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import tensorflow as tf
import streamlit as st
import pandas as pd
from transformers import pipeline
from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
st.title('Sentiment Analyser App')
st.write('Welcome to my sentiment analysis app!')
form = st.form(key='sentiment-form')
user_input = form.text_area('Enter your text')
submit = form.form_submit_button('Submit')
data = ["I love you", "I hate you","We are very hayy to show you that"]
model_name = st.sidebar.selectbox("Select Model",("distilbert-base-uncased-finetuned-sst-2-english", "finiteautomata/bertweet-base-sentiment-analysis"))
#model_name ="distilbert-base-uncased-finetuned-sst-2-english"
#model_name = "finiteautomata/bertweet-base-sentiment-analysis"
#####-------IN LUCRU---------------------------------------
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
clf = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
#token_ids = tokenizer(data, padding=True, return_tensors='tf')
#out = model(token_ids)
#Y_probas = tf.keras.activations.softmax(out.logits)
#Y_pred = tf.argmax(Y_probas, axis=1)
#print(Y_pred)
####-----------------------------------------------------------
def parse_input(ui):
SPLIT = ','
lst = list(ui.split(SPLIT))
yield lst
dfdict = {}
txtlst = []
labellst = []
scorelst = []
if submit:
model = pipeline(model=model_name)
#lst = list(user_input.split(","))
#for sentence in lst:
it = parse_input(user_input) #...NICER
for sentence in next(it):
#res = model(sentence)
res = clf(sentence) #...NICER
txtlst.append(sentence)
st.write(sentence)
label = res[0]['label']
labellst.append(label)
st.write(f'label is {label}')
score = res[0]['score']
scorelst.append(score)
st.write(f'score = {score}')
dfdict['TEXT'] = txtlst
dfdict['LABEL'] = labellst
dfdict['SCORE'] = scorelst
outdf = pd.DataFrame.from_dict(dfdict)
st.write(outdf)
#result = model(user_input)[0]
#res = model(user_input)
#st.write(result)
#label = result['label']
#score = result['score']
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