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import streamlit as st
import torch
from transformers import AutoModelForSequenceClassification, pipeline, AutoTokenizer, DistilBertForSequenceClassification, DistilBertTokenizerFast
import pandas as pd
import comments
from random import randint
def predict_cyberbullying_probability(sentence, tokenizer, model):
# Preprocess the input sentence
inputs = tokenizer(sentence, padding='max_length', return_token_type_ids=False, return_attention_mask=True, truncation=True, max_length=512, return_tensors='pt')
attention_mask = inputs['attention_mask'].flatten()
inputs = inputs['input_ids'].flatten()
# print("\n\ninputs\n\n", inputs)
# Disable gradient computation
with torch.no_grad():
# Forward pass
outputs = model(inputs, attention_mask=attention_mask)
probs = torch.sigmoid(outputs.logits.flatten())
res = probs.numpy().tolist()
return res
@st.cache
def perform_cyberbullying_analysis(tweet):
with st.spinner(text="loading model..."):
model = AutoModelForSequenceClassification.from_pretrained('kingsotn/finetuned_cyberbullying')
tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')
df = pd.DataFrame({'comment': [tweet]})
list_probs = predict_cyberbullying_probability(tweet, tokenizer, model)
for i, label in enumerate(labels[1:]):
df[label] = list_probs[i]
return df
def perform_default_analysis(model_name):
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
clf = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer, framework="pt")
tweet = st.text_area(label="Enter Text:",value="I'm nice at ping pong")
submitted = st.form_submit_button("Analyze")
if submitted:
#loading bar
with st.spinner(text="loading..."):
out = clf(tweet)
st.json(out)
if out[0]["label"] == "POSITIVE" or out[0]["label"] == "POS":
st.balloons()
# prompt = f"{basic_prompt} + \n\nThe user wrote a tweet that says: {tweet}, compliment them on how nice of a person they are! Remember try to be as cringe and awkard as possible!"
# response = generator(prompt, max_length=1000)[0]
st.success("nice tweet!")
else:
# prompt = f"{basic_prompt} + \n\nThe user wrote a tweet that says: {tweet}, tell them on how terrible of a person they are! Remember try to be as cringe and awkard as possible!"
# response = generator(prompt, max_length=1000)[0]
st.error("bad tweet!")
# main -->
st.title("Toxic Tweets Analyzer")
image = "kanye_tweet.jpg"
st.image(image, use_column_width=True)
labels = ['comment', 'toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
# toxic_list = st.cache(comments.comments)
with st.form("my_form"):
#select model
model_name = st.selectbox("Enter a text and select a pre-trained model to get the sentiment analysis", ["kingsotn/finetuned_cyberbullying", "distilbert-base-uncased-finetuned-sst-2-english", "finiteautomata/bertweet-base-sentiment-analysis", "distilbert-base-uncased"])
if model_name == "kingsotn/finetuned_cyberbullying":
if model_name == "kingsotn/finetuned_cyberbullying":
default = "I'm nice at ping pong"
tweet = st.text_area(label="Enter Text:",value=default)
submitted = st.form_submit_button("Analyze textbox")
random = st.form_submit_button("Analyze a random 😈😈😈 tweet")
if random:
tweet = comments.comments[randint(0, 354)]
df = perform_cyberbullying_analysis(tweet)
# Display the cached table
st.table(df)
else:
perform_default_analysis(model_name) |