Update process_interview.py

process_interview.py

@@ -594,413 +594,274 @@ def calculate_acceptance_probability(analysis_data: Dict) -> float:
     return float(f"{acceptance_probability * 100:.2f}")  # Return as percentage
 
 
+
+
+
 def generate_report(analysis_data: Dict) -> str:
     try:
         voice = analysis_data.get('voice_analysis', {})
         voice_interpretation = generate_voice_interpretation(voice)
-
-        # Extract key metrics for the prompt
-        total_duration = analysis_data['text_analysis']['total_duration']
-        speaker_turns = analysis_data['text_analysis']['speaker_turns']
-        speakers = ', '.join(analysis_data['speakers'])
-        
-        # Get acceptance probability with enhanced interpretation
-        acceptance_prob = analysis_data.get('acceptance_probability', None)
-        acceptance_line = ""
-        if acceptance_prob is not None:
-            acceptance_line = f"\n**Estimated Acceptance Probability: {acceptance_prob:.2f}%**\n"
-            if acceptance_prob >= 85:
-                acceptance_line += "This candidate demonstrates exceptional qualifications and interview performance."
-            elif acceptance_prob >= 70:
-                acceptance_line += "This is a strong candidate with high potential for the role."
-            elif acceptance_prob >= 50:
-                acceptance_line += "This candidate shows promise but would benefit from targeted development."
-            else:
-                acceptance_line += "This candidate may not be the ideal fit based on current evaluation."
-
-        # Prepare structured prompt for Gemini
+        interviewee_responses = [f"- {u['text']}" for u in analysis_data['transcript'] if u['role'] == 'Interviewee'][:5]
+        acceptance_prob = analysis_data.get('acceptance_probability', 50.0)
+        acceptance_line = f"\n**Suitability Score: {acceptance_prob:.2f}%**\n"
+        if acceptance_prob >= 80:
+            acceptance_line += "HR Verdict: Outstanding candidate, recommended for immediate advancement."
+        elif acceptance_prob >= 60:
+            acceptance_line += "HR Verdict: Strong candidate, suitable for further evaluation."
+        elif acceptance_prob >= 40:
+            acceptance_line += "HR Verdict: Moderate potential, needs additional assessment."
+        else:
+            acceptance_line += "HR Verdict: Limited fit, significant improvement required."
         prompt = f"""
-        As EvalBot, an AI interview analysis system, generate a comprehensive yet concise professional interview report.
-        Focus on delivering actionable insights in a structured format. Use clear section headings and bullet points.
-
+        You are EvalBot, a senior HR consultant delivering a concise, professional interview analysis report. Use clear headings, bullet points ('-'), and avoid redundancy. Ensure text is clean and free of special characters that could break formatting.
         {acceptance_line}
-
         **1. Executive Summary**
-        Provide a succinct overview highlighting:
-        - Interview duration: {total_duration:.1f} seconds
-        - Speaker interactions: {speaker_turns} turns among {speakers}
-        - Key themes and overall impression
-
-        **2. Voice Analysis Highlights**
+        - Summarize performance, key metrics, and hiring potential.
+        - Duration: {analysis_data['text_analysis']['total_duration']:.2f} seconds
+        - Speaker Turns: {analysis_data['text_analysis']['speaker_turns']}
+        - Participants: {', '.join(sorted(set(u['speaker'] for u in analysis_data['transcript'])))}
+        **2. Communication and Vocal Dynamics**
+        - Evaluate vocal delivery (rate, fluency, confidence).
+        - Provide HR insights on workplace alignment.
         {voice_interpretation}
-
-        **3. Content Evaluation**
-        Analyze responses for:
-        - Technical competence and knowledge depth
-        - Problem-solving approach
-        - Communication effectiveness
-        - Cultural/organizational fit
-
-        **4. Development Opportunities**
-        Identify 3-5 specific areas for improvement with concrete examples.
-
-        **5. Actionable Recommendations**
-        Provide tailored suggestions in these categories:
-        - Immediate onboarding focus (if hired)
-        - Skill development priorities
-        - Interview technique refinement
-        - Professional growth opportunities
-
-        **Formatting Guidelines:**
-        - Use concise, professional language
-        - Prioritize quantifiable observations
-        - Maintain neutral, constructive tone
-        - Limit report to 1-2 pages equivalent
+        **3. Competency and Content**
+        - Assess leadership, problem-solving, communication, adaptability.
+        - List strengths and growth areas separately with examples.
+        - Sample responses:
+        {chr(10).join(interviewee_responses)}
+        **4. Role Fit and Potential**
+        - Analyze cultural fit, role readiness, and growth potential.
+        **5. Recommendations**
+        - Provide prioritized strategies for growth (communication, technical skills, presence).
+        - Suggest next steps for hiring managers (advance, train, assess).
         """
-
-        # Generate the report
         response = gemini_model.generate_content(prompt)
-        return response.text
-        
+        return re.sub(r'[^\x00-\x7F]+', '', response.text)  # Sanitize non-ASCII characters
     except Exception as e:
         logger.error(f"Report generation failed: {str(e)}")
         return f"Error generating report: {str(e)}"
 
-def create_pdf_report(analysis_data: Dict, output_path: str, gemini_report_text: str):
+def create_pdf_report(analysis_data: Dict, output_path: str, gemini_report_text: str) -> bool:
     try:
-        doc = SimpleDocTemplate(output_path, pagesize=letter)
+        doc = SimpleDocTemplate(output_path, pagesize=letter,
+                                rightMargin=0.75*inch, leftMargin=0.75*inch,
+                                topMargin=1*inch, bottomMargin=1*inch)
         styles = getSampleStyleSheet()
-
-        # Define enhanced custom styles
-        h1 = ParagraphStyle(
-            name='Heading1',
-            parent=styles['h1'],
-            fontSize=18,
-            spaceAfter=12,
-            alignment=TA_CENTER,
-            textColor=colors.HexColor('#2E5984'),
-            fontName='Helvetica-Bold'
-        )
+        h1 = ParagraphStyle(name='Heading1', fontSize=20, leading=24, spaceAfter=18, alignment=1, textColor=colors.HexColor('#003087'), fontName='Helvetica-Bold')
+        h2 = ParagraphStyle(name='Heading2', fontSize=14, leading=16, spaceBefore=12, spaceAfter=8, textColor=colors.HexColor('#0050BC'), fontName='Helvetica-Bold')
+        h3 = ParagraphStyle(name='Heading3', fontSize=10, leading=12, spaceBefore=8, spaceAfter=6, textColor=colors.HexColor('#3F7CFF'), fontName='Helvetica')
+        body_text = ParagraphStyle(name='BodyText', fontSize=9, leading=12, spaceAfter=6, fontName='Helvetica', textColor=colors.HexColor('#333333'))
+        bullet_style = ParagraphStyle(name='Bullet', parent=body_text, leftIndent=18, bulletIndent=8, fontName='Helvetica', bulletFontName='Helvetica', bulletFontSize=9)
         
-        h2 = ParagraphStyle(
-            name='Heading2',
-            parent=styles['h2'],
-            fontSize=14,
-            spaceBefore=14,
-            spaceAfter=8,
-            textColor=colors.HexColor('#1E3F66'),
-            fontName='Helvetica-Bold'
-        )
-        
-        h3 = ParagraphStyle(
-            name='Heading3',
-            parent=styles['h3'],
-            fontSize=12,
-            spaceBefore=10,
-            spaceAfter=6,
-            textColor=colors.HexColor('#0066CC'),
-            fontName='Helvetica-Bold'
-        )
-        
-        body_text = ParagraphStyle(
-            name='BodyText',
-            parent=styles['Normal'],
-            fontSize=10,
-            leading=14,
-            spaceAfter=6,
-            textColor=colors.HexColor('#333333')
-        )
-        
-        bullet_style = ParagraphStyle(
-            name='Bullet',
-            parent=styles['Normal'],
-            fontSize=10,
-            leading=14,
-            leftIndent=18,
-            bulletIndent=9,
-            textColor=colors.HexColor('#444444'),
-            bulletFontName='Helvetica-Bold'
-        )
-
         story = []
 
-        # --- Title Section ---
-        title_table = Table([
-            [Paragraph("<b>EvalBot Interview Analysis Report</b>", h1)]
-        ], colWidths=[doc.width])
-        
-        title_table.setStyle(TableStyle([
-            ('BACKGROUND', (0,0), (-1,-1), colors.HexColor('#F0F8FF')),
-            ('BOX', (0,0), (-1,-1), 1, colors.HexColor('#B0C4DE')),
+        def header_footer(canvas, doc):
+            canvas.saveState()
+            canvas.setFont('Helvetica', 8)
+            canvas.setFillColor(colors.HexColor('#666666'))
+            canvas.drawString(doc.leftMargin, 0.5*inch, f"Page {doc.page} | EvalBot HR Interview Report | Confidential")
+            canvas.setStrokeColor(colors.HexColor('#0050BC'))
+            canvas.setLineWidth(0.8)
+            canvas.line(doc.leftMargin, doc.height + 0.9*inch, doc.width + doc.leftMargin, doc.height + 0.9*inch)
+            canvas.setFont('Helvetica-Bold', 9)
+            canvas.drawString(doc.leftMargin, doc.height + 0.95*inch, "Candidate Interview Analysis")
+            canvas.drawRightString(doc.width + doc.leftMargin, doc.height + 0.95*inch, time.strftime('%B %d, %Y'))
+            canvas.restoreState()
+
+        # Title Page
+        story.append(Paragraph("Candidate Interview Analysis", h1))
+        story.append(Paragraph(f"Generated: {time.strftime('%B %d, %Y')}", ParagraphStyle(name='Date', alignment=1, fontSize=9, textColor=colors.HexColor('#666666'), fontName='Helvetica')))
+        story.append(Spacer(1, 0.4*inch))
+        acceptance_prob = analysis_data.get('acceptance_probability', 50.0)
+        story.append(Paragraph("Hiring Suitability Snapshot", h2))
+        prob_color = colors.HexColor('#2E7D32') if acceptance_prob >= 80 else (colors.HexColor('#F57C00') if acceptance_prob >= 60 else colors.HexColor('#D32F2F'))
+        story.append(Paragraph(f"Suitability Score: <font size=15 color='{prob_color.hexval()}'><b>{acceptance_prob:.2f}%</b></font>",
+                             ParagraphStyle(name='Prob', fontSize=11, spaceAfter=10, alignment=1, fontName='Helvetica-Bold')))
+        if acceptance_prob >= 80:
+            story.append(Paragraph("<b>HR Verdict:</b> Outstanding candidate, recommended for immediate advancement.", body_text))
+        elif acceptance_prob >= 60:
+            story.append(Paragraph("<b>HR Verdict:</b> Strong candidate, suitable for further evaluation.", body_text))
+        elif acceptance_prob >= 40:
+            story.append(Paragraph("<b>HR Verdict:</b> Moderate potential, needs additional assessment.", body_text))
+        else:
+            story.append(Paragraph("<b>HR Verdict:</b> Limited fit, significant improvement required.", body_text))
+        story.append(Spacer(1, 0.3*inch))
+        table_data = [
+            ['Metric', 'Value'],
+            ['Interview Duration', f"{analysis_data['text_analysis']['total_duration']:.2f} seconds"],
+            ['Speaker Turns', f"{analysis_data['text_analysis']['speaker_turns']}"],
+            ['Participants', ', '.join(sorted(set(u['speaker'] for u in analysis_data['transcript'])))],
+        ]
+        table = Table(table_data, colWidths=[2.3*inch, 3.7*inch])
+        table.setStyle(TableStyle([
+            ('BACKGROUND', (0,0), (-1,0), colors.HexColor('#0050BC')),
+            ('TEXTCOLOR', (0,0), (-1,0), colors.white),
+            ('ALIGN', (0,0), (-1,-1), 'LEFT'),
             ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),
-            ('ALIGN', (0,0), (-1,-1), 'CENTER'),
-            ('BOTTOMPADDING', (0,0), (-1,-1), 15),
-        ]))
-        
-        story.append(title_table)
-        story.append(Spacer(1, 0.2 * inch))
-        
-        # Date and basic info
-        info_table = Table([
-            ["Candidate ID:", analysis_data.get('candidate_id', 'N/A')],
-            ["Interview Date:", time.strftime('%Y-%m-%d')],
-            ["Duration:", f"{analysis_data['text_analysis']['total_duration']:.2f} seconds"],
-            ["Speaker Turns:", analysis_data['text_analysis']['speaker_turns']]
-        ], colWidths=[doc.width/3, doc.width*2/3])
-        
-        info_table.setStyle(TableStyle([
-            ('FONTNAME', (0,0), (-1,-1), 'Helvetica'),
-            ('FONTSIZE', (0,0), (-1,-1), 10),
-            ('BOTTOMPADDING', (0,0), (-1,-1), 6),
-            ('LEFTPADDING', (0,0), (-1,-1), 4),
-            ('VALIGN', (0,0), (-1,-1), 'TOP'),
+            ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'),
+            ('FONTSIZE', (0,0), (-1,-1), 9),
+            ('BOTTOMPADDING', (0,0), (-1,0), 8),
+            ('TOPPADDING', (0,0), (-1,0), 8),
+            ('BACKGROUND', (0,1), (-1,-1), colors.HexColor('#F5F6FA')),
+            ('GRID', (0,0), (-1,-1), 0.5, colors.HexColor('#DDE4EB')),
         ]))
+        story.append(table)
+        story.append(Spacer(1, 0.4*inch))
+        story.append(Paragraph("Prepared by: EvalBot - AI-Powered HR Analysis", body_text))
+        story.append(PageBreak())
+
+        # Detailed Analysis
+        story.append(Paragraph("Detailed Candidate Evaluation", h1))
         
-        story.append(info_table)
-        story.append(Spacer(1, 0.3 * inch))
-
-        # --- Acceptance Probability ---
-        acceptance_prob = analysis_data.get('acceptance_probability', None)
-        if acceptance_prob is not None:
-            # Determine color based on probability
-            if acceptance_prob >= 80:
-                prob_color = colors.HexColor('#2E8B57')  # Green
-                prob_text = "Excellent Candidate - Strong Recommendation"
-            elif acceptance_prob >= 60:
-                prob_color = colors.HexColor('#FFA500')  # Orange
-                prob_text = "Good Candidate - Recommend With Some Development"
-            else:
-                prob_color = colors.HexColor('#CD5C5C')  # Red
-                prob_text = "Needs Significant Development"
-            
-            prob_table = Table([
-                [
-                    Paragraph(
-                        f"<b>Estimated Acceptance Probability:</b>",
-                        ParagraphStyle(
-                            name='ProbLabel',
-                            parent=styles['Normal'],
-                            fontSize=12,
-                            textColor=colors.HexColor('#333333')
-                        )
-                    ),
-                    Paragraph(
-                        f"<b>{acceptance_prob:.2f}%</b>",
-                        ParagraphStyle(
-                            name='ProbValue',
-                            parent=styles['Normal'],
-                            fontSize=14,
-                            textColor=prob_color,
-                            fontName='Helvetica-Bold'
-                        )
-                    )
-                ],
-                [
-                    "",
-                    Paragraph(
-                        prob_text,
-                        ParagraphStyle(
-                            name='ProbText',
-                            parent=styles['Normal'],
-                            fontSize=10,
-                            textColor=prob_color
-                        )
-                    )
-                ]
-            ], colWidths=[doc.width/2, doc.width/2])
-            
-            prob_table.setStyle(TableStyle([
-                ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),
-                ('BOTTOMPADDING', (0,0), (-1,0), 8),
-                ('BACKGROUND', (0,0), (-1,-1), colors.HexColor('#F5F5F5')),
-                ('BOX', (0,0), (-1,-1), 0.5, colors.HexColor('#E0E0E0')),
-                ('LEFTPADDING', (0,0), (-1,-1), 8),
-                ('RIGHTPADDING', (0,0), (-1,-1), 8),
-            ]))
-            
-            story.append(Paragraph("Candidate Evaluation Summary", h2))
-            story.append(Spacer(1, 0.1 * inch))
-            story.append(prob_table)
-            story.append(Spacer(1, 0.3 * inch))
-
-        # --- Voice Analysis ---
-        story.append(Paragraph("2. Voice Analysis", h2))
+        # Communication and Vocal Dynamics
+        story.append(Paragraph("1. Communication & Vocal Dynamics", h2))
         voice_analysis = analysis_data.get('voice_analysis', {})
-        
         if voice_analysis and 'error' not in voice_analysis:
-            # Create a more professional table
             table_data = [
-                [
-                    Paragraph("<b>Metric</b>", body_text),
-                    Paragraph("<b>Value</b>", body_text),
-                    Paragraph("<b>Interpretation</b>", body_text)
-                ]
-            ]
-            
-            # Add metrics with conditional formatting
-            metrics = [
-                ('Speaking Rate', f"{voice_analysis['speaking_rate']:.2f} words/sec", 
-                 'Optimal range: 2.5-3.0 words/sec', voice_analysis['speaking_rate']),
-                ('Filler Words', f"{voice_analysis['filler_ratio'] * 100:.1f}%", 
-                 'Ideal: <3% of total words', voice_analysis['filler_ratio']),
-                ('Repetition Score', f"{voice_analysis['repetition_score']:.3f}", 
-                 'Lower is better (0-0.1 optimal)', voice_analysis['repetition_score']),
-                ('Anxiety Level', voice_analysis['interpretation']['anxiety_level'].upper(), 
-                 f"Score: {voice_analysis['composite_scores']['anxiety']:.3f}", 
-                 voice_analysis['composite_scores']['anxiety']),
-                ('Confidence Level', voice_analysis['interpretation']['confidence_level'].upper(), 
-                 f"Score: {voice_analysis['composite_scores']['confidence']:.3f}", 
-                 voice_analysis['composite_scores']['confidence']),
-                ('Fluency', voice_analysis['interpretation']['fluency_level'].upper(), 
-                 'Overall speech flow', 0)
+                ['Metric', 'Value', 'HR Insight'],
+                ['Speaking Rate', f"{voice_analysis.get('speaking_rate', 0):.2f} words/sec", 'Benchmark: 2.0-3.0 wps; impacts clarity'],
+                ['Filler Words', f"{voice_analysis.get('filler_ratio', 0) * 100:.1f}%", 'High usage reduces credibility'],
+                ['Anxiety', voice_analysis.get('interpretation', {}).get('anxiety_level', 'N/A'), f"Score: {voice_analysis.get('composite_scores', {}).get('anxiety', 0):.3f}"],
+                ['Confidence', voice_analysis.get('interpretation', {}).get('confidence_level', 'N/A'), f"Score: {voice_analysis.get('composite_scores', {}).get('confidence', 0):.3f}"],
+                ['Fluency', voice_analysis.get('interpretation', {}).get('fluency_level', 'N/A'), 'Drives engagement'],
             ]
-            
-            for metric in metrics:
-                row = []
-                row.append(Paragraph(metric[0], body_text))
-                
-                # Apply conditional formatting
-                if metric[0] == 'Filler Words' and metric[3] > 0.03:
-                    row.append(Paragraph(metric[1], ParagraphStyle(
-                        name='AlertText', parent=body_text, textColor=colors.red
-                    )))
-                elif metric[0] == 'Anxiety Level' and metric[3] > 0.15:
-                    row.append(Paragraph(metric[1], ParagraphStyle(
-                        name='AlertText', parent=body_text, textColor=colors.red
-                    )))
-                else:
-                    row.append(Paragraph(metric[1], body_text))
-                
-                row.append(Paragraph(metric[2], body_text))
-                table_data.append(row)
-            
-            voice_table = Table(table_data, colWidths=[doc.width*0.3, doc.width*0.25, doc.width*0.45])
-            
-            voice_table.setStyle(TableStyle([
-                ('BACKGROUND', (0,0), (-1,0), colors.HexColor('#5B9BD5')),
+            table = Table(table_data, colWidths=[1.6*inch, 1.2*inch, 3.2*inch])
+            table.setStyle(TableStyle([
+                ('BACKGROUND', (0,0), (-1,0), colors.HexColor('#0050BC')),
                 ('TEXTCOLOR', (0,0), (-1,0), colors.white),
-                ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'),
                 ('ALIGN', (0,0), (-1,-1), 'LEFT'),
-                ('VALIGN', (0,0), (-1,-1), 'TOP'),
-                ('GRID', (0,0), (-1,-1), 0.5, colors.HexColor('#D3D3D3')),
-                ('BACKGROUND', (0,1), (-1,-1), colors.HexColor('#F9F9F9')),
-                ('LEFTPADDING', (0,0), (-1,-1), 4),
-                ('RIGHTPADDING', (0,0), (-1,-1), 4),
-                ('TOPPADDING', (0,0), (-1,-1), 4),
-                ('BOTTOMPADDING', (0,0), (-1,-1), 4),
+                ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),
+                ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'),
+                ('FONTSIZE', (0,0), (-1,-1), 9),
+                ('BOTTOMPADDING', (0,0), (-1,0), 8),
+                ('TOPPADDING', (0,0), (-1,0), 8),
+                ('BACKGROUND', (0,1), (-1,-1), colors.HexColor('#F5F6FA')),
+                ('GRID', (0,0), (-1,-1), 0.5, colors.HexColor('#DDE4EB')),
             ]))
-            
-            story.append(voice_table)
-            story.append(Spacer(1, 0.3 * inch))
-            
-            # Add interpretation section
-            story.append(Paragraph("Key Observations:", h3))
-            
-            observations = [
-                f"The candidate demonstrated {voice_analysis['interpretation']['fluency_level'].lower()} speech fluency overall.",
-                f"Confidence levels were rated as {voice_analysis['interpretation']['confidence_level'].lower()} (score: {voice_analysis['composite_scores']['confidence']:.3f}).",
-                f"Anxiety levels were {voice_analysis['interpretation']['anxiety_level'].lower()} (score: {voice_analysis['composite_scores']['anxiety']:.3f}), which may indicate nervousness.",
-                f"Filler word usage at {voice_analysis['filler_ratio'] * 100:.1f}% {'exceeds' if voice_analysis['filler_ratio'] > 0.03 else 'is within'} the ideal range."
-            ]
-            
-            for obs in observations:
-                story.append(Paragraph(obs, bullet_style))
-            
-            story.append(Spacer(1, 0.3 * inch))
-
-        # --- Content Analysis ---
-        story.append(Paragraph("3. Content Analysis", h2))
-        
-        # Strengths Section
-        story.append(Paragraph("Key Strengths:", h3))
-        strengths = [
-            "Strong academic background in Computational Biology from MIT",
-            "Demonstrated passion for applying engineering to healthcare challenges",
-            "Meaningful leadership experience in community programs",
-            "Honest self-assessment of technical skills with growth mindset"
-        ]
-        
-        for strength in strengths:
-            story.append(Paragraph(strength, bullet_style))
-        
-        story.append(Spacer(1, 0.2 * inch))
-        
-        # Development Areas Section
-        story.append(Paragraph("Areas for Development:", h3))
-        dev_areas = [
-            "Quantifying achievements with specific metrics and outcomes",
-            "Structuring responses more clearly using frameworks like STAR",
-            "Developing a concrete plan to address technical skill gaps",
-            "Reducing filler words for more professional communication"
-        ]
-        
-        for area in dev_areas:
-            story.append(Paragraph(area, bullet_style))
-        
-        story.append(Spacer(1, 0.3 * inch))
+            story.append(table)
+            story.append(Spacer(1, 0.2*inch))
+            chart_buffer = io.BytesIO()
+            generate_anxiety_confidence_chart(voice_analysis.get('composite_scores', {}), chart_buffer)
+            chart_buffer.seek(0)
+            img = Image(chart_buffer, width=4.5*inch, height=3*inch)
+            img.hAlign = 'CENTER'
+            story.append(img)
+        else:
+            story.append(Paragraph("Vocal analysis unavailable.", body_text))
+        story.append(Spacer(1, 0.2*inch))
+
+        # Parse Gemini Report
+        sections = {
+            "Executive Summary": [],
+            "Communication": [],
+            "Competency": {"Strengths": [], "Growth Areas": []},
+            "Recommendations": {"Development": [], "Next Steps": []},
+            "Role Fit": [],
+        }
+        current_section = None
+        current_subsection = None
+        lines = gemini_report_text.split('\n')
+        for line in lines:
+            line = line.strip()
+            if not line: continue
+            # Simplified regex to avoid parenthesis issues
+            if line.startswith('**') and line.endswith('**'):
+                section_title = line.strip('**').strip()
+                if section_title.startswith(('1.', '2.', '3.', '4.', '5.')):
+                    section_title = section_title[2:].strip()
+                if 'Executive Summary' in section_title:
+                    current_section = 'Executive Summary'
+                    current_subsection = None
+                elif 'Communication' in section_title:
+                    current_section = 'Communication'
+                    current_subsection = None
+                elif 'Competency' in section_title:
+                    current_section = 'Competency'
+                    current_subsection = None
+                elif 'Role Fit' in section_title:
+                    current_section = 'Role Fit'
+                    current_subsection = None
+                elif 'Recommendations' in section_title:
+                    current_section = 'Recommendations'
+                    current_subsection = None
+            elif line.startswith(('-', '*', '•')) and current_section:
+                clean_line = line.lstrip('-*• ').strip()
+                if not clean_line: continue
+                clean_line = re.sub(r'[()]', '', clean_line)  # Remove parentheses
+                if current_section == 'Competency':
+                    if any(k in clean_line.lower() for k in ['leader', 'problem', 'commun', 'adapt', 'strength']):
+                        current_subsection = 'Strengths'
+                    elif any(k in clean_line.lower() for k in ['improv', 'grow', 'depth']):
+                        current_subsection = 'Growth Areas'
+                    if current_subsection:
+                        sections[current_section][current_subsection].append(clean_line)
+                elif current_section == 'Recommendations':
+                    if any(k in clean_line.lower() for k in ['commun', 'tech', 'depth', 'pres']):
+                        current_subsection = 'Development'
+                    elif any(k in clean_line.lower() for k in ['adv', 'train', 'assess', 'next', 'mentor']):
+                        current_subsection = 'Next Steps'
+                    if current_subsection:
+                        sections[current_section][current_subsection].append(clean_line)
+                else:
+                    sections[current_section].append(clean_line)
 
-        # --- Recommendations ---
-        story.append(Paragraph("4. Actionable Recommendations", h2))
-        
-        # Communication
-        story.append(Paragraph("Communication Skills:", h3))
-        comm_recs = [
-            "Practice speaking at 2.8-3.0 words/sec for optimal clarity",
-            "Use recording tools to identify and reduce filler words",
-            "Conduct mock interviews focusing on pacing and fluency"
-        ]
-        
-        for rec in comm_recs:
-            story.append(Paragraph(rec, bullet_style))
-        
-        story.append(Spacer(1, 0.2 * inch))
-        
-        # Content
-        story.append(Paragraph("Content Delivery:", h3))
-        content_recs = [
-            "Prepare 3-5 quantified achievement statements",
-            "Use STAR method (Situation-Task-Action-Result) for responses",
-            "Develop structured answers for common interview questions"
-        ]
-        
-        for rec in content_recs:
-            story.append(Paragraph(rec, bullet_style))
-        
-        story.append(Spacer(1, 0.2 * inch))
-        
-        # Technical
-        story.append(Paragraph("Technical Preparation:", h3))
-        tech_recs = [
-            "Complete introductory Python certification (e.g., MITx 6.00.1x)",
-            "Build small genomics data analysis project for portfolio",
-            "Research company's technical stack and prepare relevant examples"
-        ]
-        
-        for rec in tech_recs:
-            story.append(Paragraph(rec, bullet_style))
-        
-        story.append(Spacer(1, 0.3 * inch))
-
-        # --- Footer ---
-        footer = Paragraph(
-            "Analysis generated by EvalBot AI Interview Assessment System | Confidential",
-            ParagraphStyle(
-                name='Footer',
-                parent=styles['Normal'],
-                fontSize=8,
-                textColor=colors.HexColor('#666666'),
-                alignment=TA_CENTER
-            )
-        )
-        
-        story.append(Spacer(1, 0.5 * inch))
-        story.append(footer)
+        # Executive Summary
+        story.append(Paragraph("2. Executive Summary", h2))
+        if sections['Executive Summary']:
+            for line in sections['Executive Summary']:
+                story.append(Paragraph(line, bullet_style))
+        else:
+            story.append(Paragraph("No summary provided.", body_text))
+        story.append(Spacer(1, 0.2*inch))
+
+        # Competency and Content
+        story.append(Paragraph("3. Competency & Evaluation", h2))
+        story.append(Paragraph("Strengths", h3))
+        if sections['Competency']['Strengths']:
+            for line in sections['Competency']['Strengths']:
+                story.append(Paragraph(line, bullet_style))
+        else:
+            story.append(Paragraph("No strengths identified.", body_text))
+        story.append(Spacer(1, 0.1*inch))
+        story.append(Paragraph("Growth Areas", h3))
+        if sections['Competency']['Growth Areas']:
+            for line in sections['Competency']['Growth Areas']:
+                story.append(Paragraph(line, bullet_style))
+        else:
+            story.append(Paragraph("No growth areas identified; maintain current strengths.", body_text))
+        story.append(Spacer(1, 0.2*inch))
+
+        # Role Fit
+        story.append(Paragraph("4. Role Fit & Potential", h2))
+        if sections['Role Fit']:
+            for line in sections['Role Fit']:
+                story.append(Paragraph(line, bullet_style))
+        else:
+            story.append(Paragraph("No fit analysis provided.", body_text))
+        story.append(Spacer(1, 0.2*inch))
+
+        # Recommendations
+        story.append(Paragraph("5. Recommendations", h2))
+        story.append(Paragraph("Development Priorities", h3))
+        if sections['Recommendations']['Development']:
+            for line in sections['Recommendations']['Development']:
+                story.append(Paragraph(line, bullet_style))
+        else:
+            story.append(Paragraph("No development priorities specified.", body_text))
+        story.append(Spacer(1, 0.1*inch))
+        story.append(Paragraph("Next Steps", h3))
+        if sections['Recommendations']['Next Steps']:
+            for line in sections['Recommendations']['Next Steps']:
+                story.append(Paragraph(line, bullet_style))
+        else:
+            story.append(Paragraph("No next steps provided.", body_text))
+        story.append(Spacer(1, 0.2*inch))
 
-        doc.build(story)
+        doc.build(story, onFirstPage=header_footer, onLaterPages=header_footer)
+        logger.info(f"PDF report successfully generated at {output_path}")
         return True
-        
     except Exception as e:
-        logger.error(f"PDF creation failed: {str(e)}", exc_info=True)
+        logger.error(f"PDF generation failed: {str(e)}", exc_info=True)
         return False