Commit
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d845ab5
1
Parent(s):
50d230c
Update app.py
Browse files
app.py
CHANGED
@@ -1,8 +1,111 @@
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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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st.
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st.write("matryoshka representation learning")
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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")
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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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# Automatically wrap the hover text
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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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with st.form("my_form"):
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query_input = st.text_input("query your product")
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sample_products = ["a","b","c"]
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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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Matry_dim = st.slider('Matryoshka Dimension', 64, 768, 64)
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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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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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# st.dataframe(df)
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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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