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Update app.py
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app.py
CHANGED
@@ -6,8 +6,6 @@ import random as r
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import gradio as gr
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import asyncio
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gr.Interface.load("models/APJ23/MultiHeaded_Sentiment_Analysis_Model").launch()
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tokenizer = AutoTokenizer.from_pretrained("APJ23/MultiHeaded_Sentiment_Analysis_Model", local_files_only=True)
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model = AutoModelForSequenceClassification.from_pretrained("APJ23/MultiHeaded_Sentiment_Analysis_Model")
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@@ -21,7 +19,7 @@ classes = {
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6: 'Identity Hate'
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}
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@st.cache(allow_output_mutation=True)
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def
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inputs = tokenizer(tweet, return_tensors="pt", padding=True, truncation=True)
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outputs = model(**inputs)
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predicted_class = torch.argmax(outputs.logits, dim=1)
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@@ -40,18 +38,15 @@ def create_table(predictions):
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st.title('Toxicity Prediction App')
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tweet=st.text_input('Enter a tweet to check for toxicity')
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async def
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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result = await loop.
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loop.close()
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return result
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def predict_toxicity_sync(tweet, model, tokenizer):
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return asyncio.run(predict_toxicity_async(tweet, model, tokenizer))
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if st.button('Predict'):
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predicted_class_label, predicted_prob =
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prediction_text = f'Prediction: {predicted_class_label} ({predicted_prob:.2f})'
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st.write(prediction_text)
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predictions = {tweet: (predicted_class_label, predicted_prob)}
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import gradio as gr
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import asyncio
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tokenizer = AutoTokenizer.from_pretrained("APJ23/MultiHeaded_Sentiment_Analysis_Model", local_files_only=True)
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model = AutoModelForSequenceClassification.from_pretrained("APJ23/MultiHeaded_Sentiment_Analysis_Model")
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6: 'Identity Hate'
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}
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@st.cache(allow_output_mutation=True)
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def prediction(tweet,model,tokenizer):
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inputs = tokenizer(tweet, return_tensors="pt", padding=True, truncation=True)
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outputs = model(**inputs)
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predicted_class = torch.argmax(outputs.logits, dim=1)
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st.title('Toxicity Prediction App')
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tweet=st.text_input('Enter a tweet to check for toxicity')
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async def run_async_function(tweet, model, tokenizer):
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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result = await loop.run_in_executor(None, prediction, tweet, model, tokenizer)
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loop.close()
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return result
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if st.button('Predict'):
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predicted_class_label, predicted_prob = asyncio.run(run_async_function(tweet, model, tokenizer))
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prediction_text = f'Prediction: {predicted_class_label} ({predicted_prob:.2f})'
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st.write(prediction_text)
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predictions = {tweet: (predicted_class_label, predicted_prob)}
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