from typing import Dict, List, Union
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import streamlit as st
from plotly.subplots import make_subplots
class ChartBuilder:
"""Builds charts and KPIs with error handling and fallback to tables"""
@staticmethod
def create_kpi_card(
value, title: str, format_type: str = "number", color: str = "#1f77b4"
):
"""Creates a KPI metric card"""
try:
if pd.isna(value) or value is None:
formatted_value = "N/A"
else:
if format_type == "currency":
formatted_value = f"${float(value):,.2f}"
elif format_type == "percentage":
formatted_value = f"{float(value):.1f}%"
else:
formatted_value = f"{float(value):,.0f}"
# Using Streamlit's built-in metric
st.metric(label=title, value=formatted_value)
except Exception as e:
st.error(f"Error creating KPI card: {str(e)}")
st.metric(label=title, value="Error")
@staticmethod
def create_metric_cards(
data: Union[pd.DataFrame, Dict],
config: Dict,
tooltip_fields: List[str] = None
) -> None:
"""
Creates multiple metric cards from DataFrame or direct values
Args:
data: DataFrame or dict with values
config: Configuration with columns/keys and formatting
tooltip_fields: List of tooltip descriptions for each metric
"""
try:
columns = config.get("columns", [])
titles = config.get("titles", columns)
formats = config.get("formats", ["number"] * len(columns))
colors = config.get("colors", ["#1f77b4"] * len(columns))
if not columns:
st.error("No columns specified for metric cards")
return
# Create columns for layout
cols = st.columns(len(columns))
for i, (col, title, fmt, color) in enumerate(zip(columns, titles, formats, colors)):
with cols[i]:
try:
# Get value from DataFrame or dict
if isinstance(data, pd.DataFrame):
if col in data.columns:
value = data[col].iloc[0] if len(data) > 0 else 0
else:
value = 0
st.warning(f"Column '{col}' not found")
else:
value = data.get(col, 0)
# Format value
if pd.isna(value) or value is None:
formatted_value = "N/A"
else:
if fmt == "currency":
formatted_value = f"${float(value):,.2f}"
elif fmt == "percentage":
formatted_value = f"{float(value):.1f}%"
else:
formatted_value = f"{float(value):,.0f}"
# Create metric with tooltip
st.metric(
label=title,
value=formatted_value,
help=tooltip_fields[i] if tooltip_fields and i < len(tooltip_fields) else None
)
except Exception as e:
st.metric(label=title, value="Error", help=f"Error: {str(e)}")
except Exception as e:
st.error(f"Error creating metric cards: {str(e)}")
@staticmethod
def create_recommendation_table(
df: pd.DataFrame,
config: Dict,
tooltip_fields: List[str] = None
) -> None:
"""
Creates an enhanced recommendation table with better visualization
Args:
df: DataFrame with cause and recommendation data
config: Configuration with x_axis (cause) and y_axis (recommendation)
tooltip_fields: List of tooltip descriptions
"""
try:
if df.empty:
st.warning("No recommendation data available")
return
title = config.get("title", "Recommendations")
cause_col = config.get("x_axis", "cause")
recommendation_col = config.get("y_axis", "recommendation")
# Validate columns exist
if cause_col not in df.columns or recommendation_col not in df.columns:
st.error(f"Required columns '{cause_col}' or '{recommendation_col}' not found")
ChartBuilder._create_table(df, title)
return
st.subheader(title)
# Create enhanced recommendation display
for idx, row in df.iterrows():
cause = row[cause_col]
recommendation = row[recommendation_col]
# Create expandable recommendation card
with st.expander(f"🔍 {cause}", expanded=False):
col1, col2 = st.columns([1, 3])
with col1:
# Status/Priority indicator
priority = row.get('priority', 'Medium')
if priority.lower() == 'high':
st.error("🔴 High Priority")
elif priority.lower() == 'low':
st.success("🟢 Low Priority")
else:
st.warning("🟡 Medium Priority")
with col2:
# Recommendation details
st.markdown(f"**Recommendation:**")
st.info(recommendation)
# Additional details if available
if 'impact' in row and pd.notna(row['impact']):
st.markdown(f"**Expected Impact:** {row['impact']}")
if 'timeline' in row and pd.notna(row['timeline']):
st.markdown(f"**Timeline:** {row['timeline']}")
# Tooltip information
if tooltip_fields and idx < len(tooltip_fields):
st.help(tooltip_fields[idx])
# Summary metrics if numeric data available
numeric_cols = df.select_dtypes(include=[np.number]).columns
if len(numeric_cols) > 0:
st.markdown("---")
st.markdown("**Summary Metrics:**")
summary_cols = st.columns(len(numeric_cols[:4])) # Limit to 4 metrics
for i, col in enumerate(numeric_cols[:4]):
with summary_cols[i]:
avg_val = df[col].mean()
st.metric(
label=col.replace('_', ' ').title(),
value=f"{avg_val:.2f}",
help=f"Average {col.replace('_', ' ').lower()}"
)
# Action items visualization
if 'status' in df.columns:
st.markdown("---")
st.markdown("**Action Status Overview:**")
status_counts = df['status'].value_counts()
fig = px.pie(
values=status_counts.values,
names=status_counts.index,
title="Recommendation Status Distribution",
color_discrete_map={
'Completed': '#00CC96',
'In Progress': '#FFA15A',
'Pending': '#EF553B',
'Not Started': '#636EFA'
}
)
st.plotly_chart(fig, use_container_width=True)
except Exception as e:
st.error(f"Error creating recommendation table: {str(e)}")
ChartBuilder._create_table(df, title)
@staticmethod
def create_chart(df: pd.DataFrame, config: Dict) -> None:
"""
Creates charts based on config with fallback to table on errors
"""
if df.empty:
st.warning("No data available for chart")
return
chart_type = config.get("chart_type", "table").lower()
title = config.get("title", "Chart")
tooltip_fields = config.get("tooltip_fields", [])
try:
if chart_type == "kpi":
# Handle KPI from dataframe
value_col = config.get("value_column")
if value_col and value_col in df.columns:
value = df[value_col].iloc[0] if len(df) > 0 else 0
ChartBuilder.create_kpi_card(
value,
title,
config.get("format", "number"),
config.get("color", "#1f77b4"),
)
else:
st.error("KPI value column not found")
return
elif chart_type == "metric_cards":
# Handle multiple metric cards
ChartBuilder.create_metric_cards(df, config, tooltip_fields)
return
elif chart_type == "recommendation_table":
# Handle recommendation table
ChartBuilder.create_recommendation_table(df, config, tooltip_fields)
return
elif chart_type == "bar":
x_col = config.get("x_axis")
y_col = config.get("y_axis")
if (
not x_col
or not y_col
or x_col not in df.columns
or y_col not in df.columns
):
raise ValueError("Required columns not found for bar chart")
fig = px.bar(
df,
x=x_col,
y=y_col,
title=title,
color_discrete_sequence=[config.get("color", "#1f77b4")],
)
# Add tooltips if provided
if tooltip_fields:
fig.update_traces(
hovertemplate="<br>".join([f"{field}: %{{customdata[{i}]}}" for i, field in enumerate(tooltip_fields)]) + "<extra></extra>",
customdata=df[tooltip_fields].values if all(field in df.columns for field in tooltip_fields) else None
)
fig.update_layout(showlegend=False)
st.plotly_chart(fig, use_container_width=True)
elif chart_type == "line":
x_col = config.get("x_axis")
y_col = config.get("y_axis")
if (
not x_col
or not y_col
or x_col not in df.columns
or y_col not in df.columns
):
raise ValueError("Required columns not found for line chart")
fig = px.line(
df,
x=x_col,
y=y_col,
title=title,
color_discrete_sequence=[config.get("color", "#1f77b4")],
)
# Add tooltips if provided
if tooltip_fields:
fig.update_traces(
hovertemplate="<br>".join([f"{field}: %{{customdata[{i}]}}" for i, field in enumerate(tooltip_fields)]) + "<extra></extra>",
customdata=df[tooltip_fields].values if all(field in df.columns for field in tooltip_fields) else None
)
st.plotly_chart(fig, use_container_width=True)
elif chart_type == "pie":
y_cols = config.get("y_axis") # This is a list of metric columns
stack_field = config.get("stack_field", "METRIC_TYPE")
title = config.get("title", "Pie Chart")
if not y_cols or not all(col in df.columns for col in y_cols):
raise ValueError("Required columns not found for pie chart")
# Melt and aggregate
df_melted = df.melt(
id_vars=["USER_NAME"] if "USER_NAME" in df.columns else ["user"],
value_vars=y_cols,
var_name=stack_field,
value_name="Value"
)
df_summary = df_melted.groupby(stack_field, as_index=False)["Value"].sum()
df_summary.rename(columns={"Value": "TOTAL_COUNT"}, inplace=True)
# Render pie chart
fig = px.pie(df_summary, names=stack_field, values="TOTAL_COUNT", title=title)
st.plotly_chart(fig, use_container_width=True)
elif chart_type == "scatter":
x_col = config.get("x_axis")
y_col = config.get("y_axis")
if (
not x_col
or not y_col
or x_col not in df.columns
or y_col not in df.columns
):
raise ValueError("Required columns not found for scatter chart")
fig = px.scatter(
df,
x=x_col,
y=y_col,
title=title,
color_discrete_sequence=[config.get("color", "#1f77b4")],
)
# Add tooltips if provided
if tooltip_fields:
fig.update_traces(
hovertemplate="<br>".join([f"{field}: %{{customdata[{i}]}}" for i, field in enumerate(tooltip_fields)]) + "<extra></extra>",
customdata=df[tooltip_fields].values if all(field in df.columns for field in tooltip_fields) else None
)
st.plotly_chart(fig, use_container_width=True)
elif chart_type == "area":
x_col = config.get("x_axis")
y_col = config.get("y_axis")
if (
not x_col
or not y_col
or x_col not in df.columns
or y_col not in df.columns
):
raise ValueError("Required columns not found for area chart")
fig = px.area(
df,
x=x_col,
y=y_col,
title=title,
color_discrete_sequence=[config.get("color", "#1f77b4")],
)
st.plotly_chart(fig, use_container_width=True)
elif chart_type == "histogram":
x_col = config.get("x_axis")
if not x_col or x_col not in df.columns:
raise ValueError("Required column not found for histogram")
fig = px.histogram(
df,
x=x_col,
title=title,
color_discrete_sequence=[config.get("color", "#1f77b4")],
)
st.plotly_chart(fig, use_container_width=True)
elif chart_type == "box":
y_col = config.get("y_axis")
x_col = config.get("x_axis") # optional for grouping
if not y_col or y_col not in df.columns:
raise ValueError("Required column not found for box plot")
fig = px.box(
df,
y=y_col,
x=x_col if x_col and x_col in df.columns else None,
title=title,
color_discrete_sequence=[config.get("color", "#1f77b4")],
)
st.plotly_chart(fig, use_container_width=True)
elif chart_type == "violin":
y_col = config.get("y_axis")
x_col = config.get("x_axis") # optional for grouping
if not y_col or y_col not in df.columns:
raise ValueError("Required column not found for violin plot")
fig = px.violin(
df,
y=y_col,
x=x_col if x_col and x_col in df.columns else None,
title=title,
color_discrete_sequence=[config.get("color", "#1f77b4")],
)
st.plotly_chart(fig, use_container_width=True)
elif chart_type == "heatmap":
# For heatmap, we'll use all numeric columns or specified columns
numeric_cols = df.select_dtypes(include=[np.number]).columns
if len(numeric_cols) < 2:
raise ValueError("Not enough numeric columns for heatmap")
correlation_matrix = df[numeric_cols].corr()
fig = px.imshow(
correlation_matrix, title=title, color_continuous_scale="RdBu_r"
)
st.plotly_chart(fig, use_container_width=True)
elif chart_type == "donut":
names_col = config.get("names_column", config.get("x_axis"))
values_col = config.get("values_column", config.get("y_axis"))
if (
not names_col
or not values_col
or names_col not in df.columns
or values_col not in df.columns
):
raise ValueError("Required columns not found for donut chart")
fig = px.pie(
df,
names=names_col,
values=values_col,
title=title,
hole=0.4, # This makes it a donut chart
)
st.plotly_chart(fig, use_container_width=True)
elif chart_type == "funnel":
x_col = config.get("x_axis")
y_col = config.get("y_axis")
if (
not x_col
or not y_col
or x_col not in df.columns
or y_col not in df.columns
):
raise ValueError("Required columns not found for funnel chart")
fig = px.funnel(
df,
x=y_col,
y=x_col,
title=title,
color_discrete_sequence=[config.get("color", "#1f77b4")],
)
st.plotly_chart(fig, use_container_width=True)
elif chart_type == "gauge":
value_col = config.get("value_column", config.get("y_axis"))
if not value_col or value_col not in df.columns:
raise ValueError("Required column not found for gauge chart")
value = df[value_col].iloc[0] if len(df) > 0 else 0
max_val = config.get("max_value", df[value_col].max())
fig = go.Figure(
go.Indicator(
mode="gauge+number",
value=value,
domain={"x": [0, 1], "y": [0, 1]},
title={"text": title},
gauge={
"axis": {"range": [None, max_val]},
"bar": {"color": config.get("color", "#1f77b4")},
"steps": [
{"range": [0, max_val / 2], "color": "lightgray"},
{"range": [max_val / 2, max_val], "color": "gray"},
],
"threshold": {
"line": {"color": "red", "width": 4},
"thickness": 0.75,
"value": max_val * 0.9,
},
},
)
)
st.plotly_chart(fig, use_container_width=True)
elif chart_type == "treemap":
path_col = config.get("path_column", config.get("x_axis"))
values_col = config.get("values_column", config.get("y_axis"))
if (
not path_col
or not values_col
or path_col not in df.columns
or values_col not in df.columns
):
raise ValueError("Required columns not found for treemap")
fig = px.treemap(df, path=[path_col], values=values_col, title=title)
st.plotly_chart(fig, use_container_width=True)
elif chart_type == "sunburst":
path_col = config.get("path_column", config.get("x_axis"))
values_col = config.get("values_column", config.get("y_axis"))
if (
not path_col
or not values_col
or path_col not in df.columns
or values_col not in df.columns
):
raise ValueError("Required columns not found for sunburst")
fig = px.sunburst(df, path=[path_col], values=values_col, title=title)
st.plotly_chart(fig, use_container_width=True)
elif chart_type == "waterfall":
x_col = config.get("x_axis")
y_col = config.get("y_axis")
if (
not x_col
or not y_col
or x_col not in df.columns
or y_col not in df.columns
):
raise ValueError("Required columns not found for waterfall chart")
fig = go.Figure(
go.Waterfall(
name=title,
orientation="v",
measure=["relative"] * len(df),
x=df[x_col],
textposition="outside",
text=[f"{v:,.0f}" for v in df[y_col]],
y=df[y_col],
connector={"line": {"color": "rgb(63, 63, 63)"}},
)
)
fig.update_layout(title=title, showlegend=False)
st.plotly_chart(fig, use_container_width=True)
elif chart_type == "stacked_bar":
x_col = config.get("x_axis")
y_cols = config.get("y_axis") # This is a list
stack_field = config.get("stack_field", "Metric Type")
title = config.get("title", "Stacked Bar Chart")
# Validate columns
if (
not x_col
or not y_cols
or x_col not in df.columns
or not all(col in df.columns for col in y_cols)
):
raise ValueError("Required columns not found for stacked bar chart")
# Melt the DataFrame to long format
df_melted = df.melt(
id_vars=[x_col],
value_vars=y_cols,
var_name=stack_field,
value_name="Value",
)
# Create the chart
fig = px.bar(
df_melted,
x=x_col,
y="Value",
color=stack_field,
title=title,
barmode="stack",
color_discrete_map=config.get(
"colors", {}
), # Optional custom colors
)
st.plotly_chart(fig, use_container_width=True)
elif chart_type == "grouped_bar":
x_col = config.get("x_axis")
color_col = config.get("color_column")
y_col = config.get("y_axis")
stack_field = config.get("stack_field", "Metric Type")
# if (
# not x_col
# or not y_col
# or x_col not in df.columns
# or not all(col in df.columns for col in y_col)
# ):
# raise ValueError("Required columns not found for grouped bar chart")
df_melted = df.melt(
id_vars=[x_col],
value_vars=y_col,
var_name=stack_field,
value_name="Value",
)
fig = px.bar(
df_melted,
x=x_col,
y="Value",
color=stack_field,
title=title,
barmode="group",
)
st.plotly_chart(fig, use_container_width=True)
elif chart_type == "horizontal_bar":
x_col = config.get("x_axis")
y_col = config.get("y_axis")
if (
not x_col
or not y_col
or x_col not in df.columns
or y_col not in df.columns
):
raise ValueError(
"Required columns not found for horizontal bar chart"
)
fig = px.bar(
df,
x=y_col,
y=x_col,
title=title,
orientation="h",
color_discrete_sequence=[config.get("color", "#1f77b4")],
)
st.plotly_chart(fig, use_container_width=True)
elif chart_type == "multi_line":
x_col = config.get("x_axis")
y_cols = config.get("y_columns", []) # List of columns
if not x_col or not y_cols or x_col not in df.columns:
raise ValueError("Required columns not found for multi-line chart")
fig = go.Figure()
colors = [
"#1f77b4",
"#ff7f0e",
"#2ca02c",
"#d62728",
"#9467bd",
"#8c564b",
]
for i, y_col in enumerate(y_cols):
if y_col in df.columns:
fig.add_trace(
go.Scatter(
x=df[x_col],
y=df[y_col],
mode="lines+markers",
name=y_col,
line=dict(color=colors[i % len(colors)]),
)
)
fig.update_layout(title=title, xaxis_title=x_col, yaxis_title="Values")
st.plotly_chart(fig, use_container_width=True)
elif chart_type == "combo":
x_col = config.get("x_axis")
y_col = config.get("y_axis")
line_col = config.get("line_column")
if not all([x_col, y_col, line_col]) or not all(
[col in df.columns for col in [x_col, y_col, line_col]]
):
raise ValueError("Required columns not found for combo chart")
fig = make_subplots(specs=[[{"secondary_y": True}]])
# Add bar chart
fig.add_trace(
go.Bar(x=df[x_col], y=df[y_col], name=y_col),
secondary_y=False,
)
# Add line chart
fig.add_trace(
go.Scatter(
x=df[x_col], y=df[line_col], mode="lines+markers", name=line_col
),
secondary_y=True,
)
fig.update_xaxes(title_text=x_col)
fig.update_yaxes(title_text=y_col, secondary_y=False)
fig.update_yaxes(title_text=line_col, secondary_y=True)
fig.update_layout(title_text=title)
st.plotly_chart(fig, use_container_width=True)
elif chart_type == "bullet":
value_col = config.get("value_column", config.get("y_axis"))
target_col = config.get("target_column")
if not value_col or value_col not in df.columns:
raise ValueError("Required column not found for bullet chart")
value = df[value_col].iloc[0] if len(df) > 0 else 0
target = (
df[target_col].iloc[0]
if target_col and target_col in df.columns
else value * 1.2
)
fig = go.Figure()
fig.add_trace(
go.Indicator(
mode="number+gauge+delta",
value=value,
domain={"x": [0, 1], "y": [0, 1]},
title={"text": title},
delta={"reference": target},
gauge={
"shape": "bullet",
"axis": {"range": [None, target * 1.5]},
"threshold": {
"line": {"color": "red", "width": 2},
"thickness": 0.75,
"value": target,
},
"steps": [
{"range": [0, target * 0.5], "color": "lightgray"},
{"range": [target * 0.5, target], "color": "gray"},
],
"bar": {"color": config.get("color", "#1f77b4")},
},
)
)
st.plotly_chart(fig, use_container_width=True)
elif chart_type == "density_heatmap":
x_col = config.get("x_axis")
y_col = config.get("y_axis")
if (
not x_col
or not y_col
or x_col not in df.columns
or y_col not in df.columns
):
raise ValueError("Required columns not found for density heatmap")
fig = px.density_heatmap(
df,
x=x_col,
y=y_col,
title=title,
marginal_x="histogram",
marginal_y="histogram",
)
st.plotly_chart(fig, use_container_width=True)
elif chart_type == "stacked_area":
x_col = config.get("x_axis")
color_col = config.get("color_column")
y_col = config.get("y_axis")
if (
not x_col
or not y_col
or x_col not in df.columns
or y_col not in df.columns
):
raise ValueError(
"Required columns not found for stacked area chart"
)
fig = px.area(
df,
x=x_col,
y=y_col,
color=color_col if color_col and color_col in df.columns else None,
title=title,
)
st.plotly_chart(fig, use_container_width=True)
elif chart_type == "radar":
# For radar charts, we need multiple numeric columns
categories_col = config.get("categories_column", config.get("x_axis"))
values_cols = config.get("values_columns", [])
Armaan Khan
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