Delete app.py

app.py

@@ -1,167 +0,0 @@
-import tweepy as tw
-import streamlit as st
-import pandas as pd
-import torch
-import numpy as np
-import re
-
-from pysentimiento.preprocessing import preprocess_tweet
-
-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")
-
-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 = st.secrets["BjipwQslVG4vBdy4qK318KnoA"]
-consumer_secret = st.secrets["3fzL70v9faklrPgvTi3zbofw9rwk92fgGdtAslFkFYt8kGmqBJ"]
-access_token = st.secrets["AAAAAAAAAAAAAAAAAAAAANvrkgEAAAAAv91xx%2Bs%2FfuteNpH7K9nHPvpcjXg%3DHzScJpkiqRvJhHTwuVGJ4apg8Jlica4xtfyC5WhWWu33CX7Y9f"]
-access_token_secret = st.secrets["AAAAAAAAAAAAAAAAAAAAANvrkgEAAAAAv91xx%2Bs%2FfuteNpH7K9nHPvpcjXg%3DHzScJpkiqRvJhHTwuVGJ4apg8Jlica4xtfyC5WhWWu33CX7Y9f"]
-auth = tw.OAuthHandler(consumer_key, consumer_secret)
-auth.set_access_token(access_token, access_token_secret)
-api = tw.API(auth, wait_on_rate_limit=True)
-
-def preprocess(text):
-    text=text.lower()
-    # remove hyperlinks
-    text = re.sub(r'https?:\/\/.*[\r\n]*', '', text)
-    text = re.sub(r'http?:\/\/.*[\r\n]*', '', text)
-    #Replace &amp, &lt, &gt with &,<,> respectively
-    text=text.replace(r'&amp;?',r'and')
-    text=text.replace(r'&lt;',r'<')
-    text=text.replace(r'&gt;',r'>')
-    #remove hashtag sign
-    #text=re.sub(r"#","",text)   
-    #remove mentions
-    text = re.sub(r"(?:\@)\w+", '', text)
-    #text=re.sub(r"@","",text)
-    #remove non ascii chars
-    text=text.encode("ascii",errors="ignore").decode()
-    #remove some puncts (except . ! ?)
-    text=re.sub(r'[:"#$%&\*+,-/:;<=>@\\^_`{|}~]+','',text)
-    text=re.sub(r'[!]+','!',text)
-    text=re.sub(r'[?]+','?',text)
-    text=re.sub(r'[.]+','.',text)
-    text=re.sub(r"'","",text)
-    text=re.sub(r"\(","",text)
-    text=re.sub(r"\)","",text)
-    text=" ".join(text.split())
-    return text
-    
-def highlight_survived(s):
-    return ['background-color: red']*len(s) if (s.Sexista == 1) else ['background-color: green']*len(s)
-
-def color_survived(val):
-    color = 'red' if val=='Sexista' else 'white'
-    return f'background-color: {color}'                
-
-st.set_page_config(layout="wide")
-st.markdown('<style>body{background-color: Blue;}</style>',unsafe_allow_html=True)
-
-#background-color: Blue;
-
-colT1,colT2 = st.columns([2,8])
-with colT2:
-    #st.title('Analisis de comentarios sexistas en Twitter') 
-    st.markdown(""" <style> .font {
-    font-size:40px ; font-family: 'Cooper Black'; color: #FF9633;} 
-    </style> """, unsafe_allow_html=True)
-    st.markdown('<p class="font">Análisis de comentarios sexistas en Twitter</p>', unsafe_allow_html=True)
-    
-    st.markdown(""" <style> .font1 {
-    font-size:28px ; font-family: 'Times New Roman'; color: #8d33ff;} 
-    </style> """, unsafe_allow_html=True)
-
-    st.markdown(""" <style> .font2 {
-    font-size:16px ; font-family: 'Times New Roman'; color: #3358ff;} 
-    </style> """, unsafe_allow_html=True)
-
-def run():
-    with st.form(key='Introduzca Texto'):
-        col,buff1, buff2 = st.columns([2,2,1])
-        #col.text_input('smaller text window:')
-        search_words = col.text_input("Introduzca el termino o usuario para analizar y pulse el check correspondiente")
-        number_of_tweets = col.number_input('Introduzca número de tweets a analizar. Máximo 50', 0,50,10)
-        termino=st.checkbox('Término')
-        usuario=st.checkbox('Usuario')
-        submit_button = col.form_submit_button(label='Analizar')
-        error=False
-        if submit_button:
-            date_since = "2020-09-14"
-            if ( termino == False and usuario == False):
-                st.text('Error no se ha seleccionado ningun check')
-                error=True
-            elif ( termino == True and usuario == True):
-                st.text('Error se han seleccionado los dos check')
-                error=True
-            
-           
-            if (error == False):
-                if (termino):
-                    new_search = search_words + " -filter:retweets"
-                    tweets =tw.Cursor(api.search_tweets,q=new_search,lang="es",since=date_since).items(number_of_tweets)
-                elif (usuario):
-                    tweets = api.user_timeline(screen_name = search_words,count=number_of_tweets)
-                
-                tweet_list = [i.text for i in tweets]
-                #tweet_list = [strip_undesired_chars(i.text) for i in tweets]
-                text= pd.DataFrame(tweet_list)
-                #text[0] = text[0].apply(preprocess)
-                text[0] = text[0].apply(preprocess_tweet)
-                text1=text[0].values
-                indices1=tokenizer.batch_encode_plus(text1.tolist(),
-                                         max_length=128,
-                                         add_special_tokens=True, 
-                                         return_attention_mask=True,
-                                         pad_to_max_length=True,
-                                         truncation=True)
-                input_ids1=indices1["input_ids"]
-                attention_masks1=indices1["attention_mask"]
-                prediction_inputs1= torch.tensor(input_ids1)
-                prediction_masks1 = torch.tensor(attention_masks1)
-                # Set the batch size.  
-                batch_size = 25
-                # Create the DataLoader.
-                prediction_data1 = TensorDataset(prediction_inputs1, prediction_masks1)
-                prediction_sampler1 = SequentialSampler(prediction_data1)
-                prediction_dataloader1 = DataLoader(prediction_data1, sampler=prediction_sampler1, batch_size=batch_size)
-                print('Predicting labels for {:,} test sentences...'.format(len(prediction_inputs1)))
-                # Put model in evaluation mode
-                model.eval()
-                # Tracking variables 
-                predictions = []
-                # Predict 
-                for batch in prediction_dataloader1:
-                    batch = tuple(t.to(device) for t in batch)
-                    # Unpack the inputs from our dataloader
-                    b_input_ids1, b_input_mask1 = batch
-                    # Telling the model not to compute or store gradients, saving memory and   # speeding up prediction
-                    with torch.no_grad():
-                        # Forward pass, calculate logit predictions
-                        outputs1 = model(b_input_ids1, token_type_ids=None,attention_mask=b_input_mask1)
-                    logits1 = outputs1[0]
-                    # Move logits and labels to CPU
-                    logits1 = logits1.detach().cpu().numpy()
-                    # Store predictions and true labels
-                    predictions.append(logits1)
-                flat_predictions = [item for sublist in predictions for item in sublist]
-                flat_predictions = np.argmax(flat_predictions, axis=1).flatten()#p = [i for i in classifier(tweet_list)]
-                df = pd.DataFrame(list(zip(tweet_list, flat_predictions)),columns =['Últimos '+ str(number_of_tweets)+' Tweets'+' de '+search_words, 'Sexista'])
-                df['Sexista']= np.where(df['Sexista']== 0, 'No Sexista', 'Sexista')
-                
-                
-                st.table(df.reset_index(drop=True).head(20).style.applymap(color_survived, subset=['Sexista']))
-
-                
-                #st.dataframe(df.style.apply(highlight_survived, axis=1))
-                #st.table(df)
-            #st.write(df)
-run()