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import streamlit as st
from transformers import AutoTokenizer, AutoModelForSequenceClassification, BertConfig
import torch
model_name = "rashiqua/dnabert2_epigenetic"
config = BertConfig.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForSequenceClassification.from_pretrained(model_name, trust_remote_code=True, config=config)
def main():
st.title("Epigenetic Marks Prediction")
st.write("An application of DNA BERT2")
st.sidebar.header("About")
st.sidebar.write("This app uses DNA BERT2 to predict the presence of epigenetic marks in a given DNA sequence.")
user_input = st.text_area("Enter a DNA sequence:", height=150)
if st.button("Classify Sequence"):
if user_input:
predicted_class, confidence = pred(user_input)
st.subheader("Prediction Result")
if predicted_class == 1:
st.success("Epigenetic Mark detected!")
else:
st.info("No epigenetic mark found.")
st.subheader("Class Distribution")
st.write("1 - Epigenetic mark found")
st.progress(confidence)
st.text(f"{confidence * 100:.2f}%")
st.write("0 - Epigenetic mark not found")
st.progress(1 - confidence)
st.text(f"{(1 - confidence) * 100:.2f}%")
else:
st.warning("Please enter a DNA sequence for classification.")
def pred(sequence):
encoded_input = tokenizer(sequence, return_tensors='pt')
with torch.no_grad():
outputs = model(input_ids=encoded_input['input_ids'], attention_mask=encoded_input['attention_mask'])
logits = outputs[0]
predicted_class = logits.argmax(-1).item()
confidence = logits.softmax(dim=-1)[0, 1].item()
return predicted_class, confidence
if __name__ == "__main__":
main()
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