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import streamlit as st |
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import pandas as pd |
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import plotly.express as px |
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import torch.nn.functional as F |
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from sentence_transformers import SentenceTransformer |
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import numpy as np |
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from sklearn.manifold import TSNE |
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import plotly.express as px |
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import torch |
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import plotly.io as pio |
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pio.templates.default = "plotly" |
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st. set_page_config(layout="wide") |
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st.header("Explore the Russian Dolls :nesting_dolls: - _ :green[Nomic Embed 1.5] _",divider='violet') |
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st.write("Matryoshka Representation Learning : to learn more :https://aniketrege.github.io/blog/2024/mrl/") |
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@st.cache_data |
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def get_df(): |
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prodDf = pd.read_csv("./sample_products.csv") |
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return prodDf |
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@st.cache_resource |
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def get_nomicModel(): |
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model = SentenceTransformer("nomic-ai/nomic-embed-text-v1.5", trust_remote_code=True) |
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return model |
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def get_searchQueryEmbedding(query): |
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embeddings = model.encode(["search_query: "+query], convert_to_tensor=True) |
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embeddings = F.layer_norm(embeddings, normalized_shape=(embeddings.shape[1],)) |
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return embeddings |
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def get_normEmbed(query_embedding,loaded_embed,matryoshka_dim): |
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query_embedNorm = query_embedding[:, :matryoshka_dim] |
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query_embedNorm = F.normalize(query_embedNorm, p=2, dim=1) |
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loaded_embedNorm = loaded_embed[:, :matryoshka_dim] |
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loaded_embedNorm = F.normalize(loaded_embedNorm, p=2, dim=1) |
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return query_embedNorm,loaded_embedNorm |
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def insert_line_breaks(text, interval=30): |
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words = text.split(' ') |
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wrapped_text = '' |
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line_length = 0 |
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for word in words: |
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wrapped_text += word + ' ' |
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line_length += len(word) + 1 |
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if line_length >= interval: |
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wrapped_text += '<br>' |
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line_length = 0 |
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return wrapped_text.strip() |
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model = get_nomicModel() |
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bigDollEmbedding = get_df()["Description"] |
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docEmbedding = torch.Tensor(np.load("./prodBigDollEmbeddings.npy")) |
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toggle = st.toggle('sample queries') |
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with st.form("my_form"): |
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if toggle: |
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query_input = st.selectbox('select a query:', |
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('Pack of two assorted boxers, has two pockets, an elasticated waistbandDisclaimer: The final product delivered might vary in colour and prints from the display here.', |
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'Beige self design shoulder bag, has a zip closure1 main compartment, 3 inner pocketsTwo Handles', |
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'Set Content: 1 photo frameColour: Black and whiteFrame Pattern: SolidShape: SquareMaterial: Acrylic', |
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'A pair of dark grey solid boxers, has a slip-on closure with an elasticated waistband and drawstring, two pocket', |
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'Red & Black solid sweatshirt, has a hood, two pockets, long sleeves, zip closure, straight hem')) |
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else: |
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query_input = st.text_input("") |
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Matry_dim = st.slider('Matryoshka Dimension', 64, 768, 64) |
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submitted = st.form_submit_button("Submit") |
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if submitted: |
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queryEmbedding = get_searchQueryEmbedding(query_input) |
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query_embedNorm,loaded_embedNorm = get_normEmbed(queryEmbedding,docEmbedding,Matry_dim) |
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similarity_scores = torch.matmul(query_embedNorm,loaded_embedNorm.T) |
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top_values, top_indices = torch.topk(similarity_scores, 10, dim=1) |
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to_index = list(top_indices.numpy()[0]) |
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top_items_per_query = [bigDollEmbedding.tolist()[index] for index in to_index] |
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print(top_values) |
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df = pd.DataFrame({"Product":top_items_per_query,"Score":top_values[0]}) |
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df["Product"] = df["Product"].str.replace("search_document:","") |
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allEmbedd = torch.concat([query_embedNorm,loaded_embedNorm]) |
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tsne = TSNE(n_components=2, random_state=0) |
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projections = tsne.fit_transform(allEmbedd) |
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listHover = bigDollEmbedding.tolist() |
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listHover =[insert_line_breaks(hover_text, 30) for hover_text in listHover] |
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fig = px.scatter( |
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projections, x=0, y=1, |
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hover_name=[query_input]+listHover, |
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color=["search_query"]+(["search_document"]*270) |
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) |
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col1, col2 = st.columns([2, 2]) |
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col2.plotly_chart(fig, use_container_width=True) |
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col1.dataframe(df) |
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st.caption("Dataset Credit : kaggle") |
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