Joshua1808 commited on
Commit
f352c0a
·
1 Parent(s): 4518611

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +3 -2
app.py CHANGED
@@ -6,6 +6,7 @@ import numpy as np
6
  import regex as re
7
  import pysentimiento
8
  import geopy
 
9
 
10
  from pysentimiento.preprocessing import preprocess_tweet
11
  from geopy.geocoders import Nominatim
@@ -254,10 +255,10 @@ def tweets_localidad(buscar_localidad):
254
  probability = np.amax(logits1,axis=1).flatten()
255
  Tweets =['Últimos 50 Tweets'+' de '+ buscar_localidad]
256
  df = pd.DataFrame(list(zip(text1, localidad,username, flat_predictions,probability)), columns = ['Tweets' ,'Localidad' , 'Usuario','Prediccion','Probabilidad'])
257
-
258
  df['Prediccion']= np.where(df['Prediccion']== 0, 'No Sexista', 'Sexista')
259
  #df['Tweets'] = df['Tweets'].str.replace('RT|@', '')
260
- #df_filtrado = df[df["Sexista"] == 'Sexista']
261
  #df['Tweets'] = df['Tweets'].apply(lambda x: re.sub(r'[:;][-o^]?[)\]DpP3]|[(/\\]|[\U0001f600-\U0001f64f]|[\U0001f300-\U0001f5ff]|[\U0001f680-\U0001f6ff]|[\U0001f1e0-\U0001f1ff]','', x))
262
 
263
  tabla = st.table(df.reset_index(drop=True).head(50).style.applymap(color_survived, subset=['Prediccion']))
 
6
  import regex as re
7
  import pysentimiento
8
  import geopy
9
+ import matplotlib.pyplot as plt
10
 
11
  from pysentimiento.preprocessing import preprocess_tweet
12
  from geopy.geocoders import Nominatim
 
255
  probability = np.amax(logits1,axis=1).flatten()
256
  Tweets =['Últimos 50 Tweets'+' de '+ buscar_localidad]
257
  df = pd.DataFrame(list(zip(text1, localidad,username, flat_predictions,probability)), columns = ['Tweets' ,'Localidad' , 'Usuario','Prediccion','Probabilidad'])
258
+ df_filtrado = df[df["Prediccion"] == 1 ]
259
  df['Prediccion']= np.where(df['Prediccion']== 0, 'No Sexista', 'Sexista')
260
  #df['Tweets'] = df['Tweets'].str.replace('RT|@', '')
261
+
262
  #df['Tweets'] = df['Tweets'].apply(lambda x: re.sub(r'[:;][-o^]?[)\]DpP3]|[(/\\]|[\U0001f600-\U0001f64f]|[\U0001f300-\U0001f5ff]|[\U0001f680-\U0001f6ff]|[\U0001f1e0-\U0001f1ff]','', x))
263
 
264
  tabla = st.table(df.reset_index(drop=True).head(50).style.applymap(color_survived, subset=['Prediccion']))