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429485e aa4bd4b 429485e e0a7610 429485e e0a7610 faca89f e0a7610 429485e | 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 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 | import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
from sentence_transformers import SentenceTransformer
from tensorflow.keras.models import model_from_json
import plotly.graph_objects as go
from PIL import Image
import streamlit as st
#####################################################################################################################################
st.set_page_config(layout='wide')
# Sidebar: logo Artefact + main info on text
with st.sidebar:
col1, col2, col3 = st.columns(3)
with col2:
logo_facebook = Image.open('static/logo_facebook.png')
st.image(logo_facebook)
# Checkboxes to see some info on our vocabularies
hateful = st.checkbox('Check to see top hateful words used')
if hateful:
# Loading some hateful text data
df_hate = pd.read_csv('static/data_hate.csv')
number_chosen_hate = st.number_input('How many top hateful words do you want to see?', value=5)
df_chosen_hate = df_hate.iloc[:number_chosen_hate, :]
st.write(f'{number_chosen_hate} most used words in the hateful vocabulary:')
st.dataframe(df_chosen_hate)
non_hateful = st.checkbox('Check to see top non-hateful words used')
if non_hateful:
# Loading some non-hateful text data
df_no_hate = pd.read_csv('static/data_no_hate.csv')
number_chosen = st.number_input('How many top non-hateful words do you want to see?', value=5)
df_chosen = df_no_hate.iloc[:number_chosen, :]
st.write(f'{number_chosen} most used words in the hateful vocabulary:')
st.dataframe(df_chosen)
#####################################################################################################################################
st.title('Facebook: Hateful Memes recognition')
st.write("---")
# Image selection
img_filepath = 'static/images_streamlit'
list_images = sorted([img for img in os.listdir(img_filepath)])
st.subheader('Some examples of hateful and non-hateful memes:')
with st.expander('Want to see some memes?'):
selected_image = st.select_slider('Select a meme to show it', options = [list_images[i] for i in range(10)], value=(list_images[0]))
col1, col2, col3 = st.columns(3)
with col2:
st.image(f'{img_filepath}/{selected_image}')
st.write("---")
#####################################################################################################################################
# Hateful test
st.subheader('Is a word in our hateful vocabulary or not?')
with st.expander('Hateful? Non-hateful?'):
word = st.text_input('Write a word to test it', 'like')
word_lower = word.lower()
# Need to reload them in case it was not done in the sidebar
df_hate = pd.read_csv('static/data_hate.csv')
df_no_hate = pd.read_csv('static/data_no_hate.csv')
try:
if word_lower not in df_hate['word'].values:
st.write(f'"{word}" is not in our hateful vocabulary.')
else:
appeared_hate = df_hate[df_hate['word'] == word_lower]['count'].values[0]
st.write(f'"{word}" is in our hateful vocabulary, it appears {appeared_hate} times.')
if word_lower not in df_no_hate['word'].values:
st.write(f'"{word}"is not in our non-hateful vocabulary.')
else:
appeared_no_hate = df_no_hate[df_no_hate['word'] == word_lower]['count'].values[0]
st.write(f'"{word}" is in our non-hateful vocabulary, it appears {appeared_no_hate} times.')
st.write(f'Ratio hateful vs non-hateful: {round(appeared_hate/appeared_no_hate, 2)}.')
except:
st.write(f'"{word}" is not in our hateful and non-hateful vocabulary.')
st.write("---")
#####################################################################################################################################
# Slider to choose how many words we want to see and plot the countplot
st.subheader('Barplot of top selected words:')
with st.expander('Select to choose how many top words you want to see and their count'):
option = st.selectbox('Which vocabulary to select?', ('Hateful vocabulary', 'Non-hateful vocabulary', 'Both vocabularies'))
st.write('You selected', option)
if option == 'Hateful vocabulary':
df_hate_subset = df_hate[df_hate.iloc[:, 1] >= 20]
start_word, end_word = st.select_slider(
'Select a range of top words',
options=[x for x in range(1, df_hate_subset.shape[0]+1)],
value=(1, 10))
df_slider_hate = df_hate_subset.iloc[start_word-1:end_word, :]
fig, ax = plt.subplots()
bars = plt.barh(y=df_slider_hate['word'], width=df_slider_hate['count'], color=['darkmagenta', 'darkblue', 'darkgreen', 'darkred', 'darkgrey', 'darkorange'])
ax.bar_label(bars)
ax = plt.gca().invert_yaxis()
st.subheader('Selected words hateful vocabulary:')
st.pyplot(fig)
elif option == 'Non-hateful vocabulary':
df_no_hate_subset = df_no_hate[df_no_hate.iloc[:, 1] >= 30]
start_word, end_word = st.select_slider(
'Select a range of top words',
options=[x for x in range(1, df_no_hate_subset.shape[0]+1)],
value=(1, 10))
df_slider_no_hate = df_no_hate_subset.iloc[start_word-1:end_word, :]
fig, ax = plt.subplots()
bars = plt.barh(y=df_slider_no_hate['word'], width=df_slider_no_hate['count'], color=['darkmagenta', 'darkblue', 'darkgreen', 'darkred', 'darkgrey', 'darkorange'])
ax.bar_label(bars)
ax = plt.gca().invert_yaxis()
st.subheader('Selected words non-hateful vocabulary:')
st.pyplot(fig)
else:
df_top = pd.read_csv('./static/data_top.csv')
start_word, end_word = st.select_slider(
'Select a range of top words',
options=[x for x in range(1, df_top.shape[0]+1)],
value=(1, 10))
df_slider = df_top.iloc[start_word-1:end_word, :]
fig, ax = plt.subplots()
bars = plt.barh(y=df_slider['word'], width=df_slider['count'], color=['darkmagenta', 'darkblue', 'darkgreen', 'darkred', 'darkgrey', 'darkorange'])
ax.bar_label(bars)
ax = plt.gca().invert_yaxis()
st.subheader('Selected words (hateful & non-hateful vocabularies):')
st.pyplot(fig)
st.write("---")
#####################################################################################################################################
# Testing some sentences
st.subheader('Testing some sentences if you dare:')
with st.expander('Input a sentence and check the probability of it being hateful:'):
# Some user input
model_nlp = SentenceTransformer('all-mpnet-base-v2')
sentence = st.text_input('Write a sentence to test it.', "Hopefully I don't write some hateful content.")
# Encoding
preprocessed_sentence = model_nlp.encode(sentence)
preprocessed_sentence = preprocessed_sentence.reshape(1, -1)
# load json and create model
json_file = open('static/model_nlp/model_nlp.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights("static/model_nlp/model_nlp.h5")
# Our predictions
y_pred = loaded_model.predict(preprocessed_sentence)
percentage = y_pred[0][0] * 100
# Scoreboard du prêt
fig1 = go.Figure(go.Indicator(
mode = "gauge+number+delta",
value = y_pred[0][0],
delta = {'reference': 0.50},
gauge = {'axis': {'range': [None, 1]},
'steps' : [
{'range': [0, 0.5], 'color': "lightgray"},
{'range': [0.5, 0.75], 'color': "gray"}],
'threshold' : {'line': {'color': "red", 'width': 4}, 'thickness': 0.9, 'value': 0.5}},
domain = {'x': [0, 1], 'y': [0, 1]},
title = {'text': 'Probability of your sentence being hateful'}))
st.plotly_chart(fig1)
if y_pred[0][0] < 0.5:
st.write(f"Congrats, it's not hateful!!!")
else:
st.write(f"Shame on you, it's hateful!!!")
st.write("---")
#####################################################################################################################################
col1, col2, col3, col4, col5 = st.columns(5)
with col5:
logo_artefact = Image.open('static/logo_artefact.png')
st.image(logo_artefact) |