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import streamlit as st
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = 'intfloat/multilingual-e5-large'
adapters_name = './checkpoint-21170'
model = AutoModelForCausalLM.from_pretrained(model_name)
model = PeftModel.from_pretrained(model, adapters_name)
model = model.merge_and_unload()
tokenizer = AutoTokenizer.from_pretrained(model_name)
description = st.text_input("Product description")
review = st.text_input("Review")
if description and review:
input_texts = [
f'query: {review}',
f'passage: {description}'
]
batch_dict = tokenizer(input_texts, max_length=512,
padding=True, truncation=True, return_tensors='pt')
query_embedding, doc_embedding = model(**batch_dict).pooler_output
similarity = torch.nn.functional.cosine_similarity(
query_embedding, doc_embedding)
threshold = 0.7
if similarity > threshold:
st.write('Relevant')
else:
st.write('Irrelevant')
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