Create 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
+
+
+# Load the first set of sentences & embeddings from disk
+with open('clinical_inno_embeddings_masterid_paraphrase-multilingual-mpnet-base-v2.pkl', "rb") as fIn:
+    stored_data_1 = pickle.load(fIn)
+    stored_masterid_1 = stored_data_1['pro_master_id']
+    stored_products_1 = stored_data_1['products']
+    stored_embeddings_1 = stored_data_1['embeddings']
+
+# Load the second set of sentences & embeddings from disk
+# Replace 'other_embeddings.pkl' with your actual second embeddings file
+with open('mean_clinical_inno_embeddings_masterid_paraphrase-multilingual-mpnet-base-v2.pkl', "rb") as fIn:
+    stored_data_2 = pickle.load(fIn)
+    stored_masterid_2 = stored_data_2['pro_master_id']
+    stored_products_2 = stored_data_2['mean_products']
+    stored_embeddings_2 = stored_data_2['mean_embeddings']
+
+# Initialize the SentenceTransformer model
+embedder = SentenceTransformer('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')
+
+def get_similar_products(query, products, embeddings, top_k=10):
+    query_embedding = embedder.encode(query, convert_to_tensor=True)
+    cos_scores = util.cos_sim(query_embedding, embeddings)[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")
+
+# Embedding selection slider
+embedding_option = st.select_slider(
+    'Select Search Approach',
+    options=['All Products', 'Master Products']
+)
+
+# Determine which embeddings to use based on the slider selection
+if embedding_option == 'All Products':
+    stored_products = stored_products_1
+    st.write(len(stored_products))
+    stored_embeddings = stored_embeddings_1
+else:
+    stored_products = stored_products_2
+    st.write(len(stored_products))
+    stored_embeddings = stored_embeddings_2
+
+# User input
+user_input = st.text_input("Enter a product name or description:")
+
+# Search button
+if st.button('Search'):
+    if user_input:
+        # Get and display similar products
+        results = get_similar_products(user_input, stored_products, stored_embeddings)
+        
+        # Convert results to a DataFrame for nicer display
+        results_df = pd.DataFrame(results, columns=['Product', 'Score'])
+        
+        # Use Streamlit's dataframe function to display results in a table with default formatting
+        st.dataframe(results_df.style.format({'Score': '{:.4f}'}))
+    else:
+        st.write("Please enter a product name or description to search.")