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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) | |
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"] | |
d = {'tweet':[model_path],'classification':[label],'score':[score]} | |
data = pd.DataFrame(data=d) | |
st.table(data) | |
#st.write("The classification of the given text is " + label + " with a score of " + str(score)) | |
# 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)) | |