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import streamlit as st #Web App
from transformers import pipeline
import numpy as np
import pandas as pd
#title
st.title("Toxic Tweets")
model = st.selectbox("Which pretrained model would you like to use?",("roberta-large-mnli","twitter-XLM-roBERTa-base","bertweet-sentiment-analysis"))
d = {'col1':[1,2],'col2':[3,4]}
data = pd.DataFrame(data=d)
st.table(data)
# data = []
# text = st.text_input("Enter text here:","Artificial Intelligence is useful")
# data.append(text)
# if model == "roberta-large-mnli":
# #1
# if st.button("Run Sentiment Analysis of Text"):
# model_path = "roberta-large-mnli"
# sentiment_pipeline = pipeline(model=model_path)
# result = sentiment_pipeline(data)
# label = result[0]["label"]
# score = result[0]["score"]
# st.write("The classification of the given text is " + label + " with a score of " + str(score))
# elif model == "twitter-XLM-roBERTa-base":
# #2
# if st.button("Run Sentiment Analysis of Text"):
# model_path = "cardiffnlp/twitter-xlm-roberta-base-sentiment"
# sentiment_task = pipeline("sentiment-analysis", model=model_path, tokenizer=model_path)
# result = sentiment_task(text)
# label = result[0]["label"].capitalize()
# score = result[0]["score"]
# st.write("The classification of the given text is " + label + " with a score of " + str(score))
# elif model == "bertweet-sentiment-analysis":
# #3
# if st.button("Run Sentiment Analysis of Text"):
# analyzer = create_analyzer(task="sentiment", lang="en")
# result = analyzer.predict(text)
# if result.output == "POS":
# label = "POSITIVE"
# elif result.output == "NEU":
# label = "NEUTRAL"
# else:
# label = "NEGATIVE"
# neg = result.probas["NEG"]
# pos = result.probas["POS"]
# neu = result.probas["NEU"]
# st.write("The classification of the given text is " + label + " with the scores broken down as: Positive - " + str(pos) + ", Neutral - " + str(neu) + ", Negative - " + str(neg))
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