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import gradio as gr
import transformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# Load the Hugging Face model and tokenizer
model_name = 'AIRI-Institute/gena-lm-bert-base-lastln-t2t' # Replace with the actual model name
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
# Define a function to process the DNA sequence
def analyze_dna(sequence):
# Preprocess the input sequence
inputs = tokenizer(sequence, return_tensors='pt')
# Get model predictions
outputs = model(**inputs)
# Convert logits to probabilities
probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1).squeeze().tolist()
# Get the top 5 most likely classes
top_5_indices = sorted(range(len(probabilities)), key=lambda i: probabilities[i], reverse=True)[:5]
top_5_probs = [probabilities[i] for i in top_5_indices]
# Prepare the output as a list of tuples (class_index, probability)
result = [(index, prob) for index, prob in zip(top_5_indices, top_5_probs)]
return result
# Create a Gradio interface
demo = gr.Interface(fn=analyze_dna, inputs="text", outputs="json")
# Launch the interface
demo.launch()
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