Create app.py

app.py

@@ -0,0 +1,37 @@
+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')