Update app.py

app.py

@@ -6,8 +6,6 @@ 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")
 
@@ -21,7 +19,7 @@ classes = {
     6: 'Identity Hate'
 }
 @st.cache(allow_output_mutation=True)
-def predict_toxicity(tweet,model,tokenizer):
+def prediction(tweet,model,tokenizer):
     inputs = tokenizer(tweet, return_tensors="pt", padding=True, truncation=True)
     outputs = model(**inputs)
     predicted_class = torch.argmax(outputs.logits, dim=1)
@@ -40,18 +38,15 @@ def create_table(predictions):
 st.title('Toxicity Prediction App')
 tweet=st.text_input('Enter a tweet to check for toxicity')
 
-async def predict_toxicity_async(tweet, model, tokenizer):
+async def run_async_function(tweet, model, tokenizer):
     loop = asyncio.new_event_loop()
     asyncio.set_event_loop(loop)
-    result = await loop.run_until_complete(predict_toxicity(tweet, model, tokenizer))
+    result = await loop.run_in_executor(None, prediction, 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)
+    predicted_class_label, predicted_prob = asyncio.run(run_async_function(tweet, model, tokenizer))
     prediction_text = f'Prediction: {predicted_class_label} ({predicted_prob:.2f})'
     st.write(prediction_text)
     predictions = {tweet: (predicted_class_label, predicted_prob)}