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import streamlit as st
import pandas as pd
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import random as r
import gradio as gr
import asyncio
gr.Interface.load("models/APJ23/MultiHeaded_Sentiment_Analysis_Model").launch()
tokenizer = AutoTokenizer.from_pretrained("APJ23/MultiHeaded_Sentiment_Analysis_Model", local_files_only=True)
model = AutoModelForSequenceClassification.from_pretrained("APJ23/MultiHeaded_Sentiment_Analysis_Model")
classes = {
0: 'Non-Toxic',
1: 'Toxic',
2: 'Severely Toxic',
3: 'Obscene',
4: 'Threat',
5: 'Insult',
6: 'Identity Hate'
}
@st.cache(allow_output_mutation=True)
def predict_toxicity(tweet,model,tokenizer):
inputs = tokenizer(tweet, return_tensors="pt", padding=True, truncation=True)
outputs = model(**inputs)
predicted_class = torch.argmax(outputs.logits, dim=1)
predicted_prob = torch.softmax(outputs.logits, dim=1)[0][predicted_class].item()
return classes[predicted_class], predicted_prob
def create_table(predictions):
data = {'Tweet': [], 'Highest Toxicity Class': [], 'Probability': []}
for tweet, prediction in predictions.items():
data['Tweet'].append(tweet)
data['Highest Toxicity Class'].append(prediction[0])
data['Probability'].append(prediction[1])
df = pd.DataFrame(data)
return df
st.title('Toxicity Prediction App')
tweet=st.text_input('Enter a tweet to check for toxicity')
async def predict_toxicity_async(tweet, model, tokenizer):
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
result = await loop.run_until_complete(predict_toxicity(tweet, model, tokenizer))
loop.close()
return result
def predict_toxicity_sync(tweet, model, tokenizer):
return asyncio.run(predict_toxicity_async(tweet, model, tokenizer))
if st.button('Predict'):
predicted_class_label, predicted_prob = predict_toxicity_sync(tweet, model, tokenizer)
prediction_text = f'Prediction: {predicted_class_label} ({predicted_prob:.2f})'
st.write(prediction_text)
predictions = {tweet: (predicted_class_label, predicted_prob)}
table = create_table(predictions)
st.table(table)
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