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from transformers import AutoTokenizer, AutoModelForSequenceClassification
import gradio as gr
import torch
# Load the model and tokenizer from Hugging Face Hub
model_name = "julian-schelb/xlm-roberta-base-latin-intertextuality"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = model.to(device)
def predict_intertextuality(sentence1, sentence2):
"""
Predict intertextuality using the specified model.
"""
# Prepare input for the model
inputs = tokenizer(
sentence1,
sentence2,
return_tensors="pt",
truncation=True,
padding="max_length",
max_length=512 # Adjust based on model's configuration
).to(device)
# Perform inference
model.eval()
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.softmax(logits, dim=1).squeeze().cpu().numpy()
# Map probabilities to labels
return {"Yes": probs[1], "No": probs[0]}
# Define the Gradio interface
inputs = [
gr.Textbox(label="Latin Sentence 1"),
gr.Textbox(label="Latin Sentence 2")
]
outputs = gr.Label(label="Intertextuality Probabilities", num_top_classes=2)
gradio_app = gr.Interface(
fn=predict_intertextuality,
inputs=inputs,
outputs=outputs,
title="Latin Intertextuality Checker",
description="Enter two Latin sentences to get the probabilities for 'Yes' (intertextual) or 'No' (not intertextual).",
# flagging="never" # Disable the flag button
)
if __name__ == "__main__":
gradio_app.launch()
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