go

pdf_report_generation_helper.py

@@ -6,9 +6,9 @@ import os
 import uuid
 import logging
 from typing import List, Dict, Any
-from pathlib import Path
 from starlette.concurrency import run_in_threadpool
 
+# The Service file defines the Generator class
 from pdf_report_generation_service import Q4ReportGenerator
 
 logger = logging.getLogger(__name__)
@@ -16,7 +16,7 @@ logger = logging.getLogger(__name__)
 def _upload_to_supabase_sync(file_path: str, file_name: str, chat_id: str) -> str:
     """
     Synchronous wrapper for Supabase upload.
-    Import your actual upload function here.
+    Import inside function to avoid potential circular dependencies if supabase_service changes.
     """
     from supabase_service import upload_file_to_supabase
     return upload_file_to_supabase(
@@ -92,24 +92,7 @@ async def generate_and_upload_report(
     confidential: bool = True
 ) -> str:
     """
-    Generates a PDF report from content configuration, uploads it to Supabase,
-    and returns the public URL. Cleans up temporary files afterwards.
-    
-    Args:
-        config_content: List of section dictionaries for the report
-        file_name: Desired filename (without or with .pdf extension)
-        chat_id: Chat ID for Supabase storage organization
-        title: Report title
-        subtitle: Report subtitle
-        author: Report author
-        department: Department name
-        output_dir: Directory for temporary file storage
-        
-    Returns:
-        str: Public URL of the uploaded PDF
-        
-    Raises:
-        Exception: If PDF generation or upload fails
+    Generates a PDF report, uploads it to Supabase, and returns the public URL.
     """
     
     local_file_path = None
@@ -118,11 +101,9 @@ async def generate_and_upload_report(
         # 1. Setup paths
         os.makedirs(output_dir, exist_ok=True)
         
-        # Ensure filename ends with .pdf
         if not file_name.endswith('.pdf'):
             file_name += '.pdf'
         
-        # Create unique filename to prevent collisions
         unique_id = str(uuid.uuid4())[:8]
         local_filename = f"{os.path.splitext(file_name)[0]}_{unique_id}.pdf"
         local_file_path = os.path.join(output_dir, local_filename)
@@ -177,13 +158,11 @@ async def generate_and_upload_report(
         raise Exception(f"Failed to generate and upload report: {str(e)}")
         
     finally:
-        # 5. Cleanup - Always execute, even if there's an error
+        # 5. Cleanup
         try:
-            # Delete local PDF
             if local_file_path:
                 _cleanup_files([local_file_path])
             
-            # Delete downloaded images
             if 'generator' in locals():
                 await run_in_threadpool(
                     _cleanup_downloaded_images,

pdf_report_generation_service.py

@@ -275,13 +275,6 @@ strong {
 def fetch_image(url: str, output_dir: str) -> Optional[str]:
     """
     Download image from URL to persistent file for PDF rendering.
-    
-    Args:
-        url: The URL of the image
-        output_dir: Directory to save downloaded images
-        
-    Returns:
-        File URI string or None if failed
     """
     if not url.startswith(('http://', 'https://')):
         if os.path.exists(url):
@@ -299,7 +292,6 @@ def fetch_image(url: str, output_dir: str) -> Optional[str]:
         response = requests.get(url, headers=headers, timeout=15)
         response.raise_for_status()
         
-        # Determine file extension
         content_type = response.headers.get('content-type', '')
         ext = '.jpg'
         if 'png' in content_type:
@@ -454,206 +446,9 @@ class Q4ReportGenerator:
             
             html_doc.write_pdf(output_path, stylesheets=[css_doc])
             
-            abs_path = os.path.abspath(output_path)
-            file_size = os.path.getsize(output_path) / 1024
-            
-            print(f"\n{'='*70}")
-            print(f"✅ PDF GENERATED SUCCESSFULLY!")
-            print(f"{'='*70}")
-            print(f"📁 Location: {abs_path}")
-            print(f"📊 Size: {file_size:.2f} KB")
-            print(f"🖼️  Images: {len(self.downloaded_images)} embedded")
-            print(f"{'='*70}\n")
-            
+            logger.info(f"PDF Generated: {output_path}")
             return True
             
         except Exception as e:
-            logger.error(f"❌ Error generating report: {e}", exc_info=True)
-            return False
-
-# ---------------------------------------------------------
-# COMPLETE TEST CASE WITH FULL Q4 REPORT
-# ---------------------------------------------------------
-# if __name__ == "__main__":
-    
-#     q4_sections = [
-#         {
-#             "content": """
-# # Executive Summary
-
-# This report presents the comprehensive Q4 2025 performance analysis for our customer analytics division. The quarter demonstrated strong growth across key metrics with notable improvements in customer engagement and retention.
-
-# <div class="key-metric-box">
-
-# ### Key Q4 Highlights
-
-# - **Revenue Growth**: 23.4% increase over Q3 2025
-# - **Customer Acquisition**: 8,547 new customers (+18% QoQ)
-# - **Retention Rate**: 94.2% (industry-leading performance)
-# - **Data Quality Score**: 98.7% completeness
-
-# </div>
-
-# > "Q4 2025 marks our strongest quarter to date, with record-breaking performance across all major KPIs and successful implementation of our new customer engagement strategy."
-
-# ## Quarter at a Glance
-
-# The analytics team processed over 2.3 million customer interactions, generating actionable insights that drove strategic decisions across the organization.
-#             """
-#         },
-#         {
-#             "content": """
-# # Performance Metrics
-
-# ## Overall Q4 Performance
-
-# | Metric | Q4 2025 | Q3 2025 | Change | Target |
-# |--------|---------|---------|--------|--------|
-# | Total Revenue | $4.2M | $3.4M | +23.4% | $4.0M |
-# | New Customers | 8,547 | 7,243 | +18.0% | 8,000 |
-# | Active Users | 45,892 | 42,156 | +8.9% | 44,000 |
-# | Retention Rate | 94.2% | 92.8% | +1.4pp | 93.0% |
-# | Avg. Order Value | $127 | $118 | +7.6% | $120 |
-
-# <div class="highlight-box">
-
-# **Performance Assessment**: All key metrics exceeded quarterly targets, demonstrating the effectiveness of our data-driven strategy and customer-focused initiatives.
-
-# </div>
-
-# ## Channel Performance Analysis
-
-# The following visualization shows the distribution of customer orders across different channels in Q4 2025.
-#             """,
-#             "images": [
-#                 {
-#                     "url": "https://quickchart.io/chart?c={type:'bar',data:{labels:['Mobile App','Desktop Web','Mobile Web'],datasets:[{label:'Q4 Orders (%)',data:[52,35,13],backgroundColor:['rgba(0,51,102,0.8)','rgba(0,85,165,0.8)','rgba(52,152,219,0.8)']}]},options:{title:{display:true,text:'Q4 2025 Channel Distribution'},scales:{y:{beginAtZero:true,max:60}},plugins:{legend:{display:true}}}}",
-#                     "caption": "Figure 1: Customer Orders by Channel - Q4 2025"
-#                 }
-#             ],
-#             "page_break": True
-#         },
-#         {
-#             "content": """
-# # Customer Analytics Deep Dive
-
-# ## Demographic Insights
-
-# Our Q4 analysis reveals significant shifts in customer demographics and behavior patterns.
-
-# ### Age Distribution
-
-# | Age Group | Customers | % of Total | Avg. Spend | Growth (QoQ) |
-# |-----------|-----------|------------|------------|--------------|
-# | 18-24 | 8,234 | 17.9% | $89 | +22% |
-# | 25-34 | 15,678 | 34.2% | $142 | +15% |
-# | 35-44 | 12,456 | 27.1% | $156 | +11% |
-# | 45-54 | 6,789 | 14.8% | $178 | +8% |
-# | 55+ | 2,735 | 6.0% | $203 | +5% |
-
-# ## Revenue Trend Analysis
-
-# Below is the revenue trend across Q4 months showing consistent growth:
-#             """,
-#             "images": [
-#                 {
-#                     "url": "https://quickchart.io/chart?c={type:'line',data:{labels:['October','November','December'],datasets:[{label:'Revenue ($M)',data:[1.2,1.5,1.5],borderColor:'rgb(0,51,102)',backgroundColor:'rgba(0,51,102,0.1)',fill:true}]},options:{title:{display:true,text:'Q4 2025 Monthly Revenue Trend'},scales:{y:{beginAtZero:true,max:2}}}}",
-#                     "caption": "Figure 2: Monthly Revenue Trend - Q4 2025"
-#                 }
-#             ],
-#             "page_break": True
-#         },
-#         {
-#             "content": """
-# # Recommendations & 2026 Strategy
-
-# ## Key Recommendations
-
-# ### Immediate Actions (Q1 2026)
-
-# 1. **Expand Mobile Capabilities**
-#    - Invest in mobile app feature development
-#    - Optimize mobile checkout experience
-#    - Target: 60% mobile order share by Q2 2026
-
-# 2. **Scale Personalization Engine**
-#    - Deploy to all customer touchpoints
-#    - Expected: 35% increase in engagement
-
-# 3. **Enhance Data Infrastructure**
-#    - Implement advanced data governance framework
-#    - Prepare for 3x data volume growth
-
-# <div class="executive-summary">
-
-# **2026 Vision**: Position our analytics capabilities as a competitive differentiator through advanced AI, real-time insights, and predictive decision-making.
-
-# </div>
-
-# ## Investment Requirements
-
-# | Initiative | Investment | Expected ROI | Timeline |
-# |------------|------------|--------------|----------|
-# | Mobile Expansion | $450K | 280% | Q1-Q2 2026 |
-# | AI/ML Platform | $780K | 420% | Q1-Q4 2026 |
-# | Infrastructure | $320K | 195% | Q1-Q3 2026 |
-# | **Total** | **$1.55M** | **310%** | **2026** |
-#             """
-#         },
-#         {
-#             "content": f"""
-# # Conclusion
-
-# ## Quarter Summary
-
-# Q4 2025 represents a milestone quarter for our analytics organization. We exceeded all primary objectives, delivered exceptional data quality, and laid the foundation for continued growth.
-
-# ### Key Achievements
-
-# - ✓ **23.4% revenue growth** exceeding target by 5%
-# - ✓ **8,547 new customers** acquired, 7% above goal
-# - ✓ **94.2% retention rate**, best in company history
-# - ✓ **98.7% data quality score**, industry-leading
-
-# <div class="highlight-box">
-
-# **Strategic Position**: Our analytics capabilities represent a significant competitive advantage for 2026 and beyond.
-
-# </div>
-
-# ---
-
-# **Prepared by**: Data Analytics Team  
-# **Report Date**: {datetime.now().strftime("%B %d, %Y")}  
-# **Classification**: Confidential - Internal Use Only
-#             """
-#         }
-#     ]
-    
-#     # Generate the complete Q4 report
-#     generator = Q4ReportGenerator(
-#         title="Q4 2025 Analytics Performance Report",
-#         subtitle="Fourth Quarter Results & 2026 Strategy",
-#         author="Senior Data Analyst",
-#         department="Data Analytics Division",
-#         confidential=True
-#     )
-    
-#     success = generator.generate(
-#         sections=q4_sections,
-#         filename="Q4_2025_Complete_Report.pdf",
-#         output_dir="./output",
-#         cover_page=True
-#     )
-    
-#     if success:
-#         print("\n📋 Report Features:")
-#         print("  ✓ Executive cover page")
-#         print("  ✓ Performance metrics with tables")
-#         print("  ✓ Embedded charts and visualizations")
-#         print("  ✓ Customer analytics insights")
-#         print("  ✓ Strategic recommendations")
-#         print("  ✓ Professional styling and formatting")
-#         print("\n💡 Check the 'output' folder for your PDF!")
-#     else:
-#         print("\n❌ Report generation failed. Check logs for details.")
+            logger.error(f"Error generating report: {e}", exc_info=True)
+            return False