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import streamlit as st
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForSequenceClassification
st.title("Milestone #2 offensive statement prediction with pre-trained models")
st.write("in this basic demo you can select a model to judge whether or not the text below is offensive")
text = "The mail man looks so dumb"
st.write(text)
options = ["zero-shot-classification", "cardiffnlp/twitter-roberta-base-offensive", "Greys/milestonemodel"]
model = st.selectbox("Select a pre-trained model", options)
con = st.button("Submit")
if con:
if model == "zero-shot-classification":
classifier = pipeline(model)
res = classifier(text, candidate_labels=["offensive"])
label = res['labels'][0]
score = res['scores'][0]
st.write(f"Prediction: {label}, Score: {score*100}% chance")
if model == "cardiffnlp/twitter-roberta-base-offensive":
classifier = pipeline('text-classification', model='cardiffnlp/twitter-roberta-base-offensive', tokenizer='cardiffnlp/twitter-roberta-base-offensive')
result = classifier(text)
label = result[0]['label']
score = result[0]['score']
st.write(f"Prediction: {label}, Score: {score*100}% chance")
if model == "Greys/milestonemodel":
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
model = AutoModelForSequenceClassification.from_pretrained("Greys/milestonemodel")
my_list = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
def classify_sentence(text):
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
probs = outputs.logits.softmax(dim=1)
return probs.detach().numpy()[0]
probs = classify_sentence(text)
def find_largest_number(numbers):
if not numbers:
print("List is empty.")
return None
max_num = numbers[0]
max_index = 0
for i in range(1, len(numbers)):
if numbers[i] > max_num:
max_num = numbers[i]
max_index = i
return max_index
print(probs)
index = find_largest_number(probs)
st.write(my_list[index])
#id,toxic,severe_toxic,obscene,threat,insult,identity_hate
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