.save')
shap_values_bb = []
for path in arquivos_processados_bb:
with fs.open(path, 'rb') as inp:
shap_values_bb.append(pickle.load(inp))
inp.close()
text_num = st.number_input(
"When do you start?",
0, max_value = len(shap_values_bb[0])-1
)
option_map = {'POSITIVE':'Positivo', 'NEGATIVE':'Negativo', 'NEUTRAL':'Neutro'}
selection = st.segmented_control(
"Tool",
options=option_map.keys(),
default = 'POSITIVE',
format_func=lambda option: option_map[option],
selection_mode="single",
)
shap.initjs()
fig = shap.plots.text(shap_values_bb[0][text_num, :, selection], display = False)
components.html(fig, height=200, scrolling = True)
fig2, ax = plt.subplots()
shap.plots.waterfall(shap_values_bb[0][text_num, :, selection], show=False)
st.pyplot(fig2)
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