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import streamlit as st
from transformers import RobertaTokenizer, RobertaModel
from your_model_file import MDFEND # Ensure you import your model correctly
# Load model and tokenizer
@st.cache(allow_output_mutation=True)
def load_model():
tokenizer = RobertaTokenizer.from_pretrained("prediction_sinhala.ipynb")
model = MDFEND.from_pretrained("prediction_sinhala.ipynb")
return model, tokenizer
model, tokenizer = load_model()
# User input
text_input = st.text_area("Enter text here:")
# Prediction
if st.button("Predict"):
inputs = tokenizer(text_input, return_tensors="pt")
with torch.no_grad(): # Ensure no gradients are computed
outputs = model(**inputs)
prediction = outputs.logits.argmax(-1).item()
st.write(f"Prediction: {prediction}")
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