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dash_app.py
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dash_app.py
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import logging
import dash
from dash.dependencies import Input, Output
from dash import dcc, html, dash_table
import plotly.graph_objs as go
from sklearn.decomposition import PCA
import numpy as np
import pandas as pd
from db import fetch_papers_as_df
from link_extractor import decode_embedding
from flask import Flask
# This is very much a v1, but we are at least rendering the embeddings in 3d,
# and showing a table with the paper data.
class DashApp:
def __init__(self, db_name):
logging.getLogger("werkzeug").setLevel(logging.WARNING)
self.db_name = db_name
self.app = dash.Dash(__name__, server=Flask(__name__))
self.setup_layout()
self.register_callbacks()
def setup_layout(self):
self.app.layout = html.Div(
[
html.Button("Refresh Data", id="refresh-button"),
dcc.Graph(id="3d-plot"),
dash_table.DataTable(
id="paper-table",
style_table={"overflowX": "auto", "width": "100%", "maxWidth": "100%"},
style_cell={"textAlign": "left", "padding": "5px"},
style_header={"backgroundColor": "lightgrey", "fontWeight": "bold"},
),
],
style={"width": "80%", "margin": "auto"},
)
def register_callbacks(self):
@self.app.callback(
[
Output("3d-plot", "figure"),
Output("paper-table", "data"),
Output("paper-table", "columns"),
],
[Input("refresh-button", "n_clicks")],
)
def update_graph(n_clicks):
df = self.fetch_and_process_new_papers()
if not df.empty and len(df) > 3:
created_at_float = (df["created_at"] - df["created_at"].min()) / (
df["created_at"].max() - df["created_at"].min()
)
marker_args = dict(
size=10,
opacity=0.8,
color=created_at_float,
colorscale="Plotly3",
colorbar=dict(title="created at. 1=new 0=old"),
)
trace = go.Scatter3d(
x=df["x"],
y=df["y"],
z=df["z"],
mode="markers",
hovertext=df.apply(get_text, axis=1),
hoverinfo="text",
hoverinfosrc="hovertext",
marker=marker_args,
)
layout = go.Layout(
margin={"l": 0, "r": 0, "b": 0, "t": 0},
scene=dict(xaxis=dict(title=""), yaxis=dict(title=""), zaxis=dict(title="")),
height=900,
)
graph_figure = {"data": [trace], "layout": layout}
table_data = df.to_dict("records")
columns = [{"name": i, "id": i} for i in df.columns]
return (graph_figure, table_data, columns)
else:
return ({"data": [], "layout": dict()}, [], [])
def fetch_and_process_new_papers(self):
df = fetch_papers_as_df(self.db_name)
if df.empty or len(df) <= 3:
return pd.DataFrame(columns=["x", "y", "z", "created_at", "url"])
df["decoded_embedding"] = df["embedding"].apply(decode_embedding)
df = df[df["decoded_embedding"].apply(lambda x: x.shape[0]) > 0]
embeddings_matrix = np.stack(df["decoded_embedding"].values)
pca = PCA(n_components=3)
pca_result = pca.fit_transform(embeddings_matrix)
df["x"], df["y"], df["z"] = pca_result[:, 0], pca_result[:, 1], pca_result[:, 2]
df["url"] = df["url"].transform(trim_url)
return df[
[
"url",
"title",
"authors",
"published_date",
"summary",
"institution",
"location",
"status",
"created_at",
"x",
"y",
"z",
]
]
def run(self, debug=False):
self.app.run_server(debug=debug)
def get_text(row):
parts = [row["url"].split("/")[-1], row.get("title", "")]
filtered_parts = [part for part in parts if part]
return "<br>".join(filtered_parts)
def trim_url(url, max_length=50):
if len(url) <= max_length:
return url
else:
part_length = max_length // 2 - 2
return url[:part_length] + "..." + url[-part_length:]