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import os
import tempfile
import pandas as pd
import plotly.graph_objects as go
def plot_metric_tool(
file_path: str,
date_col: str,
value_col: str,
output_dir: str = "/tmp",
title: str = None,
line_width: int = 2,
marker_size: int = 6
):
"""
Load CSV or Excel file, parse a time series metric, and return an interactive Plotly Figure.
Also saves a high-resolution PNG to a temp directory for static embedding.
Returns:
fig (go.Figure) or error string starting with '❌'.
"""
# 0) Load data
ext = os.path.splitext(file_path)[1].lower()
try:
if ext in ('.xls', '.xlsx'):
df = pd.read_excel(file_path)
else:
df = pd.read_csv(file_path)
except Exception as exc:
return f"❌ Failed to load file: {exc}"
# 1) Validate columns
missing = [c for c in (date_col, value_col) if c not in df.columns]
if missing:
return f"❌ Missing column(s): {', '.join(missing)}"
# 2) Parse date and ensure numeric values
try:
df[date_col] = pd.to_datetime(df[date_col], errors='coerce')
except Exception:
return f"❌ Could not parse '{date_col}' as dates."
df[value_col] = pd.to_numeric(df[value_col], errors='coerce')
df = df.dropna(subset=[date_col, value_col])
if df.empty:
return f"❌ No valid data after cleaning '{date_col}'/'{value_col}'"
# 3) Sort and aggregate duplicates
df = (
df[[date_col, value_col]]
.groupby(date_col, as_index=True)
.mean()
.sort_index()
)
# 4) Create Plotly figure
fig = go.Figure(
data=[
go.Scatter(
x=df.index,
y=df[value_col],
mode='lines+markers',
line=dict(width=line_width),
marker=dict(size=marker_size),
name=value_col
)
]
)
plot_title = title or f"{value_col} Trend"
fig.update_layout(
title=plot_title,
xaxis_title=date_col,
yaxis_title=value_col,
template='plotly_dark',
hovermode='x unified'
)
# 5) Save static PNG
os.makedirs(output_dir, exist_ok=True)
tmpfile = tempfile.NamedTemporaryFile(
suffix='.png', prefix='trend_', dir=output_dir, delete=False
)
img_path = tmpfile.name
tmpfile.close()
try:
fig.write_image(img_path, scale=2)
except Exception as exc:
return f"❌ Failed saving image: {exc}"
# 6) Return figure and path for embedding
return fig, img_path
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