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# Contains parse_embedded_chart_data() and create_chart_from_data() functions
import re
import io
import os
import sys
import base64
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from typing import List, Dict, Any, Optional
# Add the parent directory to sys.path to import from models.py
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
from models import ExportSection
def parse_embedded_chart_data(sections: List[ExportSection]) -> List[Dict[str, Any]]:
"""Parse chart data embedded in section content using CHARTDATA markers"""
charts = []
for section in sections:
if section.content:
# Look for chart data markers - handle both formats
chart_matches = re.findall(r'<!--CHART_DATA_START-->(.*?)<!--CHART_DATA_END-->', section.content, re.DOTALL)
# Also check for alternative markers as fallback
alt_chart_matches = re.findall(r'<!--CHARTDATASTART-->(.*?)<!--CHARTDATAEND-->', section.content, re.DOTALL)
chart_matches.extend(alt_chart_matches)
# logging.info(f"Section '{section.title}': Found {len(chart_matches)} chart data markers")
for chart_data in chart_matches:
try:
# Parse the chart data array
chart_array = eval(chart_data.strip())
# logging.info(f"Parsed chart array with {len(chart_array)} items")
# logging.info(f"Chart array type: {type(chart_array)}")
# Each chart is an array with: [Section, ChartType, Title, Period, Data]
for chart_item in chart_array:
if len(chart_item) >= 5:
section_name = chart_item[0]
chart_type = chart_item[1] # DC=Doughnut, BG=Bar Graph, etc.
chart_title = chart_item[2]
period = chart_item[3]
data = chart_item[4]
chart_info = {
"section": section_name,
"type": chart_type,
"title": chart_title,
"period": period,
"data": data,
"source_section": section.title
}
charts.append(chart_info)
# logging.info(f"Added chart: {chart_type} - {chart_title} for section {section_name}")
else:
# logging.warning(f"Chart item has insufficient data: {len(chart_item)} items, expected 5")
pass
except Exception as e:
# logging.error(f"Failed to parse chart data for section '{section.title}': {str(e)}")
continue
# Additional debugging: Check for any chart-related content
if not chart_matches:
# Look for any chart-related markers or text
chart_indicators = re.findall(r'<!--.*?CHART.*?-->', section.content, re.DOTALL)
if chart_indicators:
# logging.info(f"Section '{section.title}': Found chart indicators: {chart_indicators}")
pass
# Check for general chart text
if 'CHART' in section.content.upper() or 'chart' in section.content.lower():
# logging.info(f"Section '{section.title}': Contains chart-related text")
pass
# logging.info(f"Total charts parsed: {len(charts)}")
return charts
def create_enhanced_chart_style(chart_type, data, title, business_idea):
"""Create charts with enhanced professional styling"""
# Set modern style
plt.style.use('default')
# Design palette from image
colors = ['#6A2EAB', '#5A2E9B', '#C0C0C0', '#FF8C00', '#FFD700', '#696969', '#FF4500']
if chart_type == "DC": # Doughnut Chart
fig, ax = plt.subplots(figsize=(14, 10))
# Extract labels and values from data
labels = [item[0] for item in data]
values = [item[1] for item in data]
# Create doughnut chart with enhanced styling
wedges, texts, autotexts = ax.pie(values, labels=labels, colors=colors[:len(values)],
autopct='%1.1f%%', startangle=90,
wedgeprops=dict(width=0.6, edgecolor='white', linewidth=2),
textprops={'fontsize': 18})
# Enhanced title styling
ax.set_title(title, fontsize=24, fontweight='bold', pad=20, color='#374151')
# Add legend with enhanced styling
legend = ax.legend(wedges, labels, title="Categories",
loc="center left", bbox_to_anchor=(1, 0, 0.5, 1),
fontsize=16, title_fontsize=18)
legend.get_title().set_color('#FF6B6B')
# Add business name watermark
fig.text(0.5, 0.02, f"Generated for: {business_idea}",
ha='center', va='bottom', fontsize=8, color='#9CA3AF', style='italic')
plt.tight_layout()
return fig
elif chart_type == "BG": # Bar Graph
fig, ax = plt.subplots(figsize=(16, 10))
# Extract labels and values
labels = [item[0] for item in data]
values = [item[1] for item in data]
# Create bar chart with enhanced styling
bars = ax.bar(labels, values, color=colors[:len(values)],
edgecolor='white', linewidth=1, alpha=0.8)
# Add value labels on bars
for bar, value in zip(bars, values):
height = bar.get_height()
ax.text(bar.get_x() + bar.get_width()/2., height + max(values)*0.01,
f'{value}', ha='center', va='bottom', fontweight='bold', fontsize=18)
# Enhanced styling
ax.set_title(title, fontsize=24, fontweight='bold', pad=20, color='#374151')
ax.set_xlabel('Categories', fontsize=18, color='#6B7280')
ax.set_ylabel('Values', fontsize=18, color='#6B7280')
# Rotate x-axis labels for better readability
plt.setp(ax.get_xticklabels(), rotation=45, ha='right', fontsize=16)
plt.setp(ax.get_yticklabels(), fontsize=16)
# Add grid for better readability
ax.yaxis.grid(True, alpha=0.3)
ax.set_axisbelow(True)
# Add business name watermark
fig.text(0.5, 0.02, f"Generated for: {business_idea}",
ha='center', va='bottom', fontsize=8, color='#9CA3AF', style='italic')
plt.tight_layout()
return fig
elif chart_type == "LG": # Line Graph
fig, ax = plt.subplots(figsize=(16, 10))
# Extract data
x_values = [item[0] for item in data]
y_values = [item[1] for item in data]
# Create line chart with enhanced styling
ax.plot(x_values, y_values, color='#FF6B6B', linewidth=3, marker='o',
markersize=8, markerfacecolor='white', markeredgecolor='#FF6B6B')
# Fill area under line
ax.fill_between(x_values, y_values, alpha=0.3, color='#4ECDC4')
# Enhanced styling
ax.set_title(title, fontsize=24, fontweight='bold', pad=20, color='#374151')
ax.set_xlabel('Time Period', fontsize=18, color='#6B7280')
ax.set_ylabel('Values', fontsize=18, color='#6B7280')
# Add grid
ax.grid(True, alpha=0.3)
ax.set_axisbelow(True)
# Increase tick label font sizes
plt.setp(ax.get_xticklabels(), fontsize=16)
plt.setp(ax.get_yticklabels(), fontsize=16)
# Add business name watermark
fig.text(0.5, 0.02, f"Generated for: {business_idea}",
ha='center', va='bottom', fontsize=8, color='#9CA3AF', style='italic')
plt.tight_layout()
return fig
elif chart_type == "PG": # Pie Graph
fig, ax = plt.subplots(figsize=(14, 10))
# Extract labels and values
labels = [item[0] for item in data]
values = [item[1] for item in data]
# Create pie chart with enhanced styling
wedges, texts, autotexts = ax.pie(values, labels=labels, colors=colors[:len(values)],
autopct='%1.1f%%', startangle=90,
wedgeprops=dict(edgecolor='white', linewidth=2),
textprops={'fontsize': 18})
# Enhanced title styling
ax.set_title(title, fontsize=24, fontweight='bold', pad=20, color='#374151')
# Add business name watermark
fig.text(0.5, 0.02, f"Generated for: {business_idea}",
ha='center', va='bottom', fontsize=8, color='#9CA3AF', style='italic')
plt.tight_layout()
return fig
return None
def create_chart_from_data(chart_info: Dict[str, Any], business_idea: str) -> bytes:
"""Create a matplotlib chart from parsed chart data with enhanced styling"""
try:
chart_type = chart_info["type"]
chart_title = chart_info["title"]
data = chart_info["data"]
# logging.info(f"Creating chart: {chart_type} - {chart_title} with {len(data)} data points")
# Use enhanced styling function
fig = create_enhanced_chart_style(chart_type, data, chart_title, business_idea)
if fig is None:
# Fallback to original method if enhanced styling fails
if chart_type == "DC": # Doughnut Chart
fig, ax = plt.subplots(figsize=(14, 10)) # Larger size for better visibility
# Extract labels and values from data
labels = [item[0] for item in data]
values = [item[1] for item in data]
# High contrast color scheme
colors = ['#6A2EAB', '#5A2E9B', '#C0C0C0', '#FF8C00', '#FFD700', '#696969', '#FF4500']
# Create doughnut chart
wedges, texts, autotexts = ax.pie(values, labels=labels, colors=colors[:len(values)],
autopct='%1.1f%%', startangle=90,
wedgeprops=dict(width=0.6, edgecolor='white', linewidth=2),
textprops={'fontsize': 18})
# Enhanced title
ax.set_title(chart_title, fontsize=22, fontweight='bold', pad=15, color='#374151')
# Add legend
ax.legend(wedges, labels, title="Categories", loc="center left", bbox_to_anchor=(1, 0, 0.5, 1), fontsize=16)
plt.tight_layout()
elif chart_type == "BG": # Bar Graph
fig, ax = plt.subplots(figsize=(16, 10))
# Extract labels and values
labels = [item[0] for item in data]
values = [item[1] for item in data]
# High contrast color scheme
colors = ['#6A2EAB', '#5A2E9B', '#C0C0C0', '#FF8C00', '#FFD700', '#696969', '#FF4500']
# Create bar chart
bars = ax.bar(labels, values, color=colors[:len(values)],
edgecolor='white', linewidth=1, alpha=0.8)
# Add value labels on bars
for bar, value in zip(bars, values):
height = bar.get_height()
ax.text(bar.get_x() + bar.get_width()/2., height + max(values)*0.01,
f'{value}', ha='center', va='bottom', fontweight='bold', fontsize=18)
ax.set_title(chart_title, fontsize=22, fontweight='bold', pad=15, color='#374151')
ax.set_xlabel('Categories', fontsize=18, color='#6B7280')
ax.set_ylabel('Values', fontsize=18, color='#6B7280')
# Rotate x-axis labels
plt.setp(ax.get_xticklabels(), rotation=45, ha='right', fontsize=16)
plt.setp(ax.get_yticklabels(), fontsize=16)
# Add grid
ax.yaxis.grid(True, alpha=0.3)
ax.set_axisbelow(True)
plt.tight_layout()
elif chart_type == "LG": # Line Graph
fig, ax = plt.subplots(figsize=(16, 10))
# Extract data
x_values = [item[0] for item in data]
y_values = [item[1] for item in data]
# Create line chart
ax.plot(x_values, y_values, color='#FF6B6B', linewidth=3, marker='o',
markersize=8, markerfacecolor='white', markeredgecolor='#FF6B6B')
# Fill area under line
ax.fill_between(x_values, y_values, alpha=0.3, color='#4ECDC4')
ax.set_title(chart_title, fontsize=22, fontweight='bold', pad=15, color='#374151')
ax.set_xlabel('Time Period', fontsize=18, color='#6B7280')
ax.set_ylabel('Values', fontsize=18, color='#6B7280')
# Add grid
ax.grid(True, alpha=0.3)
ax.set_axisbelow(True)
# Increase tick label font sizes
plt.setp(ax.get_xticklabels(), fontsize=16)
plt.setp(ax.get_yticklabels(), fontsize=16)
plt.tight_layout()
elif chart_type == "PG": # Pie Graph
fig, ax = plt.subplots(figsize=(14, 10))
# Extract labels and values
labels = [item[0] for item in data]
values = [item[1] for item in data]
# High contrast color scheme
colors = ['#6A2EAB', '#5A2E9B', '#C0C0C0', '#FF8C00', '#FFD700', '#696969', '#FF4500']
# Create pie chart
wedges, texts, autotexts = ax.pie(values, labels=labels, colors=colors[:len(values)],
autopct='%1.1f%%', startangle=90,
wedgeprops=dict(edgecolor='white', linewidth=2),
textprops={'fontsize': 18})
ax.set_title(chart_title, fontsize=22, fontweight='bold', pad=15, color='#374151')
plt.tight_layout()
else:
# logging.warning(f"Unknown chart type: {chart_type}")
return None
# Save to bytes
buffer = io.BytesIO()
fig.savefig(buffer, format='png', dpi=300, bbox_inches='tight',
facecolor='white', edgecolor='none')
buffer.seek(0)
chart_bytes = buffer.getvalue()
buffer.close()
# Close the figure to free memory
plt.close(fig)
return chart_bytes
except Exception as e:
# logging.error(f"Failed to create chart: {str(e)}")
return None
def fix_base64_padding(base64_string: str) -> str:
"""Fix common base64 padding issues"""
# Remove any whitespace or newlines
base64_string = base64_string.strip()
# Add padding if needed
missing_padding = len(base64_string) % 4
if missing_padding:
base64_string += '=' * (4 - missing_padding)
return base64_string
def robust_base64_decode(base64_string: str) -> Optional[bytes]:
"""Robustly decode base64 string with error handling and padding correction"""
try:
# First try direct decoding
return base64.b64decode(base64_string)
except Exception as e1:
try:
# Try with padding correction
fixed_string = fix_base64_padding(base64_string)
return base64.b64decode(fixed_string)
except Exception as e2:
try:
# Try with URL-safe base64
return base64.urlsafe_b64decode(fix_base64_padding(base64_string))
except Exception as e3:
try:
# Try with standard base64 ignoring padding
return base64.b64decode(base64_string + '==', validate=False)
except Exception as e4:
return None |