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import base64
import io
import chartgpt as cg
import dash
import dash_ag_grid as dag
import dash_mantine_components as dmc
import pandas as pd
from dash import Input, Output, State, dcc, html, no_update
from dash_iconify import DashIconify
app = dash.Dash(
__name__,
external_stylesheets=[
"https://fonts.googleapis.com/css2?family=Inter:wght@100;200;300;400;500;900&display=swap",
],
title="ChartGPT",
update_title="ChartGPT | Loading...",
assets_folder="assets",
include_assets_files=True,
)
server = app.server
body = dmc.Stack(
[
dmc.Stepper(
id="stepper",
contentPadding=30,
active=0,
size="md",
breakpoint="sm",
children=[
dmc.StepperStep(
label="Upload your CSV file",
icon=DashIconify(icon="material-symbols:upload"),
progressIcon=DashIconify(icon="material-symbols:upload"),
completedIcon=DashIconify(icon="material-symbols:upload"),
children=[
dmc.Stack(
[
dcc.Upload(
id="upload-data",
children=html.Div(
[
"Drag and Drop or",
dmc.Button(
"Select CSV File",
ml=10,
leftIcon=DashIconify(
icon="material-symbols:upload"
),
),
]
),
max_size=5 * 1024 * 1024, # 5MB
style={
"borderWidth": "1px",
"borderStyle": "dashed",
"borderRadius": "5px",
"textAlign": "center",
"padding": "10px",
"backgroundColor": "#fafafa",
},
style_reject={
"borderColor": "red",
},
multiple=False,
),
dmc.Title("Preview", order=3, color="primary"),
html.Div(id="output-data-upload"),
]
)
],
),
dmc.StepperStep(
label="Plot your data ",
icon=DashIconify(icon="bi:bar-chart"),
progressIcon=DashIconify(icon="bi:bar-chart"),
completedIcon=DashIconify(icon="bi:bar-chart-fill"),
children=[
dmc.Stack(
[
dmc.Textarea(
id="input-text",
placeholder="Write here",
autosize=True,
description="""Type in your questions or requests related to your CSV file. GPT will write the code to visualize the data and find the answers you're looking for.""",
maxRows=2,
),
dmc.Title("Preview", order=3, color="primary"),
html.Div(id="output-data-upload-preview"),
]
)
],
),
dmc.StepperCompleted(
children=[
dmc.Stack(
[
dmc.Textarea(
id="input-text-retry",
description="""Type in your questions or requests related to your CSV file. GPT will write the code to visualize the data and find the answers you're looking for.""",
placeholder="Write here",
autosize=True,
icon=DashIconify(icon="material-symbols:search"),
maxRows=2,
),
dmc.LoadingOverlay(
id="output-card",
mih=300,
loaderProps={
"variant": "bars",
"color": "primary",
"size": "xl",
},
),
]
)
]
),
],
),
dmc.Group(
[
dmc.Button(
"Back",
id="stepper-back",
display="none",
size="md",
variant="outline",
radius="xl",
leftIcon=DashIconify(icon="ic:round-arrow-back"),
),
dmc.Button(
"Next",
id="stepper-next",
size="md",
radius="xl",
rightIcon=DashIconify(
icon="ic:round-arrow-forward", id="icon-next"
),
),
],
position="center",
mb=20,
),
]
)
header = dmc.Center(
html.A(
dmc.Image(
id="logo",
src="/assets/logo_light.svg",
alt="ChartGPT Logo",
width=300,
m=20,
),
href="https://github.com/chatgpt/chart",
style={"textDecoration": "none"},
)
)
theme_toggle = dmc.Switch(
id="theme-toggle",
size="lg",
onLabel="",
offLabel="",
checked=False,
mb=20,
style={"position": "absolute", "top": "10px", "right": "10px"},
)
socials = dmc.Affix(
dmc.Stack(
[
dmc.ActionIcon(
html.A(
DashIconify(icon="mdi:github", width=25),
href="https://github.com/chatgpt/chart",
style={"color": "black"},
),
),
dmc.ActionIcon(
html.A(
DashIconify(icon="mdi:linkedin", width=25),
href="https://www.linkedin.com/in/eliebrosset/",
style={"color": "#0B65C2"},
),
),
],
spacing="sm",
),
position={"top": 10, "left": 10},
)
def show_graph_card(graph, code):
return dmc.Card(
dmc.Stack(
[
html.Div(graph),
dmc.Accordion(
variant="separated",
chevronPosition="right",
radius="md",
children=[
dmc.AccordionItem(
[
dmc.AccordionControl(
"Show code",
icon=DashIconify(icon="solar:code-bold"),
),
dmc.AccordionPanel(
dmc.Prism(
code,
language="python",
id="output-code",
withLineNumbers=True,
),
),
],
value="customization",
)
],
),
]
)
)
page = [
dcc.Store(id="dataset-store", storage_type="local"),
dmc.Container(
[
theme_toggle,
dmc.Stack(
[
socials,
header,
dmc.Alert(
"",
title="Error",
id="alert-error",
color="red",
withCloseButton=True,
hide=True
),
body,
]
),
]
),
]
# Define theme colors globally
CUSTOM_COLORS = {
"custom": ["#FFFFFF", "#F2F2F2", "#E5E5E5", "#D9D9D9", "#BFBFBF", "#8C8C8C", "#595959", "#3D3D3D", "#1E1E1E", "#000000"],
}
LIGHT_THEME = {
"colorScheme": "light",
"primaryColor": "custom",
"colors": CUSTOM_COLORS,
"fontFamily": "'Inter', sans-serif",
"defaultRadius": "md",
"white": "#fff",
"black": "#1E1E1E",
"primaryShade": 8, # This will make it use #1E1E1E
}
DARK_THEME = {
**LIGHT_THEME,
"colorScheme": "dark",
}
app.layout = dmc.MantineProvider(
id="mantine-provider",
theme=LIGHT_THEME,
withGlobalStyles=True,
withNormalizeCSS=True,
children=page,
inherit=True,
)
def parse_contents(contents, filename):
content_type, content_string = contents.split(",")
decoded = base64.b64decode(content_string)
try:
if "csv" in filename:
# Assuming the uploaded file is a CSV, parse it
df = pd.read_csv(io.StringIO(decoded.decode("utf-8")))
return df
else:
return "Invalid file format, please upload a CSV file."
except Exception as e:
print(e)
return "An error occurred while processing the file."
@app.callback(
Output("dataset-store", "data"),
Input("upload-data", "contents"),
State("upload-data", "filename"),
prevent_initial_call=True,
)
def store_data(contents, filename):
if contents is not None:
df = parse_contents(contents, filename)
return df.to_json(orient="split")
@app.callback(
Output("output-data-upload", "children"),
Output("output-data-upload-preview", "children"),
Output("upload-data", "style"),
Output("upload-data", "children"),
Input("dataset-store", "data"),
)
def load_data(dataset):
if dataset is not None:
df = pd.read_json(io.StringIO(dataset), orient="split")
table_preview = dag.AgGrid(
id="data-preview",
rowData=df.to_dict("records"),
style={"height": "275px"},
columnDefs=[{"field": i} for i in df.columns],
dashGridOptions={"defaultColDef": {"resizable": True, "sortable": True, "filter": True}},
className="ag-theme-alpine",
)
return (
table_preview,
table_preview,
{
"borderWidth": "1px",
"borderStyle": "dashed",
"borderRadius": "5px",
"textAlign": "center",
"padding": "7px",
"backgroundColor": "#fafafa",
},
dmc.Group(
[
html.Div(
[
"Drag and Drop or",
dmc.Button(
"Replace file",
ml=10,
leftIcon=DashIconify(icon="mdi:file-replace"),
),
]
)
],
position="center",
align="center",
spacing="xs",
),
)
return no_update
@app.callback(
Output("stepper", "active"),
Input("stepper-next", "n_clicks"),
Input("stepper-back", "n_clicks"),
State("stepper", "active"),
prevent_initial_call=True,
)
def update_stepper(stepper_next, stepper_back, current):
ctx = dash.callback_context
id_clicked = ctx.triggered[0]["prop_id"]
if id_clicked == "stepper-next.n_clicks" and current < 2:
return current + 1
elif id_clicked == "stepper-back.n_clicks":
return current - 1
return no_update
@app.callback(
Output("stepper-next", "disabled"),
Output("stepper-back", "disabled"),
Output("stepper-next", "display"),
Output("stepper-back", "display"),
Output("stepper-next", "children"),
Output("icon-next", "icon"),
Input("stepper", "active"),
Input("dataset-store", "data"),
)
def update_stepper_buttons(current, data):
if current == 0 and data is not None:
return (
False,
False,
"block",
"block",
"Next",
"ic:round-arrow-forward",
)
elif current == 0 and data is None:
return (
True,
False,
"block",
"block",
"Next",
"ic:round-arrow-forward",
)
elif current == 1:
return (
False,
False,
"block",
"block",
"Ask ChartGPT",
"ph:flask-bold",
)
elif current == 2:
return (False, False, "block", "block", "Ask again", "ic:refresh")
@app.callback(
[Output("mantine-provider", "theme"), Output("logo", "src"), Output("data-preview", "className")],
[Input("theme-toggle", "checked")],
)
def toggle_theme(is_dark_mode):
if is_dark_mode:
return DARK_THEME, "/assets/logo_light.svg", "ag-theme-alpine-dark"
return LIGHT_THEME, "/assets/logo_dark.svg", "ag-theme-alpine"
@app.callback(
Output("input-text-retry", "value"),
Output("output-card", "children"),
Output("alert-error", "hide"),
Output("alert-error", "children"),
Input("stepper-next", "n_clicks"),
State("stepper", "active"),
State("dataset-store", "data"),
State("input-text", "value"),
State("input-text-retry", "value"),
prevent_initial_call=True,
)
def update_graph(n_clicks, active, df, prompt, prompt_retry):
if n_clicks is not None and active == 1:
try:
return prompt, predict(df, prompt), True, ""
except Exception as e:
return no_update, no_update, False, str(e)
elif n_clicks is not None and active == 2:
try:
return prompt_retry, predict(df, prompt_retry), True, ""
except Exception as e:
return no_update, no_update, False, str(e)
return no_update
def predict(df, prompt):
df = pd.read_json(df, orient="split")
chart = cg.Chart(df, model="huggingface/Qwen/Qwen2.5-Coder-32B-Instruct")
fig = chart.plot(prompt, return_fig=True)
output = show_graph_card(graph=dcc.Graph(figure=fig), code=chart.last_run_code)
return output
if __name__ == "__main__":
app.run_server()
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