Update src/app.py

src/app.py

@@ -31,12 +31,16 @@ import language_tool_python
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics.pairwise import cosine_similarity
 
-# Report generation
+# Report generation - FIXED IMPORTS
 from reportlab.lib.pagesizes import letter
-from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, BarChart
+from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer
 from reportlab.lib.styles import getSampleStyleSheet
 from reportlab.lib.units import inch
 
+# Chart imports (if you need charts for PDF reports)
+from reportlab.graphics.charts.barcharts import VerticalBarChart
+from reportlab.graphics.shapes import Drawing
+
 # Download NLTK data if not already present
 @st.cache_resource
 def download_nltk_data():
@@ -305,6 +309,60 @@ class ResumeAnalyzer:
         
         return summary
 
+    def create_pdf_report(self, text, sections, ats_score, match_percentage, selected_role, tech_skills, soft_skills, found_keywords):
+        """Create a PDF report using ReportLab"""
+        buffer = io.BytesIO()
+        doc = SimpleDocTemplate(buffer, pagesize=letter)
+        styles = getSampleStyleSheet()
+        story = []
+        
+        # Title
+        story.append(Paragraph("Resume Analysis Report", styles['Title']))
+        story.append(Spacer(1, 12))
+        
+        # Date
+        story.append(Paragraph(f"Generated on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}", styles['Normal']))
+        story.append(Spacer(1, 12))
+        
+        # Overview section
+        story.append(Paragraph("Overview", styles['Heading1']))
+        story.append(Paragraph(f"ATS Score: {ats_score}/100", styles['Normal']))
+        story.append(Paragraph(f"Role Match for {selected_role}: {match_percentage:.1f}%", styles['Normal']))
+        story.append(Paragraph(f"Overall Score: {(ats_score + match_percentage) / 2:.1f}/100", styles['Normal']))
+        story.append(Spacer(1, 12))
+        
+        # Skills section
+        story.append(Paragraph("Skills Analysis", styles['Heading1']))
+        if tech_skills:
+            story.append(Paragraph(f"Technical Skills: {', '.join(tech_skills)}", styles['Normal']))
+        if soft_skills:
+            story.append(Paragraph(f"Soft Skills: {', '.join(soft_skills)}", styles['Normal']))
+        if found_keywords:
+            story.append(Paragraph(f"Role-specific Keywords Found: {', '.join(found_keywords)}", styles['Normal']))
+        story.append(Spacer(1, 12))
+        
+        # Recommendations
+        story.append(Paragraph("Recommendations", styles['Heading1']))
+        recommendations = []
+        
+        if ats_score < 70:
+            recommendations.extend([
+                "• Add more bullet points to improve readability",
+                "• Include contact information (email, phone)",
+                "• Ensure all major sections are present"
+            ])
+        
+        if match_percentage < 60:
+            recommendations.append(f"• Include more {selected_role}-specific keywords")
+        
+        for rec in recommendations:
+            story.append(Paragraph(rec, styles['Normal']))
+        
+        # Build PDF
+        doc.build(story)
+        buffer.seek(0)
+        return buffer
+
 def main():
     st.set_page_config(
         page_title="AI Resume Analyzer",
@@ -347,6 +405,12 @@ def main():
             # Process the resume
             st.success("✅ Resume uploaded and processed successfully!")
             
+            # Extract data for analysis
+            sections = analyzer.extract_sections(text)
+            tech_skills, soft_skills = analyzer.extract_skills(text)
+            found_keywords, match_percentage = analyzer.keyword_matching(text, selected_role)
+            ats_score = analyzer.calculate_ats_score(text, sections)
+            
             # Create tabs for different analyses
             tab1, tab2, tab3, tab4, tab5 = st.tabs([
                 "📊 Overview", "🎯 Skills Analysis", "📝 Section Breakdown", 
@@ -365,9 +429,6 @@ def main():
                     
                     st.metric("Word Count", word_count)
                     st.metric("Character Count", char_count)
-                    
-                    # Extract sections
-                    sections = analyzer.extract_sections(text)
                     st.metric("Sections Found", len([s for s in sections.values() if s]))
                 
                 with col2:
@@ -391,9 +452,6 @@ def main():
             with tab2:
                 st.header("Skills Analysis")
                 
-                # Extract skills
-                tech_skills, soft_skills = analyzer.extract_skills(text)
-                
                 col1, col2 = st.columns(2)
                 
                 with col1:
@@ -405,7 +463,7 @@ def main():
                         st.info("No technical skills detected")
                 
                 with col2:
-                    st.subheader("🤝 Soft Skills")
+                    st.subheader("🤝 Soft Skills")  
                     if soft_skills:
                         for skill in soft_skills:
                             st.badge(skill, type="primary")
@@ -414,7 +472,6 @@ def main():
                 
                 # Job role matching
                 st.subheader(f"🎯 Match Analysis for {selected_role}")
-                found_keywords, match_percentage = analyzer.keyword_matching(text, selected_role)
                 
                 # Progress bar for match percentage
                 st.metric("Match Percentage", f"{match_percentage:.1f}%")
@@ -435,8 +492,6 @@ def main():
             with tab3:
                 st.header("Section Breakdown")
                 
-                sections = analyzer.extract_sections(text)
-                
                 for section_name, content in sections.items():
                     if content:
                         with st.expander(f"📋 {section_name.title()} Section"):
@@ -447,9 +502,6 @@ def main():
             with tab4:
                 st.header("ATS Analysis")
                 
-                # Calculate ATS score
-                ats_score = analyzer.calculate_ats_score(text, sections)
-                
                 col1, col2 = st.columns(2)
                 
                 with col1:
@@ -566,9 +618,13 @@ def main():
                 
                 # Generate downloadable report
                 st.subheader("📄 Download Report")
-                if st.button("Generate PDF Report"):
-                    # Create a simple text report (in real implementation, use ReportLab)
-                    report_content = f"""
+                
+                col1, col2 = st.columns(2)
+                
+                with col1:
+                    # Text report
+                    if st.button("Generate Text Report"):
+                        report_content = f"""
 RESUME ANALYSIS REPORT
 Generated on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
 
@@ -578,7 +634,7 @@ OVERVIEW:
 - Overall Score: {overall_score:.1f}/100
 
 PERSONA SUMMARY:
-{persona_summary}
+{analyzer.generate_persona_summary(text, sections)}
 
 TECHNICAL SKILLS FOUND:
 {', '.join(tech_skills) if tech_skills else 'None detected'}
@@ -594,14 +650,33 @@ STRENGTHS:
 
 AREAS FOR IMPROVEMENT:
 {chr(10).join(f'- {i}' for i in improvements)}
-                    """
-                    
-                    st.download_button(
-                        label="📥 Download Report",
-                        data=report_content,
-                        file_name=f"resume_analysis_report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.txt",
-                        mime="text/plain"
-                    )
+                        """
+                        
+                        st.download_button(
+                            label="📥 Download Text Report",
+                            data=report_content,
+                            file_name=f"resume_analysis_report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.txt",
+                            mime="text/plain"
+                        )
+                
+                with col2:
+                    # PDF report
+                    if st.button("Generate PDF Report"):
+                        try:
+                            pdf_buffer = analyzer.create_pdf_report(
+                                text, sections, ats_score, match_percentage, 
+                                selected_role, tech_skills, soft_skills, found_keywords
+                            )
+                            
+                            st.download_button(
+                                label="📄 Download PDF Report",
+                                data=pdf_buffer.getvalue(),
+                                file_name=f"resume_analysis_report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pdf",
+                                mime="application/pdf"
+                            )
+                        except Exception as e:
+                            st.error(f"Error generating PDF: {str(e)}")
+                            st.info("PDF generation requires all ReportLab dependencies. Using text report as fallback.")
         else:
             st.error("❌ Error processing the uploaded file. Please try a different file.")