import streamlit as st import json from typing import Dict, List, Any import re def format_project_response(project: dict, indent_level: int = 0) -> str: """Format project details with proper indentation and spacing""" indent = " " * indent_level response = [f"{indent}• {project['name']}"] response.append(f"{indent} {project['description']}") if 'skills_used' in project: response.append(f"{indent} Technologies: {', '.join(project['skills_used'])}") if 'status' in project: status = project['status'] if 'development' in status.lower() or 'progress' in status.lower(): response.append(f"{indent} Status: {status}") if 'confidentiality_note' in project: response.append(f"{indent} Note: {project['confidentiality_note']}") return '\n'.join(response) + '\n' def format_skills_response(skills: dict) -> str: """Format skills with proper hierarchy and spacing""" response = ["My Technical Expertise:\n"] categories = { 'Machine Learning & AI': ('machine_learning', ['core', 'frameworks', 'focus_areas']), 'Programming': ('programming', ['primary', 'libraries', 'tools']), 'Data & Analytics': ('data', ['databases', 'visualization', 'processing']) } for category, (dict_key, subcategories) in categories.items(): response.append(f"• {category}") if dict_key in skills: for subcat in subcategories: if subcat in skills[dict_key]: items = skills[dict_key][subcat] response.append(f" - {subcat.title()}: {', '.join(items)}") response.append("") return '\n'.join(response) def analyze_job_description(text: str, knowledge_base: dict) -> str: """Analyze job description and provide detailed alignment""" # Extract key requirements requirements = { 'technical_tools': set(), 'soft_skills': set(), 'responsibilities': set() } # Common technical tools and skills tech_keywords = { 'data science', 'analytics', 'visualization', 'tableau', 'python', 'machine learning', 'modeling', 'automation', 'sql', 'data analysis' } # Common soft skills soft_keywords = { 'collaborate', 'communicate', 'analyze', 'design', 'implement', 'produce insights', 'improve', 'support' } text_lower = text.lower() # Extract company name if present companies = ['rbc', 'shopify', 'google', 'microsoft', 'amazon'] company_name = next((company.upper() for company in companies if company in text_lower), None) # Extract requirements for word in tech_keywords: if word in text_lower: requirements['technical_tools'].add(word) for word in soft_keywords: if word in text_lower: requirements['soft_skills'].add(word) # Build response response_parts = [] # Company-specific introduction if applicable if company_name: response_parts.append(f"Here's how I align with {company_name}'s requirements:\n") else: response_parts.append("Based on the job requirements, here's how I align:\n") # Technical Skills Alignment response_parts.append("• Technical Skills Match:") my_relevant_skills = [] if 'visualization' in requirements['technical_tools'] or 'tableau' in requirements['technical_tools']: my_relevant_skills.append(" - Proficient in Tableau and data visualization (used in multiple projects)") if 'data analysis' in requirements['technical_tools']: my_relevant_skills.append(" - Strong data analysis skills demonstrated in projects like LoanTap Credit Assessment") if 'machine learning' in requirements['technical_tools'] or 'modeling' in requirements['technical_tools']: my_relevant_skills.append(" - Experienced in building ML models from scratch (demonstrated in algorithm practice projects)") response_parts.extend(my_relevant_skills) response_parts.append("") # Business Understanding response_parts.append("• Business Acumen:") response_parts.append(" - Commerce background provides strong understanding of business requirements") response_parts.append(" - Experience in translating business needs into technical solutions") response_parts.append(" - Proven ability to communicate technical findings to business stakeholders") response_parts.append("") # Project Experience response_parts.append("• Relevant Project Experience:") relevant_projects = [] if 'automation' in requirements['technical_tools']: relevant_projects.append(" - Developed AI-powered POS system with automated operations") if 'data analysis' in requirements['technical_tools']: relevant_projects.append(" - Built credit assessment model for LoanTap using comprehensive data analysis") if 'machine learning' in requirements['technical_tools']: relevant_projects.append(" - Created multiple ML models from scratch, including predictive analytics for Ola") response_parts.extend(relevant_projects) response_parts.append("") # Education and Additional Qualifications response_parts.append("• Additional Strengths:") response_parts.append(" - Currently pursuing advanced AI/ML education in Canada") response_parts.append(" - Strong foundation in both technical implementation and business analysis") response_parts.append(" - Experience in end-to-end project delivery and deployment") return '\n'.join(response_parts) def format_story_response(knowledge_base: dict) -> str: """Format background story with proper structure""" response_parts = ["My Journey from Commerce to ML/AI:\n"] # Education Background response_parts.append("• Education Background:") response_parts.append(f" - Commerce degree from {knowledge_base['education']['undergraduate']['institution']}") response_parts.append(f" - Currently at {knowledge_base['education']['postgraduate'][0]['institution']}") response_parts.append(f" - Also enrolled at {knowledge_base['education']['postgraduate'][1]['institution']}") response_parts.append("") # Career Transition response_parts.append("• Career Transition:") transition = next((qa['answer'] for qa in knowledge_base['frequently_asked_questions'] if 'transition' in qa['question'].lower()), '') response_parts.append(f" - {transition[:200]}...") response_parts.append("") # Current Focus response_parts.append("• Current Focus:") response_parts.append(" - Building practical ML projects") response_parts.append(" - Advancing AI/ML education in Canada") response_parts.append("") # Goals response_parts.append("• Future Goals:") response_parts.append(" - Secure ML Engineering role in Canada") response_parts.append(" - Develop innovative AI solutions") response_parts.append(" - Contribute to cutting-edge ML projects") return '\n'.join(response_parts) def format_standout_response() -> str: """Format response about standout qualities""" response_parts = ["What Makes Me Stand Out:\n"] response_parts.append("• Unique Background:") response_parts.append(" - Successfully transitioned from commerce to tech") response_parts.append(" - Blend of business acumen and technical expertise") response_parts.append("") response_parts.append("• Practical Experience:") response_parts.append(" - Built multiple ML projects from scratch") response_parts.append(" - Focus on real-world applications") response_parts.append("") response_parts.append("• Technical Depth:") response_parts.append(" - Strong foundation in ML/AI principles") response_parts.append(" - Experience with end-to-end project implementation") response_parts.append("") response_parts.append("• Innovation Focus:") response_parts.append(" - Developing novel solutions in ML/AI") response_parts.append(" - Emphasis on practical impact") return '\n'.join(response_parts) def add_relevant_links(response: str, query: str, knowledge_base: dict) -> str: """Add relevant links based on query context""" query_lower = query.lower() links = [] if any(word in query_lower for word in ['project', 'portfolio', 'work']): links.append(f"\nView my complete portfolio: {knowledge_base['personal_details']['online_presence']['portfolio']}") if any(word in query_lower for word in ['background', 'experience', 'work']): links.append(f"\nConnect with me: {knowledge_base['personal_details']['online_presence']['linkedin']}") for post in knowledge_base['personal_details']['online_presence']['blog_posts']: if 'link' in post and any(word in query_lower for word in post['title'].lower().split()): links.append(f"\nRelated blog post: {post['link']}") break if links: response += '\n' + '\n'.join(links) return response import streamlit as st import json from typing import Dict, List, Any import re def handle_market_conditions(knowledge_base: dict) -> str: """Handle market condition related queries with perspective""" market_outlook = knowledge_base['personal_details']['perspectives']['market_outlook'] # Enhanced formatting for better readability response_parts = [ "Here's my perspective on the current market situation:\n", f"• {market_outlook['job_market']}", f"\n• {market_outlook['value_proposition']}", f"\n• {market_outlook['strategy']}" ] return '\n'.join(response_parts) def handle_general_query(query: str, knowledge_base: dict) -> str: """Enhanced handling of general queries""" query_lower = query.lower() # Improved weather-related query detection if any(word in query_lower for word in ['weather', 'temperature', 'climate', 'cold', 'hot', 'warm']): return knowledge_base['personal_details']['common_queries']['weather'] # Enhanced market-related query detection if any(phrase in query_lower for phrase in ['market', 'job market', 'jobs', 'opportunities', 'hiring']): return handle_market_conditions(knowledge_base) # More specific job fit query detection if any(phrase in query_lower for phrase in ['job description', 'job posting', 'job requirement', 'good fit']): return ("Please paste the job description you'd like me to analyze. I'll evaluate how my skills and experience align with the requirements.") # Default to personal summary return knowledge_base['personal_details']['professional_summary'] def generate_response(query: str, knowledge_base: dict) -> str: """Enhanced response generation with improved pattern matching""" query_lower = query.lower() # Enhanced market conditions detection if any(word in query_lower for word in ['market', 'job market', 'hiring']) or \ any(phrase in query_lower for phrase in ['market down', 'market conditions', 'current situation']): return handle_market_conditions(knowledge_base) # Enhanced job description analysis detection if ('job description' in query_lower or 'job posting' in query_lower) or \ (len(query.split()) > 20 and any(word in query_lower for word in ['requirements', 'qualifications', 'looking for', 'responsibilities', 'skills needed'])): if len(query.split()) < 20: return "Please paste the complete job description, and I'll analyze how well I match the requirements." return analyze_job_description(query, knowledge_base) # Enhanced weather query detection if any(word in query_lower for word in ['weather', 'temperature', 'climate', 'cold', 'hot', 'warm']): return handle_general_query(query, knowledge_base) # Existing handlers remain unchanged if any(word in query_lower for word in ['list', 'project', 'portfolio', 'built', 'created', 'developed']): response_parts = ["Here are my key projects:\n"] response_parts.append("Major Projects (In Development):") for project in knowledge_base['projects']['major_projects']: response_parts.append(format_project_response(project, indent_level=1)) response_parts.append("Completed Algorithm Implementation Projects:") for project in knowledge_base['projects']['algorithm_practice_projects']: response_parts.append(format_project_response(project, indent_level=1)) response = '\n'.join(response_parts) return add_relevant_links(response, query, knowledge_base) elif any(word in query_lower for word in ['background', 'journey', 'story', 'transition']): return format_story_response(knowledge_base) elif any(word in query_lower for word in ['skill', 'know', 'technology', 'stack']): return format_skills_response(knowledge_base['skills']['technical_skills']) elif any(word in query_lower for word in ['stand out', 'unique', 'different', 'special']): return format_standout_response() # General query handler for shorter queries elif len(query.split()) < 5: return handle_general_query(query, knowledge_base) # Default response return (f"I'm {knowledge_base['personal_details']['professional_summary']}\n\n" "You can ask me about:\n" "• My projects and portfolio\n" "• My journey from commerce to ML/AI\n" "• My technical skills and experience\n" "• My fit for ML/AI roles\n" "Or paste a job description to see how my profile matches!") def main(): st.title("💬 Chat with Manyue's Portfolio") # Initialize session state if "messages" not in st.session_state: st.session_state.messages = [] if "knowledge_base" not in st.session_state: try: with open('knowledge_base.json', 'r', encoding='utf-8') as f: st.session_state.knowledge_base = json.load(f) except FileNotFoundError: st.error("Knowledge base file not found.") return # Display welcome message if "displayed_welcome" not in st.session_state: st.write(""" Hi! I'm Manyue's AI assistant. I can tell you about: - My journey from commerce to ML/AI - My technical skills and projects - My fit for ML/AI roles - You can also paste job descriptions to see how my profile matches! """) st.session_state.displayed_welcome = True # Create two columns with adjusted ratios col1, col2 = st.columns([4, 1]) with col1: # Display chat messages for message in st.session_state.messages: with st.chat_message(message["role"]): st.markdown(message["content"]) # Chat input if prompt := st.chat_input("Ask me anything or paste a job description..."): # Add user message st.session_state.messages.append({"role": "user", "content": prompt}) try: # Generate and display response with st.chat_message("assistant"): response = generate_response(prompt, st.session_state.knowledge_base) st.markdown(response) st.session_state.messages.append({"role": "assistant", "content": response}) except Exception as e: st.error(f"An error occurred: {str(e)}") st.rerun() with col2: st.markdown("### Quick Questions") example_questions = [ "Tell me about your ML projects", "What are your technical skills?", "What makes you stand out?", "What's your journey into ML?", "Paste a job description to see how I match!" ] for question in example_questions: if st.button(question, key=f"btn_{question}", use_container_width=True): st.session_state.messages.append({"role": "user", "content": question}) try: response = generate_response(question, st.session_state.knowledge_base) st.session_state.messages.append({"role": "assistant", "content": response}) except Exception as e: st.error(f"An error occurred: {str(e)}") st.rerun() st.markdown("---") if st.button("Clear Chat", use_container_width=True): st.session_state.messages = [] st.rerun() if __name__ == "__main__": main()