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from typing import Dict, List, Any, Optional, Tuple, Union
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import io
import base64
import numpy as np
from llama_index.tools import FunctionTool
from pathlib import Path
# Configure matplotlib for non-interactive environments
matplotlib.use('Agg')
class VisualizationTools:
"""Tools for creating visualizations from CSV data."""
def __init__(self, csv_directory: str):
"""Initialize with directory containing CSV files."""
self.csv_directory = csv_directory
self.dataframes = {}
self.tools = self._create_tools()
self.figure_size = (10, 6)
self.dpi = 100
def _load_dataframe(self, filename: str) -> pd.DataFrame:
"""Load a CSV file as DataFrame, with caching."""
if filename not in self.dataframes:
file_path = Path(self.csv_directory) / filename
if not file_path.exists() and not filename.endswith('.csv'):
file_path = Path(self.csv_directory) / f"{filename}.csv"
if file_path.exists():
self.dataframes[filename] = pd.read_csv(file_path)
else:
raise ValueError(f"CSV file not found: {filename}")
return self.dataframes[filename]
def _create_tools(self) -> List[FunctionTool]:
"""Create LlamaIndex function tools for visualizations."""
tools = [
FunctionTool.from_defaults(
name="create_line_chart",
description="Create a line chart from CSV data",
fn=self.create_line_chart
),
FunctionTool.from_defaults(
name="create_bar_chart",
description="Create a bar chart from CSV data",
fn=self.create_bar_chart
),
FunctionTool.from_defaults(
name="create_scatter_plot",
description="Create a scatter plot from CSV data",
fn=self.create_scatter_plot
),
FunctionTool.from_defaults(
name="create_histogram",
description="Create a histogram from CSV data",
fn=self.create_histogram
),
FunctionTool.from_defaults(
name="create_pie_chart",
description="Create a pie chart from CSV data",
fn=self.create_pie_chart
)
]
return tools
def get_tools(self) -> List[FunctionTool]:
"""Get all available visualization tools."""
return self.tools
def _figure_to_base64(self, fig) -> str:
"""Convert matplotlib figure to base64 encoded string."""
buf = io.BytesIO()
fig.savefig(buf, format='png', dpi=self.dpi)
buf.seek(0)
img_str = base64.b64encode(buf.read()).decode('utf-8')
plt.close(fig)
return img_str
# Visualization tool implementations
def create_line_chart(self, filename: str, x_column: str, y_column: str,
title: str = None, limit: int = 50) -> Dict[str, Any]:
"""Create a line chart visualization."""
df = self._load_dataframe(filename)
# Limit data points if needed
if len(df) > limit:
df = df.head(limit)
fig, ax = plt.subplots(figsize=self.figure_size)
# Create line chart
ax.plot(df[x_column], df[y_column], marker='o', linestyle='-')
# Set labels and title
ax.set_xlabel(x_column)
ax.set_ylabel(y_column)
ax.set_title(title or f"{y_column} vs {x_column}")
ax.grid(True)
# Convert to base64
img_str = self._figure_to_base64(fig)
return {
"chart_type": "line",
"x_column": x_column,
"y_column": y_column,
"data_points": len(df),
"image": img_str
}
def create_bar_chart(self, filename: str, x_column: str, y_column: str,
title: str = None, limit: int = 20) -> Dict[str, Any]:
"""Create a bar chart visualization."""
df = self._load_dataframe(filename)
# Limit categories if needed
if len(df) > limit:
df = df.head(limit)
fig, ax = plt.subplots(figsize=self.figure_size)
# Create bar chart
ax.bar(df[x_column], df[y_column])
# Set labels and title
ax.set_xlabel(x_column)
ax.set_ylabel(y_column)
ax.set_title(title or f"{y_column} by {x_column}")
# Rotate x labels if there are many categories
if len(df) > 5:
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
# Convert to base64
img_str = self._figure_to_base64(fig)
return {
"chart_type": "bar",
"x_column": x_column,
"y_column": y_column,
"categories": len(df),
"image": img_str
}
def create_scatter_plot(self, filename: str, x_column: str, y_column: str,
color_column: str = None, title: str = None) -> Dict[str, Any]:
"""Create a scatter plot visualization."""
df = self._load_dataframe(filename)
fig, ax = plt.subplots(figsize=self.figure_size)
# Create scatter plot
if color_column and color_column in df.columns:
scatter = ax.scatter(df[x_column], df[y_column], c=df[color_column], cmap='viridis', alpha=0.7)
plt.colorbar(scatter, ax=ax, label=color_column)
else:
ax.scatter(df[x_column], df[y_column], alpha=0.7)
# Set labels and title
ax.set_xlabel(x_column)
ax.set_ylabel(y_column)
ax.set_title(title or f"{y_column} vs {x_column}")
ax.grid(True, linestyle='--', alpha=0.7)
# Convert to base64
img_str = self._figure_to_base64(fig)
return {
"chart_type": "scatter",
"x_column": x_column,
"y_column": y_column,
"color_column": color_column,
"data_points": len(df),
"image": img_str
}
def create_histogram(self, filename: str, column: str, bins: int = 10,
title: str = None) -> Dict[str, Any]:
"""Create a histogram visualization."""
df = self._load_dataframe(filename)
fig, ax = plt.subplots(figsize=self.figure_size)
# Create histogram
ax.hist(df[column], bins=bins, alpha=0.7, edgecolor='black')
# Set labels and title
ax.set_xlabel(column)
ax.set_ylabel('Frequency')
ax.set_title(title or f"Distribution of {column}")
ax.grid(True, linestyle='--', alpha=0.7)
# Convert to base64
img_str = self._figure_to_base64(fig)
return {
"chart_type": "histogram",
"column": column,
"bins": bins,
"data_points": len(df),
"image": img_str
}
def create_pie_chart(self, filename: str, label_column: str, value_column: str = None,
title: str = None, limit: int = 10) -> Dict[str, Any]:
"""Create a pie chart visualization."""
df = self._load_dataframe(filename)
# If value column not provided, count occurrences of each label
if value_column is None:
data = df[label_column].value_counts().head(limit)
labels = data.index.tolist()
values = data.values.tolist()
else:
# Group by label and sum values
grouped = df.groupby(label_column)[value_column].sum().reset_index()
# Limit to top categories
grouped = grouped.nlargest(limit, value_column)
labels = grouped[label_column].tolist()
values = grouped[value_column].tolist()
fig, ax = plt.subplots(figsize=self.figure_size)
# Create pie chart
ax.pie(values, labels=labels, autopct='%1.1f%%', startangle=90, shadow=True)
ax.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle
# Set title
ax.set_title(title or f"Distribution of {label_column}")
# Convert to base64
img_str = self._figure_to_base64(fig)
return {
"chart_type": "pie",
"label_column": label_column,
"value_column": value_column,
"categories": len(labels),
"image": img_str
}
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