Update app.py

app.py

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+# streamlit_app.py
+# import streamlit as st
+import pandas as pd
+import torch
+from sentence_transformers import SentenceTransformer, util
+import pickle
+import numpy as np
+
+import os
+import importlib
+
+#Load sentences & embeddings from disc
+with open('mean_clinical_inno_embeddings_masterid_paraphrase-multilingual-mpnet-base-v2.pkl', "rb") as fIn:
+    stored_data = pickle.load(fIn)
+    stored_masterid = stored_data['pro_master_id']
+    stored_products = stored_data['mean_products']
+    stored_embeddings = stored_data['mean_embeddings']
+
+# Initialize the SentenceTransformer model
+embedder = SentenceTransformer('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')
+
+def get_similar_products(query, products, mean_embeddings_tensor, top_k=10):
+    query_embedding = embedder.encode(query, convert_to_tensor=True)
+    cos_scores = util.cos_sim(query_embedding, mean_embeddings_tensor)[0]
+    top_results = torch.topk(cos_scores, k=top_k)
+    
+    similar_products = [(products[idx.item()], score.item()) for score, idx in zip(top_results[0], top_results[1])]
+    return similar_products
+
+
+# Streamlit UI
+st.title("Product Similarity Finder")
+
+# User input
+user_query = "SuperSole Bred læst er en utrolig..."
+
+results = get_similar_products(user_query, stored_products, stored_embeddings)
+for product, score in results:
+    st.write(f"Product: {product} (Score: {score:.4f})")