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import streamlit as st
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
from transformers import AutoConfig
model_name = "farhan2206/dnabert2fourth"
# Load the tokenizer and model
# Load the configuration associated with the model
config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
# Load the model using the correct configuration
tokenizer = AutoTokenizer.from_pretrained(model_name, config=config, trust_remote_code=True)
model = AutoModelForSequenceClassification.from_pretrained(model_name, config=config, trust_remote_code=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
# Streamlit UI
def main():
st.title("Epigenetic Marks Prediction")
st.write("An application of DNA BERT2")
# Sidebar with information
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
user_input = st.text_area("Enter a DNA sequence:", height=150)
# Predict when the user provides input
if st.button("Classify Sequence"):
if user_input:
# Call the pred function for prediction
# predicted_class, confidence = pred(user_input)
predicted_class = pred(user_input)
# Display the result
st.subheader("Prediction Result")
if predicted_class == 1:
st.success("Epigenetic Mark detected!")
else:
st.info("No epigenetic mark found.")
# # Display progress bars with percentages
# 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.")
# Function for prediction
# def pred(sequence):
# encoded_input = tokenizer(sequence, return_tensors='pt')
# # Pass the encoded input through the model
# 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
def pred(sequence):
# Move the input tensors to the GPU
encoded_input = tokenizer(sequence, return_tensors='pt').to(device)
# Pass the encoded input through the model
with torch.no_grad():
outputs = model(input_ids=encoded_input['input_ids'], attention_mask=encoded_input['attention_mask']).to(device)
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()
# streamlit run app.py --server.port 9000
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