Create app.py

app.py

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+"""
+A Streamlit application to visualize sentence embeddings
+Author: Mohit Mayank
+Contact: mohitmayank1@gmail.com
+"""
+
+## Import
+## ----------------
+# data
+import pandas as pd
+# model
+from sentence_transformers import SentenceTransformer, util
+# viz
+import streamlit as st
+import plotly.express as px
+# DR
+from sklearn.decomposition import PCA
+from sklearn.manifold import TSNE
+
+## Init
+## ----------------
+# set config
+# st.set_page_config(layout="wide", page_title="SentenceViz 🕵")
+st.markdown("# SentenceViz")
+st.markdown("A Streamlit application to visulize sentence embeddings")
+
+# load the summarization model (cache for faster loading)
+@st.cache(allow_output_mutation=True)
+def load_similarity_model(model_name='all-MiniLM-L6-v2'):
+    model = SentenceTransformer(model_name)
+    return model
+
+@st.cache(allow_output_mutation=True)
+def perform_embedding(df, text_col_name):
+    embeddings = model.encode(df[text_col_name])
+    return embeddings
+
+# gloabl vars
+df = None
+model = None
+embeddings = None
+
+## Design Sidebar
+## -----------------
+## Data
+st.sidebar.markdown("## Data")
+uploaded_file = st.sidebar.file_uploader("Upload a CSV file with sentences (we remove NaN)")
+if uploaded_file is not None:
+    progress = st.empty()
+    progress.text("Reading file...")
+    df = pd.read_csv(uploaded_file).dropna().reset_index(drop=True)
+    progress.text(f"Reading file...Done! Size: {df.shape[0]}")
+
+## Embedding
+st.sidebar.markdown("## Embedding")
+supported_models = ['all-MiniLM-L6-v2', 'paraphrase-albert-small-v2', 'paraphrase-MiniLM-L3-v2', 'all-distilroberta-v1', 'all-mpnet-base-v2']
+selected_model_option = st.sidebar.selectbox("Select Model:", supported_models)
+text_col_name = st.sidebar.text_input("Text column to embed")
+if len(text_col_name) > 0 and df is not None:
+    print("text_col_name -->", text_col_name)
+    df[text_col_name] = df[text_col_name].str.wrap(30)
+    df[text_col_name] = df[text_col_name].apply(lambda x: x.replace('\n', '<br>'))
+    progress = st.empty()
+    progress.text("Creating embedding...")
+    model = load_similarity_model(selected_model_option)
+    embeddings = perform_embedding(df, text_col_name)
+    progress.text("Creating embedding...Done!")
+
+## Visualization
+st.sidebar.markdown("## Visualization")
+dr_algo = st.sidebar.selectbox("Dimensionality Reduction Algorithm", ('PCA', 't-SNE'))
+color_col = st.sidebar.text_input("Color using this col")
+if len(color_col.strip()) == 0:
+    color_col = None
+
+if st.sidebar.button('Plot!'):
+    # get the embeddings and perform DR
+    if dr_algo == 'PCA':
+        pca = PCA(n_components=2)
+        reduced_embeddings = pca.fit_transform(embeddings)
+    elif dr_algo == 't-SNE':
+        tsne = TSNE(n_components=2)
+        reduced_embeddings = tsne.fit_transform(embeddings)
+    
+    # modify the df
+    # df['complete_embeddings'] = embeddings
+    df['viz_embeddings_x'] = reduced_embeddings[:, 0]
+    df['viz_embeddings_y'] = reduced_embeddings[:, 1]
+
+    # plot the data
+    fig = px.scatter(df, x='viz_embeddings_x', y='viz_embeddings_y', 
+            title=f'"{dr_algo}" on {df.shape[0]} "{selected_model_option}" embeddings', 
+            color=color_col, hover_data=[text_col_name])
+    fig.update_layout(yaxis={'visible': False, 'showticklabels': False})
+    fig.update_layout(xaxis={'visible': False, 'showticklabels': False})
+    fig.update_traces(marker=dict(size=10, opacity=0.7, line=dict(width=1,color='DarkSlateGrey')),selector=dict(mode='markers'))
+    st.plotly_chart(fig, use_container_width=True)