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import streamlit as st
import time
from transformers import pipeline
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import os
import torch
import numpy as np
import pandas as pd
os.environ['KMP_DUPLICATE_LIB_OK'] = "True"
st.title("Sentiment Analysis App")
if 'logs' not in st.session_state:
st.session_state.logs = dict()
if 'labels' not in st.session_state:
st.session_state.labels = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
if 'id2label' not in st.session_state:
st.session_state.id2label = {idx: label for idx, label in enumerate(st.session_state.labels)}
if 'filled' not in st.session_state:
st.session_state.filled = False
form = st.form(key='Sentiment Analysis')
st.session_state.options = ['bertweet-base-sentiment-analysis',
'distilbert-base-uncased-finetuned-sst-2-english',
'twitter-roberta-base-sentiment',
'Modified Bert Toxicity Classification'
]
box = form.selectbox('Select Pre-trained Model:', st.session_state.options, key=1)
tweet = form.text_input(label='Enter text to analyze:', value="\"We've seen in the last few months, unprecedented amounts of Voter Fraud.\" @SenTedCruz True!")
submit = form.form_submit_button(label='Submit')
if 'df' not in st.session_state:
st.session_state.df = pd.read_csv("test.csv")
if not st.session_state.filled:
for s in st.session_state.options:
st.session_state.logs[s] = []
if not st.session_state.filled:
st.session_state.filled = True
for x in range(10):
print(x)
text = st.session_state.df["comment_text"].iloc[x][:128]
for s in st.session_state.options:
pline = None
if s == 'bertweet-base-sentiment-analysis':
pline = pipeline(task="sentiment-analysis", model="finiteautomata/bertweet-base-sentiment-analysis")
elif s == 'twitter-roberta-base-sentiment':
pline = pipeline(task="sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment")
elif s == 'distilbert-base-uncased-finetuned-sst-2-english':
pline = pipeline(task="sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
else:
model = AutoModelForSequenceClassification.from_pretrained('Ptato/Modified-Bert-Toxicity-Classification')
model.eval()
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
encoding = tokenizer(tweet, return_tensors="pt")
encoding = {k: v.to(model.device) for k,v in encoding.items()}
predictions = model(**encoding)
logits = predictions.logits
sigmoid = torch.nn.Sigmoid()
probs = sigmoid(logits.squeeze().cpu())
predictions = np.zeros(probs.shape)
predictions[np.where(probs >= 0.5)] = 1
predicted_labels = [st.session_state.id2label[idx] for idx, label in enumerate(predictions) if label == 1.0]
log = []
if pline:
predictions = pline(text)
log = [0] * 4
log[1] = text
for p in predictions:
if s == 'bertweet-base-sentiment-analysis':
if p['label'] == "POS":
log[0] = 0
log[2] = "POSITIVE"
log[3] = f"{ round(p['score'] * 100, 1)}%"
elif p['label'] == "NEU":
log[0] = 2
log[2] = f"{ p['label'] }"
log[3] = f"{round(p['score'] * 100, 1)}%"
else:
log[2] = "NEG"
log[0] = 1
log[3] = f"{round(p['score'] * 100, 1)}%"
elif s == 'distilbert-base-uncased-finetuned-sst-2-english':
if p['label'] == "POSITIVE":
log[0] = 0
log[2] = "POSITIVE"
log[3] = (f"{round(p['score'] * 100, 1)}%")
else:
log[2] = ("NEGATIVE")
log[0] = 1
log[3] = (f"{round(p['score'] * 100, 1)}%")
elif s == 'twitter-roberta-base-sentiment':
if p['label'] == "LABEL_2":
log[0] = 0
log[2] = ("POSITIVE")
log[3] = (f"{round(p['score'] * 100, 1)}%")
elif p['label'] == "LABEL_0":
log[0] = 1
log[2] = ("NEGATIVE")
log[3] = f"{round(p['score'] * 100, 1)}%"
else:
log[0] = 2
log[2] = "NEUTRAL"
log[3] = f"{round(p['score'] * 100, 1)}%"
else:
log = [0] * 6
log[1] = text
if max(predictions) == 0:
log[0] = 0
log[2] = ("NO TOXICITY")
log[3] = (f"{100 - round(probs[0].item() * 100, 1)}%")
log[4] = ("N/A")
log[5] = ("N/A")
else:
log[0] = 1
_max = 0
_max2 = 2
for i in range(1, len(predictions)):
if probs[i].item() > probs[_max].item():
_max = i
if i > 2 and probs[i].item() > probs[_max2].item():
_max2 = i
log[2] = (st.session_state.labels[_max])
log[3] = (f"{round(probs[_max].item() * 100, 1)}%")
log[4] = (st.session_state.labels[_max2])
log[5] = (f"{round(probs[_max2].item() * 100, 1)}%")
st.session_state.logs[s].append(log)
if submit and tweet:
with st.spinner('Analyzing...'):
time.sleep(1)
if tweet is not None:
pline = None
if box != 'Modified Bert Toxicity Classification':
col1, col2, col3 = st.columns(3)
else:
col1, col2, col3, col4, col5 = st.columns(5)
if box == 'bertweet-base-sentiment-analysis':
pline = pipeline(task="sentiment-analysis", model="finiteautomata/bertweet-base-sentiment-analysis")
elif box == 'twitter-roberta-base-sentiment':
pline = pipeline(task="sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment")
elif box == 'distilbert-base-uncased-finetuned-sst-2-english':
pline = pipeline(task="sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
else:
model = AutoModelForSequenceClassification.from_pretrained('Ptato/Modified-Bert-Toxicity-Classification')
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
encoding = tokenizer(tweet, return_tensors="pt")
encoding = {k: v.to(model.device) for k,v in encoding.items()}
predictions = model(**encoding)
logits = predictions.logits
sigmoid = torch.nn.Sigmoid()
probs = sigmoid(logits.squeeze().cpu())
print(probs[0].item())
predictions = np.zeros(probs.shape)
predictions[np.where(probs >= 0.5)] = 1
predicted_labels = [st.session_state.id2label[idx] for idx, label in enumerate(predictions) if label == 1.0]
if pline:
predictions = pline(tweet)
col2.header("Judgement")
else:
col2.header("Category")
col4.header("Type")
col5.header("Score")
col1.header("Tweet")
col3.header("Score")
if pline:
log = [0] * 4
log[1] = tweet
for p in predictions:
if box == 'bertweet-base-sentiment-analysis':
if p['label'] == "POS":
col1.success(tweet.split("\n")[0][:20])
log[0] = 0
col2.success("POS")
col3.success(f"{ round(p['score'] * 100, 1)}%")
log[2] = ("POS")
log[3] = (f"{ round(p['score'] * 100, 1)}%")
elif p['label'] == "NEU":
col1.warning(tweet.split("\n")[0][:20])
log[0] = 2
col2.warning(f"{ p['label'] }")
col3.warning(f"{round(p['score'] * 100, 1)}%")
log[2] = ("NEU")
log[3] = (f"{round(p['score'] * 100, 1)}%")
else:
log[0] = 1
col1.error(tweet.split("\n")[0][:20])
col2.error("NEG")
col3.error(f"{round(p['score'] * 100, 1)}%")
log[2] = ("NEG")
log[3] = (f"{round(p['score'] * 100, 1)}%")
elif box == 'distilbert-base-uncased-finetuned-sst-2-english':
if p['label'] == "POSITIVE":
col1.success(tweet.split("\n")[0][:20])
log[0] = 0
col2.success("POSITIVE")
log[2] = "POSITIVE"
col3.success(f"{round(p['score'] * 100, 1)}%")
log[3] = f"{round(p['score'] * 100, 1)}%"
else:
col2.error("NEGATIVE")
col1.error(tweet.split("\n")[0][:20])
log[2] = ("NEGATIVE")
log[0] = 1
col3.error(f"{round(p['score'] * 100, 1)}%")
log[3] = f"{round(p['score'] * 100, 1)}%"
elif box == 'twitter-roberta-base-sentiment':
if p['label'] == "LABEL_2":
log[0] = 0
col1.success(tweet.split("\n")[0][:20])
col2.success("POSITIVE")
col3.success(f"{round(p['score'] * 100, 1)}%")
log[3] = f"{round(p['score'] * 100, 1)}%"
log[2] = "POSITIVE"
elif p['label'] == "LABEL_0":
log[0] = 1
col1.error(tweet.split("\n")[0][:20])
col2.error("NEGATIVE")
col3.error(f"{round(p['score'] * 100, 1)}%")
log[3] = f"{round(p['score'] * 100, 1)}%"
log[2] = "NEGATIVE"
else:
log[0] = 2
col1.warning(tweet.split("\n")[0][:20])
col2.warning("NEUTRAL")
col3.warning(f"{round(p['score'] * 100, 1)}%")
log[3] = f"{round(p['score'] * 100, 1)}%"
log[2] = "NEUTRAL"
for a in st.session_state.logs[box][::-1]:
if a[0] == 0:
col1.success(a[1].split("\n")[0][:20])
col2.success(a[2])
col3.success(a[3])
elif a[0] == 1:
col1.error(a[1].split("\n")[0][:20])
col2.error(a[2])
col3.error(a[3])
else:
col1.warning(a[1].split("\n")[0][:20])
col2.warning(a[2])
col3.warning(a[3])
st.session_state.logs[box].append(log)
else:
log = [0] * 6
log[1] = tweet
if max(predictions) == 0:
col1.success(tweet.split("\n")[0][:10])
col2.success("NO TOXICITY")
col3.success(f"{100 - round(probs[0].item() * 100, 1)}%")
col4.success("N/A")
col5.success("N/A")
log[0] = 0
log[2] = "NO TOXICITY"
log[3] = (f"{100 - round(probs[0].item() * 100, 1)}%")
log[4] = ("N/A")
log[5] = ("N/A")
else:
_max = 0
_max2 = 2
for i in range(1, len(predictions)):
if probs[i].item() > probs[_max].item():
_max = i
if i > 2 and probs[i].item() > probs[_max2].item():
_max2 = i
col1.error(tweet.split("\n")[0][:10])
col2.error(st.session_state.labels[_max])
col3.error(f"{round(probs[_max].item() * 100, 1)}%")
col4.error(st.session_state.labels[_max2])
col5.error(f"{round(probs[_max2].item() * 100, 1)}%")
log[0] = 1
log[2] = (st.session_state.labels[_max])
log[3] = (f"{round(probs[_max].item() * 100, 1)}%")
log[4] = (st.session_state.labels[_max2])
log[5] = (f"{round(probs[_max2].item() * 100, 1)}%")
for a in st.session_state.logs[box][::-1]:
if a[0] == 0:
col1.success(a[1].split("\n")[0][:10])
col2.success(a[2])
col3.success(a[3])
col4.success(a[4])
col5.success(a[5])
elif a[0] == 1:
col1.error(a[1].split("\n")[0][:10])
col2.error(a[2])
col3.error(a[3])
col4.error(a[4])
col5.error(a[5])
else:
col1.warning(a[1].split("\n")[0][:10])
col2.warning(a[2])
col3.warning(a[3])
col4.warning(a[4])
col5.warning(a[5])
st.session_state.logs[box].append(log) |