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import streamlit as st
import vec2text
import torch
from umap import UMAP
import plotly.express as px
import numpy as np
from streamlit_plotly_events import plotly_events
from resources import reduce_embeddings
import utils
import pandas as pd
from scipy.spatial import distance
def diffs(embeddings: np.ndarray, corrector):
st.text(f"Embedding shape: {embeddings.shape}")
st.html('<a href="https://www.flaticon.com/free-icons/array" title="array icons">Array icons created by Voysla - Flaticon</a>')
def plot(df: pd.DataFrame, embeddings: np.ndarray, vectors_2d, reducer, corrector):
# Add a scatter plot using Plotly
fig = px.scatter(
x=vectors_2d[:, 0],
y=vectors_2d[:, 1],
opacity=0.6,
hover_data={"Title": df["title"]},
labels={'x': 'UMAP Dimension 1', 'y': 'UMAP Dimension 2'},
title="UMAP Scatter Plot of Reddit Titles",
color_discrete_sequence=["#ff504c"] # Set default blue color for points
)
# Customize the layout to adapt to browser settings (light/dark mode)
fig.update_layout(
template=None, # Let Plotly adapt automatically based on user settings
plot_bgcolor="rgba(0, 0, 0, 0)",
paper_bgcolor="rgba(0, 0, 0, 0)"
)
x, y = 0.0, 0.0
vec = np.array([x, y]).astype("float32")
inferred_embedding = None
# Add a card container to the right of the content with Streamlit columns
col1, col2 = st.columns([0.6, 0.4]) # Adjusting ratio to allocate space for the card container
with col1:
# Main content stays here (scatterplot, form, etc.)
selected_points = plotly_events(fig, click_event=True, hover_event=False, #override_height=600, override_width="100%"
)
with st.form(key="form1_main"):
if selected_points:
clicked_point = selected_points[0]
x = clicked_point['x']
y = clicked_point['y']
x = st.number_input("X Coordinate", value=x, format="%.10f")
y = st.number_input("Y Coordinate", value=y, format="%.10f")
vec = np.array([x, y]).astype("float32")
submit_button = st.form_submit_button("Submit")
if selected_points or submit_button:
inferred_embedding = reducer.inverse_transform(np.array([[x, y]]) if not isinstance(reducer, UMAP) else np.array([[x, y]]))
inferred_embedding = inferred_embedding.astype("float32")
output = vec2text.invert_embeddings(
embeddings=torch.tensor(inferred_embedding).cuda(),
corrector=corrector,
num_steps=20,
)
st.text(str(output))
st.text(str(inferred_embedding))
else:
st.text("Click on a point in the scatterplot to see its coordinates.")
with col2:
closest_sentence_index = utils.find_exact_match(vectors_2d, vec, decimals=3)
selected_sentence = df.title.iloc[closest_sentence_index] if closest_sentence_index > -1 else None
selected_sentence_embedding = embeddings[closest_sentence_index] if closest_sentence_index > -1 else None
st.markdown(
f"### Selected text:\n```console\n{selected_sentence}\n```"
)
if inferred_embedding is not None and (closest_sentence_index != -1):
couple = selected_sentence_embedding.squeeze(), inferred_embedding.squeeze()
st.markdown(f"### Inferred embedding distance:")
st.number_input("Euclidean", value=distance.euclidean(
*couple
), disabled=True)
st.number_input("Cosine", value=distance.cosine(*couple), disabled=True)
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