Update process_interview.py

process_interview.py

@@ -369,22 +369,22 @@ def analyze_interviewee_voice(audio_path: str, utterances: List[Dict]) -> Dict:
 
 def generate_voice_interpretation(analysis: Dict) -> str:
     if 'error' in analysis:
-        return "Voice analysis not available due to processing error."
+        return "Voice analysis unavailable due to processing limitations."
     interpretation_lines = [
-        "Voice and Speech Profile:",
-        f"- Speaking Rate: {analysis['speaking_rate']} words/sec - Compared to optimal range (2.0-3.0 words/sec)",
-        f"- Filler Word Usage: {analysis['filler_ratio'] * 100:.1f}% - Frequency of non-content words (e.g., 'um', 'like')",
-        f"- Repetition Tendency: {analysis['repetition_score']:.3f} - Measure of repeated phrases",
-        f"- Anxiety Indicator: {analysis['interpretation']['anxiety_level']} (Score: {analysis['composite_scores']['anxiety']:.3f}) - Based on pitch and voice stability",
-        f"- Confidence Indicator: {analysis['interpretation']['confidence_level']} (Score: {analysis['composite_scores']['confidence']:.3f}) - Derived from vocal consistency",
-        f"- Fluency Assessment: {analysis['interpretation']['fluency_level']} - Reflects speech flow and coherence",
+        "Vocal Performance Profile:",
+        f"- Speaking Rate: {analysis['speaking_rate']} words/sec - Benchmark: 2.0-3.0 wps for clear, professional delivery",
+        f"- Filler Word Frequency: {analysis['filler_ratio'] * 100:.1f}% - Measures non-content words (e.g., 'um', 'like')",
+        f"- Repetition Index: {analysis['repetition_score']:.3f} - Frequency of repeated phrases or ideas",
+        f"- Anxiety Indicator: {analysis['interpretation']['anxiety_level']} (Score: {analysis['composite_scores']['anxiety']:.3f}) - Derived from pitch variation and vocal stability",
+        f"- Confidence Indicator: {analysis['interpretation']['confidence_level']} (Score: {analysis['composite_scores']['confidence']:.3f}) - Reflects vocal strength and consistency",
+        f"- Fluency Rating: {analysis['interpretation']['fluency_level']} - Assesses speech flow and coherence",
         "",
-        "HR Insights:",
-        "- Faster speaking rates may indicate confidence but can suggest nervousness if excessive.",
-        "- High filler word usage often reduces perceived professionalism and clarity.",
-        "- Elevated anxiety indicators (pitch variability, jitter) may reflect interview pressure.",
-        "- Strong confidence scores suggest effective vocal presence and control.",
-        "- Fluency impacts listener engagement; disfluency may hinder communication effectiveness."
+        "HR Performance Insights:",
+        "- Rapid speech (>3.0 wps) may signal enthusiasm but risks clarity; slower, deliberate pacing enhances professionalism.",
+        "- Elevated filler word use reduces perceived polish and can distract from key messages.",
+        "- High anxiety scores suggest interview pressure; training can build resilience.",
+        "- Strong confidence indicators align with leadership presence and effective communication.",
+        "- Fluent speech enhances engagement, critical for client-facing or team roles."
     ]
     return "\n".join(interpretation_lines)
 
@@ -392,17 +392,18 @@ def generate_anxiety_confidence_chart(composite_scores: Dict, chart_path_or_buff
     try:
         labels = ['Anxiety', 'Confidence']
         scores = [composite_scores.get('anxiety', 0), composite_scores.get('confidence', 0)]
-        fig, ax = plt.subplots(figsize=(4, 2.5))
-        bars = ax.bar(labels, scores, color=['#FF6B6B', '#4ECDC4'], edgecolor='black')
-        ax.set_ylabel('Score (Normalized)')
-        ax.set_title('Vocal Dynamics: Anxiety vs. Confidence')
+        fig, ax = plt.subplots(figsize=(5, 3))
+        bars = ax.bar(labels, scores, color=['#FF6B6B', '#4ECDC4'], edgecolor='black', width=0.6)
+        ax.set_ylabel('Score (Normalized)', fontsize=12)
+        ax.set_title('Vocal Dynamics: Anxiety vs. Confidence', fontsize=14, pad=15)
         ax.set_ylim(0, 1.2)
         for bar in bars:
             height = bar.get_height()
             ax.text(bar.get_x() + bar.get_width()/2, height + 0.05, f"{height:.2f}",
-                    ha='center', color='black', fontweight='bold', fontsize=10)
+                    ha='center', color='black', fontweight='bold', fontsize=11)
+        ax.grid(True, axis='y', linestyle='--', alpha=0.7)
         plt.tight_layout()
-        plt.savefig(chart_path_or_buffer, format='png', bbox_inches='tight', dpi=150)
+        plt.savefig(chart_path_or_buffer, format='png', bbox_inches='tight', dpi=200)
         plt.close(fig)
     except Exception as e:
         logger.error(f"Error generating chart: {str(e)}")
@@ -410,21 +411,21 @@ def generate_anxiety_confidence_chart(composite_scores: Dict, chart_path_or_buff
 def calculate_acceptance_probability(analysis_data: Dict) -> float:
     voice = analysis_data.get('voice_analysis', {})
     if 'error' in voice: return 0.0
-    w_confidence, w_anxiety, w_fluency, w_speaking_rate, w_filler_repetition, w_content_strengths = 0.4, -0.3, 0.2, 0.1, -0.1, 0.2
+    w_confidence, w_anxiety, w_fluency, w_speaking_rate, w_filler_repetition, w_content_strengths = 0.35, -0.25, 0.2, 0.15, -0.15, 0.25
     confidence_score = voice.get('composite_scores', {}).get('confidence', 0.0)
     anxiety_score = voice.get('composite_scores', {}).get('anxiety', 0.0)
     fluency_level = voice.get('interpretation', {}).get('fluency_level', 'Disfluent')
     speaking_rate = voice.get('speaking_rate', 0.0)
     filler_ratio = voice.get('filler_ratio', 0.0)
     repetition_score = voice.get('repetition_score', 0.0)
-    fluency_map = {'Fluent': 1.0, 'Moderate': 0.5, 'Disfluent': 0.0}
-    fluency_val = fluency_map.get(fluency_level, 0.0)
+    fluency_map = {'Fluent': 1.0, 'Moderate': 0.6, 'Disfluent': 0.2}
+    fluency_val = fluency_map.get(fluency_level, 0.2)
     ideal_speaking_rate = 2.5
     speaking_rate_deviation = abs(speaking_rate - ideal_speaking_rate)
     speaking_rate_score = max(0, 1 - (speaking_rate_deviation / ideal_speaking_rate))
     filler_repetition_composite = (filler_ratio + repetition_score) / 2
     filler_repetition_score = max(0, 1 - filler_repetition_composite)
-    content_strength_val = 0.8 if analysis_data.get('text_analysis', {}).get('total_duration', 0) > 0 else 0.0
+    content_strength_val = 0.85 if analysis_data.get('text_analysis', {}).get('total_duration', 0) > 60 else 0.4
     raw_score = (confidence_score * w_confidence + (1 - anxiety_score) * abs(w_anxiety) + fluency_val * w_fluency + speaking_rate_score * w_speaking_rate + filler_repetition_score * abs(w_filler_repetition) + content_strength_val * w_content_strengths)
     max_possible_score = (w_confidence + abs(w_anxiety) + w_fluency + w_speaking_rate + abs(w_filler_repetition) + w_content_strengths)
     if max_possible_score == 0: return 50.0
@@ -436,38 +437,39 @@ def generate_report(analysis_data: Dict) -> str:
     try:
         voice = analysis_data.get('voice_analysis', {})
         voice_interpretation = generate_voice_interpretation(voice)
-        interviewee_responses = [f"Speaker {u['speaker']} ({u['role']}): {u['text']}" for u in analysis_data['transcript'] if u['role'] == 'Interviewee'][:5]
+        interviewee_responses = [f"Speaker {u['speaker']} ({u['role']}): {u['text']}" for u in analysis_data['transcript'] if u['role'] == 'Interviewee'][:6]
         acceptance_prob = analysis_data.get('acceptance_probability', None)
         acceptance_line = ""
         if acceptance_prob is not None:
-            acceptance_line = f"\n**Hiring Potential Score: {acceptance_prob:.2f}%**\n"
-            if acceptance_prob >= 80: acceptance_line += "Assessment: Exceptional candidate, strongly recommended for advancement."
-            elif acceptance_prob >= 50: acceptance_line += "Assessment: Promising candidate with moderate strengths; consider for further evaluation."
-            else: acceptance_line += "Assessment: Limited alignment with role expectations; significant development needed."
+            acceptance_line = f"\n**Hiring Suitability Score: {acceptance_prob:.2f}%**\n"
+            if acceptance_prob >= 80: acceptance_line += "HR Verdict: Outstanding candidate, highly recommended for immediate advancement."
+            elif acceptance_prob >= 60: acceptance_line += "HR Verdict: Strong candidate, suitable for further evaluation with targeted development."
+            elif acceptance_prob >= 40: acceptance_line += "HR Verdict: Moderate potential, requires additional assessment and skill-building."
+            else: acceptance_line += "HR Verdict: Limited fit, significant improvement needed for role alignment."
         prompt = f"""
-        You are an expert HR consultant, EvalBot, tasked with producing a professional, concise, and actionable interview analysis report. Structure the report with clear headings, subheadings, and bullet points (use '- ' for bullets). Adopt a formal, HR-professional tone, focusing on candidate evaluation, fit for role, and development insights.
+        You are EvalBot, a senior HR consultant with 20+ years of experience, delivering a polished, concise, and visually engaging interview analysis report. Use a professional tone, clear headings, and bullet points ('- ') for readability. Focus on candidate suitability, strengths, and actionable growth strategies.
         {acceptance_line}
         **1. Executive Summary**
-        - Provide a concise overview of the interview, highlighting key metrics and overall candidate performance.
-        - Interview duration: {analysis_data['text_analysis']['total_duration']:.2f} seconds
-        - Total speaker turns: {analysis_data['text_analysis']['speaker_turns']}
+        - Deliver a crisp overview of the candidate's performance, emphasizing key metrics and hiring potential.
+        - Interview length: {analysis_data['text_analysis']['total_duration']:.2f} seconds
+        - Speaker turns: {analysis_data['text_analysis']['speaker_turns']}
         - Participants: {', '.join(analysis_data['speakers'])}
-        **2. Communication and Vocal Analysis**
-        - Evaluate the candidate's vocal delivery, including speaking rate, fluency, and confidence indicators.
-        - Provide HR-relevant insights into how these metrics impact perceived professionalism and role suitability.
+        **2. Communication and Vocal Dynamics**
+        - Assess the candidate's vocal delivery (rate, fluency, confidence) and its impact on professional presence.
+        - Provide HR insights on how these traits align with workplace expectations.
         {voice_interpretation}
-        **3. Content Analysis and Competency Assessment**
-        - Analyze key themes in the candidate's responses to assess alignment with job competencies (e.g., problem-solving, communication, leadership).
-        - Identify strengths and areas for improvement, supported by specific examples.
-        - Sample responses for context:
+        **3. Competency and Content Evaluation**
+        - Evaluate responses for core competencies: leadership, problem-solving, communication, adaptability.
+        - Highlight strengths and growth areas with specific, concise examples.
+        - Sample responses:
         {chr(10).join(interviewee_responses)}
-        **4. Fit and Potential Evaluation**
-        - Assess the candidate's overall fit for a typical professional role based on communication, content, and vocal dynamics.
-        - Consider cultural fit, adaptability, and readiness for the role.
-        **5. Actionable HR Recommendations**
-        - Provide specific, prioritized recommendations for the candidate’s development.
-        - Focus areas: Effective Communication, Content Clarity and Depth, Professional Presence.
-        - Suggest next steps for hiring managers (e.g., advance to next round, additional assessments, training focus).
+        **4. Role Fit and Growth Potential**
+        - Analyze alignment with professional roles, focusing on cultural fit, readiness, and scalability.
+        - Consider enthusiasm, teamwork, and long-term potential.
+        **5. Strategic HR Recommendations**
+        - Offer prioritized, actionable strategies to enhance candidate performance.
+        - Target: Communication Effectiveness, Response Depth, Professional Impact.
+        - Suggest clear next steps for hiring managers (e.g., advance, train, assess).
         """
         response = gemini_model.generate_content(prompt)
         return response.text
@@ -478,63 +480,89 @@ def generate_report(analysis_data: Dict) -> str:
 def create_pdf_report(analysis_data: Dict, output_path: str, gemini_report_text: str):
     try:
         doc = SimpleDocTemplate(output_path, pagesize=letter,
-                                rightMargin=0.75*inch, leftMargin=0.75*inch,
-                                topMargin=1*inch, bottomMargin=1*inch)
+                                rightMargin=0.6*inch, leftMargin=0.6*inch,
+                                topMargin=0.8*inch, bottomMargin=0.8*inch)
         styles = getSampleStyleSheet()
-        h1 = ParagraphStyle(name='Heading1', fontSize=22, leading=26, spaceAfter=20, alignment=1, textColor=colors.HexColor('#1A3C5E'))
-        h2 = ParagraphStyle(name='Heading2', fontSize=14, leading=18, spaceBefore=14, spaceAfter=8, textColor=colors.HexColor('#2E5A87'))
-        body_text = ParagraphStyle(name='BodyText', parent=styles['Normal'], fontSize=10, leading=14, spaceAfter=8, fontName='Helvetica')
-        bullet_style = ParagraphStyle(name='Bullet', parent=body_text, leftIndent=20, bulletIndent=10, fontName='Helvetica')
+        h1 = ParagraphStyle(name='Heading1', fontSize=24, leading=28, spaceAfter=25, alignment=1, textColor=colors.HexColor('#1A3C5E'), fontName='Helvetica-Bold')
+        h2 = ParagraphStyle(name='Heading2', fontSize=16, leading=20, spaceBefore=16, spaceAfter=10, textColor=colors.HexColor('#2E5A87'), fontName='Helvetica-Bold')
+        h3 = ParagraphStyle(name='Heading3', fontSize=12, leading=16, spaceBefore=12, spaceAfter=8, textColor=colors.HexColor('#4A6FA5'), fontName='Helvetica')
+        body_text = ParagraphStyle(name='BodyText', parent=styles['Normal'], fontSize=10, leading=14, spaceAfter=10, fontName='Helvetica')
+        bullet_style = ParagraphStyle(name='Bullet', parent=body_text, leftIndent=25, bulletIndent=12, fontName='Helvetica')
         
         story = []
 
         def header_footer(canvas, doc):
             canvas.saveState()
             canvas.setFont('Helvetica', 9)
-            canvas.setFillColor(colors.grey)
+            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('#2E5A87'))
-            canvas.setLineWidth(1)
-            canvas.line(doc.leftMargin, doc.height + 0.85*inch, doc.width + doc.leftMargin, doc.height + 0.85*inch)
-            canvas.setFont('Helvetica-Bold', 10)
-            canvas.drawString(doc.leftMargin, doc.height + 0.9*inch, "Candidate Interview Analysis Report")
+            canvas.setLineWidth(1.2)
+            canvas.line(doc.leftMargin, doc.height + 0.9*inch, doc.width + doc.leftMargin, doc.height + 0.9*inch)
+            canvas.setFont('Helvetica-Bold', 11)
+            canvas.drawString(doc.leftMargin, doc.height + 0.95*inch, "Candidate Interview Analysis")
+            canvas.setFillColor(colors.HexColor('#666666'))
+            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 Report", h1))
-        story.append(Paragraph(f"Generated on: {time.strftime('%B %d, %Y')}", ParagraphStyle(name='Date', alignment=1, fontSize=10, textColor=colors.grey)))
-        story.append(Spacer(1, 0.5 * inch))
+        story.append(Paragraph("Candidate Interview Analysis", h1))
+        story.append(Paragraph(f"Generated: {time.strftime('%B %d, %Y')}", ParagraphStyle(name='Date', alignment=1, fontSize=11, textColor=colors.HexColor('#666666'), fontName='Helvetica')))
+        story.append(Spacer(1, 0.6 * inch))
         acceptance_prob = analysis_data.get('acceptance_probability')
         if acceptance_prob is not None:
-            story.append(Paragraph("Hiring Potential Snapshot", h2))
-            prob_color = colors.HexColor('#2E7D32') if acceptance_prob >= 70 else (colors.HexColor('#F57C00') if acceptance_prob >= 40 else colors.HexColor('#D32F2F'))
-            story.append(Paragraph(f"Hiring Potential Score: <font size=16 color='{prob_color.hexval()}'><b>{acceptance_prob:.2f}%</b></font>",
-                                 ParagraphStyle(name='Prob', fontSize=12, spaceAfter=12, alignment=1)))
+            story.append(Paragraph("Hiring Suitability Overview", 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"Hiring Suitability Score: <font size=18 color='{prob_color.hexval()}'><b>{acceptance_prob:.2f}%</b></font>",
+                                 ParagraphStyle(name='Prob', fontSize=14, spaceAfter=15, alignment=1, fontName='Helvetica-Bold')))
             if acceptance_prob >= 80:
-                story.append(Paragraph("<b>HR Assessment:</b> Exceptional candidate, strongly recommended for advancement to the next stage.", body_text))
-            elif acceptance_prob >= 50:
-                story.append(Paragraph("<b>HR Assessment:</b> Promising candidate with moderate strengths; consider for further evaluation.", body_text))
+                story.append(Paragraph("<b>HR Verdict:</b> Outstanding candidate, highly recommended for immediate advancement.", body_text))
+            elif acceptance_prob >= 60:
+                story.append(Paragraph("<b>HR Verdict:</b> Strong candidate, suitable for further evaluation with targeted development.", body_text))
+            elif acceptance_prob >= 40:
+                story.append(Paragraph("<b>HR Verdict:</b> Moderate potential, requires additional assessment and skill-building.", body_text))
             else:
-                story.append(Paragraph("<b>HR Assessment:</b> Limited alignment with role expectations; significant development needed.", body_text))
-        story.append(Spacer(1, 0.3 * inch))
-        story.append(Paragraph("Prepared by: EvalBot - AI-Powered HR Interview Analysis System", body_text))
+                story.append(Paragraph("<b>HR Verdict:</b> Limited fit, significant improvement needed for role alignment.", body_text))
+            story.append(Spacer(1, 0.4 * inch))
+            table_data = [
+                ['Key Metrics', 'Value'],
+                ['Interview Length', f"{analysis_data['text_analysis']['total_duration']:.2f} seconds"],
+                ['Speaker Turns', f"{analysis_data['text_analysis']['speaker_turns']}"],
+                ['Participants', ', '.join(analysis_data['speakers'])]
+            ]
+            table = Table(table_data, colWidths=[2.5*inch, 4*inch])
+            table.setStyle(TableStyle([
+                ('BACKGROUND', (0,0), (-1,0), colors.HexColor('#2E5A87')),
+                ('TEXTCOLOR', (0,0), (-1,0), colors.whitesmoke),
+                ('ALIGN', (0,0), (-1,-1), 'LEFT'),
+                ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),
+                ('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
+                ('FONTSIZE', (0, 0), (-1, -1), 10),
+                ('BOTTOMPADDING', (0, 0), (-1, 0), 12),
+                ('TOPPADDING', (0, 0), (-1, 0), 12),
+                ('BACKGROUND', (0, 1), (-1, -1), colors.HexColor('#F5F7FA')),
+                ('GRID', (0,0), (-1,-1), 1, colors.HexColor('#DDE4EB'))
+            ]))
+            story.append(table)
+        story.append(Spacer(1, 0.5 * inch))
+        story.append(Paragraph("Prepared by: EvalBot - AI-Powered HR Analysis System", body_text))
         story.append(PageBreak())
 
         # Detailed Analysis
-        story.append(Paragraph("Detailed Candidate Evaluation", h1))
+        story.append(Paragraph("Detailed Candidate Profile", h1))
         
-        story.append(Paragraph("1. Communication and Vocal Profile", h2))
+        story.append(Paragraph("1. Communication & Vocal Dynamics", h2))
         voice_analysis = analysis_data.get('voice_analysis', {})
         if voice_analysis and 'error' not in voice_analysis:
             table_data = [
                 ['Metric', 'Value', 'HR Insight'],
-                ['Speaking Rate', f"{voice_analysis.get('speaking_rate', 0):.2f} words/sec", 'Optimal: 2.0-3.0 wps; impacts clarity and confidence'],
-                ['Filler Word Usage', f"{voice_analysis.get('filler_ratio', 0) * 100:.1f}%", 'High usage may reduce perceived professionalism'],
-                ['Anxiety Indicator', voice_analysis.get('interpretation', {}).get('anxiety_level', 'N/A'), f"Score: {voice_analysis.get('composite_scores', {}).get('anxiety', 0):.3f}; reflects pressure response"],
-                ['Confidence Indicator', voice_analysis.get('interpretation', {}).get('confidence_level', 'N/A'), f"Score: {voice_analysis.get('composite_scores', {}).get('confidence', 0):.3f}; indicates vocal authority"],
-                ['Fluency Assessment', voice_analysis.get('interpretation', {}).get('fluency_level', 'N/A'), 'Affects engagement and message delivery']
+                ['Speaking Rate', f"{voice_analysis.get('speaking_rate', 0):.2f} words/sec", 'Benchmark: 2.0-3.0 wps; affects clarity, poise'],
+                ['Filler Word Frequency', f"{voice_analysis.get('filler_ratio', 0) * 100:.1f}%", 'Excess use impacts polish, credibility'],
+                ['Anxiety Indicator', voice_analysis.get('interpretation', {}).get('anxiety_level', 'N/A'), f"Score: {voice_analysis.get('composite_scores', {}).get('anxiety', 0):.3f}; shows stress response"],
+                ['Confidence Indicator', voice_analysis.get('interpretation', {}).get('confidence_level', 'N/A'), f"Score: {voice_analysis.get('composite_scores', {}).get('confidence', 0):.3f}; reflects vocal strength"],
+                ['Fluency Rating', voice_analysis.get('interpretation', {}).get('fluency_level', 'N/A'), 'Drives engagement, message impact']
             ]
-            table = Table(table_data, colWidths=[1.8*inch, 1.2*inch, 3.5*inch])
+            table = Table(table_data, colWidths=[1.9*inch, 1.3*inch, 3.3*inch])
             table.setStyle(TableStyle([
                 ('BACKGROUND', (0,0), (-1,0), colors.HexColor('#2E5A87')),
                 ('TEXTCOLOR', (0,0), (-1,0), colors.whitesmoke),
@@ -548,22 +576,22 @@ def create_pdf_report(analysis_data: Dict, output_path: str, gemini_report_text:
                 ('GRID', (0,0), (-1,-1), 1, colors.HexColor('#DDE4EB'))
             ]))
             story.append(table)
-            story.append(Spacer(1, 0.25 * inch))
+            story.append(Spacer(1, 0.3 * 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=2.8*inch)
+            img = Image(chart_buffer, width=5*inch, height=3*inch)
             img.hAlign = 'CENTER'
             story.append(img)
         else:
-            story.append(Paragraph("Voice analysis unavailable due to processing limitations.", body_text))
-        story.append(Spacer(1, 0.3 * inch))
+            story.append(Paragraph("Vocal analysis unavailable due to processing constraints.", body_text))
+        story.append(Spacer(1, 0.4 * inch))
 
         # Parse Gemini Report
         sections = {}
-        section_titles = ["Executive Summary", "Communication and Vocal Analysis", 
-                         "Content Analysis and Competency Assessment", 
-                         "Fit and Potential Evaluation", "Actionable HR Recommendations"]
+        section_titles = ["Executive Summary", "Communication and Vocal Dynamics", 
+                         "Competency and Content Evaluation", 
+                         "Role Fit and Growth Potential", "Strategic HR Recommendations"]
         for title in section_titles:
             sections[title] = []
         report_parts = re.split(r'(\s*\*\*\s*\d\.\s*.*?\s*\*\*)', gemini_report_text)
@@ -588,43 +616,69 @@ def create_pdf_report(analysis_data: Dict, output_path: str, gemini_report_text:
                 else:
                     story.append(Paragraph(line, body_text))
         else:
-            story.append(Paragraph("Summary not available from analysis.", body_text))
-        story.append(Spacer(1, 0.3 * inch))
-
-        # Content and Competency
-        story.append(Paragraph("3. Content and Competency Assessment", h2))
-        if sections['Content Analysis and Competency Assessment']:
-            for line in sections['Content Analysis and Competency Assessment']:
-                if line.startswith(('-', '•', '*')):
+            story.append(Paragraph("Executive summary unavailable.", body_text))
+        story.append(Spacer(1, 0.4 * inch))
+
+        # Competency and Content
+        story.append(Paragraph("3. Competency & Content Evaluation", h2))
+        if sections['Competency and Content Evaluation']:
+            story.append(Paragraph("Strengths", h3))
+            strengths_found = False
+            for line in sections['Competency and Content Evaluation']:
+                if 'strength' in line.lower() or any(k in line.lower() for k in ['leadership', 'problem-solving', 'communication', 'adaptability']):
                     story.append(Paragraph(line.lstrip('-•* ').strip(), bullet_style))
-                else:
-                    story.append(Paragraph(line, body_text))
+                    strengths_found = True
+            if not strengths_found:
+                story.append(Paragraph("No specific strengths identified.", body_text))
+            story.append(Spacer(1, 0.2 * inch))
+            story.append(Paragraph("Growth Areas", h3))
+            growth_found = False
+            for line in sections['Competency and Content Evaluation']:
+                if 'improve' in line.lower() or 'weak' in line.lower() or 'challenge' in line.lower():
+                    story.append(Paragraph(line.lstrip('-•* ').strip(), bullet_style))
+                    growth_found = True
+            if not growth_found:
+                story.append(Paragraph("No specific growth areas identified.", body_text))
         else:
-            story.append(Paragraph("Content and competency analysis not provided.", body_text))
+            story.append(Paragraph("Competency and content evaluation unavailable.", body_text))
         story.append(PageBreak())
 
-        # Fit and Potential
-        story.append(Paragraph("4. Fit and Potential Evaluation", h2))
-        if sections['Fit and Potential Evaluation']:
-            for line in sections['Fit and Potential Evaluation']:
+        # Role Fit
+        story.append(Paragraph("4. Role Fit & Growth Potential", h2))
+        if sections['Role Fit and Growth Potential']:
+            for line in sections['Role Fit and Growth Potential']:
                 if line.startswith(('-', '•', '*')):
                     story.append(Paragraph(line.lstrip('-•* ').strip(), bullet_style))
                 else:
                     story.append(Paragraph(line, body_text))
         else:
-            story.append(Paragraph("Fit and potential evaluation not available.", body_text))
-        story.append(Spacer(1, 0.3 * inch))
+            story.append(Paragraph("Role fit and potential analysis unavailable.", body_text))
+        story.append(Spacer(1, 0.4 * inch))
 
         # HR Recommendations
-        story.append(Paragraph("5. Actionable HR Recommendations", h2))
-        if sections['Actionable HR Recommendations']:
-            for line in sections['Actionable HR Recommendations']:
-                if line.startswith(('-', '•', '*')):
+        story.append(Paragraph("5. Strategic HR Recommendations", h2))
+        if sections['Strategic HR Recommendations']:
+            story.append(Paragraph("Development Priorities", h3))
+            dev_found = False
+            for line in sections['Strategic HR Recommendations']:
+                if any(k in line.lower() for k in ['communication', 'clarity', 'depth', 'presence', 'improve']):
                     story.append(Paragraph(line.lstrip('-•* ').strip(), bullet_style))
-                else:
-                    story.append(Paragraph(line, body_text))
+                    dev_found = True
+            if not dev_found:
+                story.append(Paragraph("No development priorities specified.", body_text))
+            story.append(Spacer(1, 0.2 * inch))
+            story.append(Paragraph("Next Steps for Hiring Managers", h3))
+            steps_found = False
+            for line in sections['Strategic HR Recommendations']:
+                if any(k in line.lower() for k in ['advance', 'train', 'assess', 'next step']):
+                    story.append(Paragraph(line.lstrip('-•* ').strip(), bullet_style))
+                    steps_found = True
+            if not steps_found:
+                story.append(Paragraph("No specific next steps provided.", body_text))
         else:
-            story.append(Paragraph("HR recommendations not provided.", body_text))
+            story.append(Paragraph("Strategic recommendations unavailable.", body_text))
+        story.append(Spacer(1, 0.3 * inch))
+        story.append(Paragraph("This report delivers a comprehensive, data-driven evaluation to guide hiring decisions and candidate development.", body_text))
 
         doc.build(story, onFirstPage=header_footer, onLaterPages=header_footer)
         return True