Upload 2 files

main.py

@@ -18,7 +18,7 @@ import subprocess
 import platform
 import docx
 from reportlab.lib.pagesizes import letter
-from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle, Image as RLImage, PageBreak
+from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle, Image as RLImage
 from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
 from reportlab.lib import colors
 from reportlab.lib.units import inch
@@ -501,10 +501,7 @@ async def generate_report(analysis_id: str, include_consultation: bool = True):
         doc.add_paragraph(f"Primary Diagnosis: {analysis_data['highest_probability_class']}")
         doc.add_paragraph(f"Severity Index: {analysis_data['severity_index']}/100")
         
-        # Add a page break before the detailed classification
-        doc.add_page_break()
-        
-        # Detailed classification table
+        # Add detailed classification table
         doc.add_heading('Detailed Classification', level=2)
         table = doc.add_table(rows=1, cols=4)
         table.style = 'Table Grid'
@@ -560,10 +557,10 @@ async def generate_report(analysis_id: str, include_consultation: bool = True):
         docx_path = os.path.join(temp_dir, f"report_{analysis_id}.docx")
         doc.save(docx_path)
         
+        # Now generate PDF directly using ReportLab
         try:
-            # Now generate PDF directly using ReportLab
             from reportlab.lib.pagesizes import letter
-            from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle, Image as RLImage, PageBreak
+            from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle, Image as RLImage
             from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
             from reportlab.lib import colors
             from reportlab.lib.units import inch
@@ -572,200 +569,208 @@ async def generate_report(analysis_id: str, include_consultation: bool = True):
             
             # Create PDF file
             pdf_path = os.path.join(temp_dir, f"report_{analysis_id}.pdf")
+            pdf_doc = SimpleDocTemplate(pdf_path, pagesize=letter)
+            styles = getSampleStyleSheet()
             
-            try:
-                # Create the PDF document with proper settings
-                pdf_doc = SimpleDocTemplate(
-                    pdf_path,
-                    pagesize=letter,
-                    rightMargin=72,
-                    leftMargin=72,
-                    topMargin=72,
-                    bottomMargin=72,
-                    title=f"ClarirAI Medical Report - {analysis_id}",
-                    author="ClarirAI",
-                    subject="Diabetic Retinopathy Analysis",
-                    creator="ClarirAI Report Generator"
-                )
-                
-                # Get the styles
-                styles = getSampleStyleSheet()
-                
-                # Create custom styles with explicit fonts to avoid font issues
-                title_style = ParagraphStyle(
-                    'Title',
-                    parent=styles['Title'],
-                    fontName='Helvetica-Bold',
-                    fontSize=16,
-                    spaceAfter=12
-                )
-                heading1_style = ParagraphStyle(
-                    'Heading1',
-                    parent=styles['Heading1'],
-                    fontName='Helvetica-Bold',
-                    fontSize=14,
-                    spaceAfter=10,
-                    spaceBefore=10
-                )
-                heading2_style = ParagraphStyle(
-                    'Heading2',
-                    parent=styles['Heading2'],
-                    fontName='Helvetica-Bold',
-                    fontSize=12,
-                    spaceAfter=8,
-                    spaceBefore=8
-                )
-                normal_style = ParagraphStyle(
-                    'Normal',
-                    parent=styles["Normal"],
-                    fontName='Helvetica',
-                    fontSize=10,
-                    leading=12
-                )
-                
-                # Build PDF content
-                elements = []
-                
-                # Title
-                elements.append(Paragraph('ClarirAI Medical Report', title_style))
-                elements.append(Spacer(1, 0.25*inch))
-                
-                # Metadata
-                elements.append(Paragraph(f"Generated on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}", normal_style))
-                elements.append(Paragraph(f"Analysis ID: {analysis_id}", normal_style))
-                elements.append(Paragraph(f"Original Analysis Date: {analysis_data['timestamp']}", normal_style))
-                elements.append(Spacer(1, 0.25*inch))
+            # Create custom styles
+            title_style = ParagraphStyle(
+                'Title',
+                parent=styles['Title'],
+                fontSize=16,
+                spaceAfter=12
+            )
+            heading1_style = ParagraphStyle(
+                'Heading1',
+                parent=styles['Heading1'],
+                fontSize=14,
+                spaceAfter=10,
+                spaceBefore=10
+            )
+            heading2_style = ParagraphStyle(
+                'Heading2',
+                parent=styles['Heading2'],
+                fontSize=12,
+                spaceAfter=8,
+                spaceBefore=8
+            )
+            normal_style = styles["Normal"]
+            
+            # Build PDF content
+            elements = []
+            
+            # Title
+            elements.append(Paragraph('ClarirAI Medical Report', title_style))
+            elements.append(Spacer(1, 0.25*inch))
+            
+            # Metadata
+            elements.append(Paragraph(f"Generated on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}", normal_style))
+            elements.append(Paragraph(f"Analysis ID: {analysis_id}", normal_style))
+            elements.append(Paragraph(f"Original Analysis Date: {analysis_data['timestamp']}", normal_style))
+            elements.append(Spacer(1, 0.25*inch))
+            
+            # Add the analyzed image if available
+            if image_path and os.path.exists(image_path):
+                elements.append(Paragraph('Analyzed Retinal Image', heading1_style))
+                elements.append(Paragraph('Below is the retinal image that was analyzed:', normal_style))
                 
-                # Add the analyzed image if available
-                if image_path and os.path.exists(image_path):
-                    try:
-                        elements.append(Paragraph('Analyzed Retinal Image', heading1_style))
-                        elements.append(Paragraph('Below is the retinal image that was analyzed:', normal_style))
-                        
-                        # Process the image for ReportLab
-                        img = PILImage.open(image_path)
-                        img_width, img_height = img.size
-                        aspect = img_height / float(img_width)
-                        rl_img_width = 4 * inch
-                        rl_img_height = rl_img_width * aspect
-                        
-                        # Convert PIL Image to ReportLab Image
-                        img_buffer = BytesIO()
-                        img.save(img_buffer, format='JPEG')
-                        img_buffer.seek(0)
-                        rl_img = RLImage(img_buffer, width=rl_img_width, height=rl_img_height)
-                        elements.append(rl_img)
-                        elements.append(Spacer(1, 0.25*inch))
-                    except Exception as img_err:
-                        logger.error(f"Error adding image to PDF: {img_err}")
-                        elements.append(Paragraph('Error loading retinal image', normal_style))
-                        elements.append(Spacer(1, 0.25*inch))
+                # Process the image for ReportLab
+                img = PILImage.open(image_path)
+                img_width, img_height = img.size
+                aspect = img_height / float(img_width)
+                rl_img_width = 4 * inch
+                rl_img_height = rl_img_width * aspect
                 
-                # Diagnosis section
-                elements.append(Paragraph('Diabetic Retinopathy Assessment', heading1_style))
-                elements.append(Paragraph(f"Primary Diagnosis: {analysis_data['highest_probability_class']}", normal_style))
-                elements.append(Paragraph(f"Severity Index: {analysis_data['severity_index']}/100", normal_style))
+                # Convert PIL Image to ReportLab Image
+                img_buffer = BytesIO()
+                img.save(img_buffer, format='JPEG')
+                img_buffer.seek(0)
+                rl_img = RLImage(img_buffer, width=rl_img_width, height=rl_img_height)
+                elements.append(rl_img)
                 elements.append(Spacer(1, 0.25*inch))
+            
+            # Diagnosis section
+            elements.append(Paragraph('Diabetic Retinopathy Assessment', heading1_style))
+            elements.append(Paragraph(f"Primary Diagnosis: {analysis_data['highest_probability_class']}", normal_style))
+            elements.append(Paragraph(f"Severity Index: {analysis_data['severity_index']}/100", normal_style))
+            elements.append(Spacer(1, 0.25*inch))
+            
+            # Detailed classification table
+            elements.append(Paragraph('Detailed Classification', heading2_style))
+            
+            # Create table data
+            table_data = [
+                ['Classification', 'Description', 'Probability', 'Confidence Level']
+            ]
+            
+            for item in analysis_data['detailed_classification']:
+                table_data.append([
+                    item['class'],
+                    item['description'],
+                    f"{item['percentage']}%",
+                    item['confidence_level']
+                ])
+            
+            # Create table
+            table = Table(table_data, colWidths=[1.5*inch, 2.5*inch, 1*inch, 1.5*inch])
+            table.setStyle(TableStyle([
+                ('BACKGROUND', (0, 0), (-1, 0), colors.lightgrey),
+                ('TEXTCOLOR', (0, 0), (-1, 0), colors.black),
+                ('ALIGN', (0, 0), (-1, -1), 'CENTER'),
+                ('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
+                ('BOTTOMPADDING', (0, 0), (-1, 0), 12),
+                ('GRID', (0, 0), (-1, -1), 1, colors.black),
+                ('VALIGN', (0, 0), (-1, -1), 'MIDDLE'),
+            ]))
+            elements.append(table)
+            elements.append(Spacer(1, 0.25*inch))
+            
+            # Add bar chart visualization
+            elements.append(Paragraph('Classification Probability Chart', heading2_style))
+            
+            # Create the drawing with a proper size
+            drawing = Drawing(500, 250)
+            
+            # Create the bar chart
+            chart = VerticalBarChart()
+            chart.x = 50
+            chart.y = 50
+            chart.height = 150
+            chart.width = 350
+            
+            # Extract data for the chart
+            data = []
+            categories = []
+            for item in analysis_data['detailed_classification']:
+                data.append(item['percentage'])
+                categories.append(item['class'])
+            
+            # Set chart data
+            chart.data = [data]
+            chart.categoryAxis.categoryNames = categories
+            chart.categoryAxis.labels.boxAnchor = 'ne'
+            chart.categoryAxis.labels.dx = -8
+            chart.categoryAxis.labels.dy = -2
+            chart.categoryAxis.labels.angle = 30
+            
+            # Set value axis properties
+            chart.valueAxis.valueMin = 0
+            chart.valueAxis.valueMax = 100
+            chart.valueAxis.valueStep = 10
+            
+            # Set bar properties
+            chart.bars[0].fillColor = colors.skyblue
+            chart.bars[0].strokeColor = colors.black
+            chart.bars[0].strokeWidth = 0.5
+            
+            # Add a legend
+            legend = Legend()
+            legend.alignment = 'right'
+            legend.x = 400
+            legend.y = 150
+            legend.colorNamePairs = [(colors.skyblue, 'Probability (%)')]            
+            
+            # Add chart and legend to the drawing
+            drawing.add(chart)
+            drawing.add(legend)
+            
+            # Add the drawing to the PDF
+            elements.append(drawing)
+            elements.append(Spacer(1, 0.25*inch))
+            
+            # Clinical information
+            if clinical_info:
+                elements.append(Paragraph('Clinical Information', heading1_style))
                 
-                # Add a page break before the detailed classification
-                elements.append(PageBreak())
-                
-                # Detailed classification table
-                elements.append(Paragraph('Detailed Classification', heading2_style))
-                
-                # Create table data
-                table_data = [
-                    ['Classification', 'Description', 'Probability', 'Confidence Level']
-                ]
-                
-                for item in analysis_data['detailed_classification']:
-                    table_data.append([
-                        item['class'],
-                        item['description'],
-                        f"{item['percentage']}%",
-                        item['confidence_level']
-                    ])
+                elements.append(Paragraph('Findings', heading2_style))
+                elements.append(Paragraph(clinical_info.get('findings', 'Not available'), normal_style))
+                elements.append(Spacer(1, 0.15*inch))
                 
-                # Create table
-                table = Table(table_data, colWidths=[1.5*inch, 2.5*inch, 1*inch, 1.5*inch])
-                table.setStyle(TableStyle([
-                    ('BACKGROUND', (0, 0), (-1, 0), colors.lightgrey),
-                    ('TEXTCOLOR', (0, 0), (-1, 0), colors.black),
-                    ('ALIGN', (0, 0), (-1, -1), 'CENTER'),
-                    ('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
-                    ('BOTTOMPADDING', (0, 0), (-1, 0), 12),
-                    ('GRID', (0, 0), (-1, -1), 1, colors.black),
-                    ('VALIGN', (0, 0), (-1, -1), 'MIDDLE'),
-                ]))
-                elements.append(table)
-                elements.append(Spacer(1, 0.25*inch))
+                elements.append(Paragraph('Associated Risks', heading2_style))
+                elements.append(Paragraph(clinical_info.get('risks', 'Not available'), normal_style))
+                elements.append(Spacer(1, 0.15*inch))
                 
-                # Add simple text-based classification summary instead of chart
-                elements.append(Paragraph('Classification Probability Summary', heading2_style))
+                elements.append(Paragraph('Recommendations', heading2_style))
+                elements.append(Paragraph(clinical_info.get('recommendations', 'Not available'), normal_style))
+                elements.append(Spacer(1, 0.15*inch))
                 
-                # Create a simple text summary instead of a chart
-                for item in analysis_data['detailed_classification']:
-                    elements.append(Paragraph(
-                        f"{item['class']}: {item['percentage']}% ({item['confidence_level']})", 
-                        normal_style
-                    ))
+                elements.append(Paragraph('Follow-up', heading2_style))
+                elements.append(Paragraph(clinical_info.get('follow_up', 'Not available'), normal_style))
                 elements.append(Spacer(1, 0.25*inch))
-                
-                # Clinical information
-                if clinical_info:
-                    elements.append(Paragraph('Clinical Information', heading1_style))
-                    
-                    elements.append(Paragraph('Findings', heading2_style))
-                    elements.append(Paragraph(clinical_info.get('findings', 'Not available'), normal_style))
-                    elements.append(Spacer(1, 0.15*inch))
-                    
-                    elements.append(Paragraph('Associated Risks', heading2_style))
-                    elements.append(Paragraph(clinical_info.get('risks', 'Not available'), normal_style))
-                    elements.append(Spacer(1, 0.15*inch))
-                    
-                    elements.append(Paragraph('Recommendations', heading2_style))
-                    elements.append(Paragraph(clinical_info.get('recommendations', 'Not available'), normal_style))
-                    elements.append(Spacer(1, 0.15*inch))
-                    
-                    elements.append(Paragraph('Follow-up', heading2_style))
-                    elements.append(Paragraph(clinical_info.get('follow_up', 'Not available'), normal_style))
-                    elements.append(Spacer(1, 0.25*inch))
-                
-                # AI explanation
-                elements.append(Paragraph('AI Analysis Explanation', heading1_style))
-                elements.append(Paragraph(clinical_info.get('explanation', 'No explanation available for this classification.'), normal_style))
+            
+            # AI explanation
+            elements.append(Paragraph('AI Analysis Explanation', heading1_style))
+            elements.append(Paragraph(clinical_info.get('explanation', 'No explanation available for this classification.'), normal_style))
+            elements.append(Spacer(1, 0.25*inch))
+            
+            # AI consultation
+            if consultation_text:
+                elements.append(Paragraph('AI-Generated Medical Consultation', heading1_style))
+                elements.append(Paragraph(consultation_text, normal_style))
                 elements.append(Spacer(1, 0.25*inch))
-                
-                # AI consultation
-                if consultation_text:
-                    elements.append(Paragraph('AI-Generated Medical Consultation', heading1_style))
-                    elements.append(Paragraph(consultation_text, normal_style))
-                    elements.append(Spacer(1, 0.25*inch))
-                
-                # Disclaimer
-                elements.append(Paragraph('Disclaimer', heading1_style))
-                elements.append(Paragraph('This report is generated using artificial intelligence and should not replace professional medical advice. The analysis and recommendations provided are based on automated image processing and AI consultation. Please consult with a qualified healthcare provider for proper diagnosis, treatment, and follow-up care.', normal_style))
-                
-                # Build the PDF
-                pdf_doc.build(elements)
-                
-                # Return the PDF file
-                return FileResponse(
-                    path=pdf_path,
-                    filename=f"ClarirAI_Report_{analysis_id}.pdf",
-                    media_type="application/pdf",
-                    headers={"Content-Disposition": f"attachment; filename=ClarirAI_Report_{analysis_id}.pdf"}
-                )
-            except Exception as e:
-                logger.error(f"Error generating PDF with ReportLab: {e}")
-                # Fall back to DOCX if PDF generation fails
-                return FileResponse(
-                    path=docx_path,
-                    filename=f"ClarirAI_Report_{analysis_id}.docx",
-                    media_type="application/vnd.openxmlformats-officedocument.wordprocessingml.document",
-                    headers={"Content-Disposition": f"attachment; filename=ClarirAI_Report_{analysis_id}.docx"}
-                )
-    
+            
+            # Disclaimer
+            elements.append(Paragraph('Disclaimer', heading1_style))
+            elements.append(Paragraph('This report is generated using artificial intelligence and should not replace professional medical advice. The analysis and recommendations provided are based on automated image processing and AI consultation. Please consult with a qualified healthcare provider for proper diagnosis, treatment, and follow-up care.', normal_style))
+            
+            # Build the PDF
+            pdf_doc.build(elements)
+            
+            # Return the PDF file
+            return FileResponse(
+                path=pdf_path,
+                filename=f"ClarirAI_Report_{analysis_id}.pdf",
+                media_type="application/pdf"
+            )
+            
+        except Exception as e:
+            logger.error(f"Error generating PDF with ReportLab: {e}")
+            # Fall back to DOCX if PDF generation fails
+            return FileResponse(
+                path=docx_path,
+                filename=f"ClarirAI_Report_{analysis_id}.docx",
+                media_type="application/vnd.openxmlformats-officedocument.wordprocessingml.document"
+            )
+            
     except Exception as e:
         logger.error(f"Error generating report: {e}")
         raise HTTPException(status_code=500, detail=f"Error generating report: {str(e)}")

requirements.txt

@@ -8,5 +8,4 @@ openai
 python-docx
 docx
 python-dotenv
-reportlab>=3.6.12
-pypdf2
+reportlab