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import streamlit as st
import pandas as pd
import numpy as np
import torch
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
from PIL import Image
st.markdown("Link to the app - [milestone2-app](https://huggingface.co/spaces/aim9061/sentiment-analysis)")
st.title("Toxic Tweets Sentiment Analysis")
def create_dict(prob, text):
sorted_indices = np.argsort(prob)[-2:]
info = {"text": text,
"label1": toxic_tweet_cats[sorted_indices[1]],
"percentage1": str(round(prob[sorted_indices[1]], 3)),
"label2": toxic_tweet_cats[sorted_indices[0]],
"percentage2": str(round(prob[sorted_indices[0]], 3))}
return info
def get_cats(text):
tokenizer = AutoTokenizer.from_pretrained("aim9061/fine-tuned-toxic-tweet-dilbert")
token = tokenizer(text, return_tensors="pt")
model = AutoModelForSequenceClassification.from_pretrained("aim9061/fine-tuned-toxic-tweet-dilbert")
outputs = model(**token)
prob = torch.sigmoid(outputs.logits).detach().numpy()[0]
data = create_dict(prob, text)
res = pd.DataFrame([data])
st.table(res)
words = "Take that, you funking cat-dragon! You smell really bad!"
text = st.text_area("Insert text for analysis below.", words)
toxic_tweet_cats = ["Toxic", "Severe Toxic", "Obscene", "Threat", "Insult", "Identity Hate", "Not Toxic"]
model_list = ["aim9061/fine-tuned-toxic-tweet-dilbert", "distilbert-base-uncased-finetuned-sst-2-english", "bert-base-cased", "openai/clip-vit-base-patch32", "emilyalsentzer/Bio_ClinicalBERT",
"sentence-transformers/all-mpnet-base-v2", "facebook/bart-large-cnn", "openai/clip-vit-base-patch16", "speechbrain/spkrec-ecapa-voxceleb",
"albert-base-v2"]
model = st.selectbox("", model_list)
sub = st.write("Pick the model to use for analyzing the text!")
button = st.button("Analyze!")
pipe = pipeline("text-classification")
if(button):
if model == "aim9061/fine-tuned-toxic-tweet-dilbert":
get_cats(text)
pipe = pipeline("text-classification", model)
results = pipe(text)
st.write(results)
#TODO: DOCUMENT CODE
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