app.py

import gradio as gr
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

# Load model and tokenizer
model_name = "cross-encoder/ms-marco-MiniLM-L-12-v2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Function to compute relevance score
def get_relevance_score(query, paragraph):
    inputs = tokenizer(query, paragraph, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        scores = model(**inputs).logits.squeeze().item()
    return round(scores, 4)

# Gradio interface
interface = gr.Interface(
    fn=get_relevance_score,
    inputs=[
        gr.Textbox(label="Query", placeholder="Enter your search query..."),
        gr.Textbox(label="Document Paragraph", placeholder="Enter a paragraph to match...")
    ],
    outputs=gr.Number(label="Relevance Score"),
    title="Cross-Encoder Relevance Scoring",
    description="Enter a query and a document paragraph to get a relevance score using the MS MARCO MiniLM L-12 v2 model."
)

if __name__ == "__main__":
    interface.launch()