[Init] Setup

resume_agent.py

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+import json
+import re
+from typing import Dict, List, Optional, Tuple
+from dataclasses import dataclass
+from abc import ABC, abstractmethod
+import google.generativeai as genai
+from datetime import datetime
+
+# Configure Gemini API
+genai.configure(api_key="YOUR_GEMINI_API_KEY")
+
+@dataclass
+class ResumeData:
+    """Data structure to hold resume information"""
+    personal_info: Dict
+    summary: str
+    experiences: List[Dict]
+    skills: List[str]
+    education: List[Dict]
+    raw_text: str
+
+@dataclass
+class JobDescription:
+    """Data structure for job descriptions"""
+    title: str
+    company: str
+    description: str
+    requirements: List[str]
+    keywords: List[str]
+
+class Agent(ABC):
+    """Base agent class"""
+    
+    def __init__(self, model_name: str = "gemini-1.5-flash"):
+        self.model = genai.GenerativeModel(model_name)
+    
+    @abstractmethod
+    def execute(self, *args, **kwargs):
+        pass
+    
+    def generate_response(self, prompt: str) -> str:
+        """Generate response using Gemini"""
+        try:
+            response = self.model.generate_content(prompt)
+            return response.text
+        except Exception as e:
+            return f"Error generating response: {str(e)}"
+
+class SummaryAgent(Agent):
+    """Agent responsible for creating compelling professional summaries"""
+    
+    def execute(self, resume_data: ResumeData, job_desc: Optional[JobDescription] = None) -> str:
+        context = f"""
+        Personal Info: {resume_data.personal_info}
+        Experience: {resume_data.experiences}
+        Skills: {resume_data.skills}
+        Education: {resume_data.education}
+        """
+        
+        job_context = ""
+        if job_desc:
+            job_context = f"""
+            Target Job: {job_desc.title} at {job_desc.company}
+            Job Requirements: {job_desc.requirements}
+            """
+        
+        prompt = f"""
+        Create a compelling professional summary (2-3 sentences) based on this resume information:
+        {context}
+        
+        {job_context}
+        
+        Guidelines:
+        - Highlight unique value proposition
+        - Use action-oriented language
+        - Focus on achievements and impact
+        - Keep it concise and engaging
+        - If job description provided, align with role requirements
+        
+        Return only the professional summary text.
+        """
+        
+        return self.generate_response(prompt)
+
+class ExperienceMatchingAgent(Agent):
+    """Agent for matching experiences to job descriptions"""
+    
+    def execute(self, resume_data: ResumeData, job_desc: JobDescription) -> List[Dict]:
+        experiences_text = json.dumps(resume_data.experiences, indent=2)
+        
+        prompt = f"""
+        Analyze these work experiences and rank them by relevance to the target job:
+        
+        EXPERIENCES:
+        {experiences_text}
+        
+        TARGET JOB:
+        Title: {job_desc.title}
+        Company: {job_desc.company}
+        Description: {job_desc.description}
+        Requirements: {job_desc.requirements}
+        
+        For each experience, provide:
+        1. Relevance score (1-10)
+        2. Key matching points
+        3. Suggested improvements for better alignment
+        4. Recommended order for resume
+        
+        Return as JSON format:
+        {{
+            "ranked_experiences": [
+                {{
+                    "original_experience": {{...}},
+                    "relevance_score": 8,
+                    "matching_points": ["point1", "point2"],
+                    "suggested_improvements": ["improvement1", "improvement2"],
+                    "recommended_position": 1
+                }}
+            ]
+        }}
+        """
+        
+        response = self.generate_response(prompt)
+        try:
+            return json.loads(response)
+        except json.JSONDecodeError:
+            return {"error": "Failed to parse experience matching results"}
+
+class KeywordOptimizationAgent(Agent):
+    """Agent for optimizing ATS keywords"""
+    
+    def execute(self, resume_data: ResumeData, job_desc: JobDescription) -> Dict:
+        prompt = f"""
+        Analyze the resume and job description to optimize ATS keywords:
+        
+        RESUME CONTENT:
+        Summary: {resume_data.summary}
+        Skills: {resume_data.skills}
+        Experiences: {json.dumps(resume_data.experiences)}
+        
+        JOB DESCRIPTION:
+        {job_desc.description}
+        Requirements: {job_desc.requirements}
+        
+        Provide:
+        1. Missing critical keywords from job description
+        2. Keyword density analysis
+        3. Suggested keyword placements
+        4. Industry-specific terms to include
+        5. ATS optimization score (1-100)
+        
+        Return as JSON:
+        {{
+            "missing_keywords": ["keyword1", "keyword2"],
+            "current_keyword_density": {{"keyword": "frequency"}},
+            "suggested_placements": [
+                {{
+                    "keyword": "Python",
+                    "sections": ["skills", "experience"],
+                    "context": "Add to technical skills section"
+                }}
+            ],
+            "industry_terms": ["term1", "term2"],
+            "ats_score": 75,
+            "recommendations": ["rec1", "rec2"]
+        }}
+        """
+        
+        response = self.generate_response(prompt)
+        try:
+            return json.loads(response)
+        except json.JSONDecodeError:
+            return {"error": "Failed to parse keyword optimization results"}
+
+class DesignAgent(Agent):
+    """Agent for design and formatting suggestions"""
+    
+    def execute(self, resume_data: ResumeData, job_desc: Optional[JobDescription] = None) -> Dict:
+        industry = job_desc.title.split()[0] if job_desc else "General"
+        
+        prompt = f"""
+        Suggest design and formatting improvements for a {industry} professional's resume:
+        
+        CURRENT RESUME STRUCTURE:
+        - Personal Info: {len(resume_data.personal_info)} fields
+        - Experiences: {len(resume_data.experiences)} positions
+        - Skills: {len(resume_data.skills)} skills listed
+        - Education: {len(resume_data.education)} entries
+        
+        Consider:
+        1. Industry standards for {industry}
+        2. ATS-friendly formatting
+        3. Visual hierarchy and readability
+        4. Professional appearance
+        5. Length optimization
+        
+        Return JSON with:
+        {{
+            "recommended_template": "template_name",
+            "layout_suggestions": ["suggestion1", "suggestion2"],
+            "formatting_rules": ["rule1", "rule2"],
+            "color_scheme": "color_description",
+            "typography": "font_recommendations",
+            "sections_order": ["section1", "section2", "section3"],
+            "design_tips": ["tip1", "tip2"]
+        }}
+        """
+        
+        response = self.generate_response(prompt)
+        try:
+            return json.loads(response)
+        except json.JSONDecodeError:
+            return {"error": "Failed to parse design suggestions"}
+
+class EditingAgent(Agent):
+    """Agent for grammar, punctuation, and content improvement"""
+    
+    def execute(self, text: str) -> Dict:
+        prompt = f"""
+        Analyze this resume text for improvements:
+        
+        TEXT TO REVIEW:
+        {text}
+        
+        Check for:
+        1. Grammar and punctuation errors
+        2. Clarity and conciseness
+        3. Action verb usage
+        4. Quantifiable achievements
+        5. Professional tone
+        6. Consistency in formatting
+        
+        Return JSON:
+        {{
+            "grammar_errors": [
+                {{
+                    "original": "original text",
+                    "corrected": "corrected text",
+                    "explanation": "reason for change"
+                }}
+            ],
+            "clarity_improvements": [
+                {{
+                    "original": "original text",
+                    "improved": "improved text",
+                    "reason": "why it's better"
+                }}
+            ],
+            "action_verb_suggestions": ["verb1", "verb2"],
+            "quantification_opportunities": ["opportunity1", "opportunity2"],
+            "overall_score": 85,
+            "summary_feedback": "Overall assessment"
+        }}
+        """
+        
+        response = self.generate_response(prompt)
+        try:
+            return json.loads(response)
+        except json.JSONDecodeError:
+            return {"error": "Failed to parse editing suggestions"}
+
+class ResumeAgent:
+    """Main orchestrating agent that coordinates all sub-agents"""
+    
+    def __init__(self):
+        self.summary_agent = SummaryAgent()
+        self.experience_agent = ExperienceMatchingAgent()
+        self.keyword_agent = KeywordOptimizationAgent()
+        self.design_agent = DesignAgent()
+        self.editing_agent = EditingAgent()
+    
+    def parse_resume(self, resume_text: str) -> ResumeData:
+        """Simple resume parsing - can be enhanced with proper NLP"""
+        # This is a simplified parser - in production, you'd use more sophisticated parsing
+        lines = resume_text.split('\n')
+        
+        # Extract basic sections (this is a simplified implementation)
+        personal_info = {"name": "John Doe", "email": "john@email.com"}  # Placeholder
+        summary = ""
+        experiences = []
+        skills = []
+        education = []
+        
+        # Simple pattern matching (enhance as needed)
+        current_section = None
+        for line in lines:
+            line = line.strip()
+            if re.match(r'(summary|profile|objective)', line.lower()):
+                current_section = 'summary'
+            elif re.match(r'(experience|work|employment)', line.lower()):
+                current_section = 'experience'
+            elif re.match(r'(skills|technical)', line.lower()):
+                current_section = 'skills'
+            elif re.match(r'(education|academic)', line.lower()):
+                current_section = 'education'
+            elif line and current_section:
+                if current_section == 'summary':
+                    summary += line + " "
+                elif current_section == 'skills':
+                    skills.extend([skill.strip() for skill in line.split(',')])
+        
+        return ResumeData(
+            personal_info=personal_info,
+            summary=summary.strip(),
+            experiences=experiences,
+            skills=skills,
+            education=education,
+            raw_text=resume_text
+        )
+    
+    def optimize_resume(self, resume_text: str, job_description: Optional[str] = None) -> Dict:
+        """Main method to optimize resume using all agents"""
+        
+        # Parse resume
+        resume_data = self.parse_resume(resume_text)
+        
+        # Parse job description if provided
+        job_desc = None
+        if job_description:
+            job_desc = JobDescription(
+                title="Target Position",
+                company="Target Company",
+                description=job_description,
+                requirements=[req.strip() for req in job_description.split('.') if req.strip()],
+                keywords=[]
+            )
+        
+        results = {
+            "timestamp": datetime.now().isoformat(),
+            "original_resume": resume_data.__dict__,
+        }
+        
+        # Generate new summary
+        print("🔄 Generating compelling summary...")
+        results["new_summary"] = self.summary_agent.execute(resume_data, job_desc)
+        
+        # Match experiences to job
+        if job_desc:
+            print("🔄 Analyzing experience relevance...")
+            results["experience_matching"] = self.experience_agent.execute(resume_data, job_desc)
+            
+            print("🔄 Optimizing keywords for ATS...")
+            results["keyword_optimization"] = self.keyword_agent.execute(resume_data, job_desc)
+        
+        # Design suggestions
+        print("🔄 Generating design recommendations...")
+        results["design_suggestions"] = self.design_agent.execute(resume_data, job_desc)
+        
+        # Edit and improve
+        print("🔄 Analyzing content for improvements...")
+        results["editing_suggestions"] = self.editing_agent.execute(resume_text)
+        
+        return results
+
+# File handling utilities
+def read_file(file_path: str) -> str:
+    """Read content from a file"""
+    try:
+        with open(file_path, 'r', encoding='utf-8') as file:
+            return file.read()
+    except FileNotFoundError:
+        print(f"❌ File not found: {file_path}")
+        return ""
+    except Exception as e:
+        print(f"❌ Error reading file: {str(e)}")
+        return ""
+
+def get_sample_resume() -> str:
+    """Return sample resume text"""
+    return """
+    John Doe
+    Software Engineer
+    john.doe@email.com
+    (555) 123-4567
+    
+    SUMMARY
+    Experienced software developer with 5 years in web development and system design.
+    
+    EXPERIENCE
+    Software Developer at TechCorp (2019-2024)
+    - Developed web applications using Python and JavaScript
+    - Worked with databases and APIs
+    - Collaborated with team members on agile projects
+    - Maintained code quality and performed code reviews
+    
+    Senior Developer Intern at StartupXYZ (2018-2019)
+    - Built responsive web interfaces using React
+    - Integrated third-party APIs and services
+    - Participated in daily standups and sprint planning
+    
+    SKILLS
+    Python, JavaScript, React, SQL, Git, Docker, AWS, REST APIs
+    
+    EDUCATION
+    BS Computer Science, University XYZ (2019)
+    GPA: 3.7/4.0
+    """
+
+def get_sample_job_description() -> str:
+    """Return sample job description"""
+    return """
+    Senior Python Developer position at InnovaTech
+    
+    We are looking for an experienced Python developer with expertise in Django, 
+    REST APIs, database optimization, and cloud technologies. The ideal candidate 
+    should have 3+ years of experience, strong problem-solving skills, and 
+    experience with AWS or Azure.
+    
+    Requirements:
+    - 3+ years of Python development experience
+    - Strong knowledge of Django framework
+    - Experience with REST API development
+    - Database design and optimization skills
+    - Cloud platform experience (AWS/Azure)
+    - Git version control
+    - Agile development methodology
+    - Strong communication skills
+    """
+
+# Example usage and testing
+def main():
+    """Main function with file upload capability"""
+    
+    print("🚀 AI Resume Optimization Agent")
+    print("=" * 50)
+    
+    # Get resume content
+    resume_file = input("📄 Enter resume file path (or press Enter for sample): ").strip()
+    if resume_file and resume_file != "":
+        resume_text = read_file(resume_file)
+        if not resume_text:
+            print("📄 Using sample resume instead...")
+            resume_text = get_sample_resume()
+    else:
+        print("📄 Using sample resume...")
+        resume_text = get_sample_resume()
+    
+    # Get job description
+    job_file = input("💼 Enter job description file path (or press Enter for sample): ").strip()
+    if job_file and job_file != "":
+        job_description = read_file(job_file)
+        if not job_description:
+            print("💼 Using sample job description instead...")
+            job_description = get_sample_job_description()
+    else:
+        print("💼 Using sample job description...")
+        job_description = get_sample_job_description()
+    
+    # Initialize the agent
+    agent = ResumeAgent()
+    
+    print("\n🔄 Starting Resume Optimization...")
+    print("=" * 50)
+    
+    # Optimize resume
+    results = agent.optimize_resume(resume_text, job_description)
+    
+    print("\n✅ Optimization Complete!")
+    print("=" * 50)
+    
+    # Display results
+    print(f"\n📝 NEW SUMMARY:")
+    print(results.get("new_summary", ""))
+    
+    if "keyword_optimization" in results:
+        keyword_data = results["keyword_optimization"]
+        if isinstance(keyword_data, dict) and "ats_score" in keyword_data:
+            print(f"\n🎯 ATS SCORE: {keyword_data['ats_score']}/100")
+    
+    if "design_suggestions" in results:
+        design_data = results["design_suggestions"]
+        if isinstance(design_data, dict) and "recommended_template" in design_data:
+            print(f"\n🎨 RECOMMENDED TEMPLATE: {design_data['recommended_template']}")
+    
+    print(f"\n📊 ANALYSIS COMPLETE")
+    print(f"Full results saved with timestamp: {results['timestamp']}")
+    
+    # Save results to file
+    output_file = f"resume_optimization_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
+    try:
+        with open(output_file, 'w', encoding='utf-8') as f:
+            json.dump(results, f, indent=2, default=str)
+        print(f"💾 Results saved to: {output_file}")
+    except Exception as e:
+        print(f"❌ Error saving results: {str(e)}")
+    
+    return results
+
+if __name__ == "__main__":
+    # Note: Replace "YOUR_GEMINI_API_KEY" with your actual API key
+    main()