Update app.py

app.py

@@ -1,8 +1,6 @@
 import tweepy as tw
 import streamlit as st
 import pandas as pd
-import torch
-import numpy as np
 import regex as re
 import pysentimiento
 import geopy
@@ -13,23 +11,10 @@ from pysentimiento.preprocessing import preprocess_tweet
 from geopy.geocoders import Nominatim
 from transformers import pipeline
 
-from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
-from transformers import AutoTokenizer, AutoModelForSequenceClassification,AdamW
-tokenizer = AutoTokenizer.from_pretrained('hackathon-pln-es/twitter_sexismo-finetuned-robertuito-exist2021')
-model = AutoModelForSequenceClassification.from_pretrained("hackathon-pln-es/twitter_sexismo-finetuned-robertuito-exist2021")
 
 model_checkpoint = "hackathon-pln-es/twitter_sexismo-finetuned-robertuito-exist2021" 
 pipeline_nlp = pipeline("text-classification", model=model_checkpoint)
 
-import torch
-if torch.cuda.is_available():  
-    device = torch.device(	"cuda")
-    print('I will use the GPU:', torch.cuda.get_device_name(0))
-    
-else:
-    print('No GPU available, using the CPU instead.')
-    device = torch.device("cpu")
-
     
 consumer_key = "BjipwQslVG4vBdy4qK318KnoA"
 consumer_secret = "3fzL70v9faklrPgvTi3zbofw9rwk92fgGdtAslFkFYt8kGmqBJ"
@@ -141,7 +126,6 @@ def tweets_localidad(buscar_localidad):
         location = geolocator.geocode(buscar_localidad)
         radius = "10km"
         tweets = api.search_tweets(q="",lang="es",geocode=f"{location.latitude},{location.longitude},{radius}", count = 50, tweet_mode="extended")
-        
         tweet_list = [i.full_text for i in tweets]
         text= pd.DataFrame(tweet_list)
         text[0] = text[0].apply(preprocess_tweet)
@@ -157,6 +141,7 @@ def tweets_localidad(buscar_localidad):
                     result.append(etiqueta)
         df = pd.DataFrame(result)
         df['Prediccion'] = np.where( df['Prediccion'] == 'LABEL_1', 'Sexista', 'No Sexista')
+        #df['Tweets'] = df['Tweets'].str.replace('RT|@', '')
         #df['Probabilidad'] = df['Probabilidad'].apply(lambda x: '{:.2f}%'.format(x))
         #df.sort_values(by='Probabilidad', ascending=False, inplace=True)
         #df = df.sort_values(by=['Probabilidad', 'Prediccion'], ascending=[False, False])