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"\n{indent}• {project['name']}:"] # Add description with proper indentation description_lines = project['description'].split('. ') response.extend([f"{indent} {line.strip()}." for line in description_lines]) # Add technologies with proper line break if 'skills_used' in project: response.append(f"\n{indent} Technologies: {', '.join(project['skills_used'])}") # Add status and notes if 'status' in project: status = project['status'] if 'development' in status.lower() or 'progress' in status.lower(): response.append(f"\n{indent} Status: {status}") if 'confidentiality_note' in project: response.append(f"{indent} Note: {project['confidentiality_note']}") return '\n'.join(response) + '\n' def analyze_job_requirements(text: str, knowledge_base: dict) -> Dict[str, List[str]]: """Analyze job requirements and match with skills""" text_lower = text.lower() # Common ML/AI related keywords tech_keywords = { 'machine learning': ['ml', 'machine learning', 'deep learning', 'neural networks'], 'data science': ['data science', 'data analysis', 'analytics', 'statistics'], 'programming': ['python', 'sql', 'programming', 'coding'], 'tools': ['tableau', 'powerbi', 'visualization', 'git'], 'cloud': ['aws', 'azure', 'cloud', 'deployment'] } # Extract matches from knowledge base matches = {category: [] for category in tech_keywords} my_skills = { skill.lower() for skill_type in knowledge_base['skills']['technical_skills'].values() for skill_list in skill_type.values() for skill in skill_list } # Find matching skills in each category for category, keywords in tech_keywords.items(): for keyword in keywords: if keyword in text_lower and any(skill in keyword or keyword in skill for skill in my_skills): matches[category].append(keyword) return matches def handle_perspective_query(query: str, knowledge_base: dict) -> str: """Handle philosophical or perspective-based queries""" query_lower = query.lower() perspectives = knowledge_base.get('perspectives', {}) # Market-related queries if any(word in query_lower for word in ['market', 'opportunity', 'job', 'hiring']): if any(word in query_lower for word in ['down', 'bad', 'difficult', 'tough']): response_parts = [ "• My Perspective on the Current Market:", f" {perspectives['market_outlook']['job_market']}", "", "• My Strategic Approach:", f" {perspectives['market_outlook']['strategy']}", "", "• My Unique Value Proposition:", f" {perspectives['market_outlook']['value_proposition']}" ] return '\n'.join(response_parts) # Learning and growth queries elif any(word in query_lower for word in ['learn', 'study', 'growth']): return f"• My Learning Philosophy:\n {perspectives['learning_philosophy']}" # Handle non-portfolio queries gracefully return knowledge_base['common_queries']['general'] def generate_response(query: str, knowledge_base: dict) -> str: """Generate enhanced responses using the knowledge base""" query_lower = query.lower() # Handle project listing requests if any(word in query_lower for word in ['list', 'project', 'portfolio', 'built', 'created', 'developed']): response_parts = ["Here are my key projects:"] # Major Projects (under development) response_parts.append("\nMajor Projects (In Development):") for project in knowledge_base['projects']['major_projects']: response_parts.append(format_project_response(project, indent_level=1)) # Algorithm Implementation Projects response_parts.append("\nCompleted 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) # Handle job description analysis elif len(query.split()) > 20 and any(phrase in query_lower for phrase in ['requirements', 'qualifications', 'looking for', 'job description']): matches = analyze_job_requirements(query, knowledge_base) relevant_projects = find_relevant_projects(query, knowledge_base['projects']['major_projects']) response_parts = ["Based on the job requirements, here's how my profile aligns:\n"] # Technical Skills Match if any(matches.values()): response_parts.append("• Technical Skills Alignment:") for category, skills in matches.items(): if skills: response_parts.append(f" - Strong {category} skills: {', '.join(skills)}") response_parts.append("") # Project Experience if relevant_projects: response_parts.append("• Relevant Project Experience:") for project in relevant_projects: desc = f" - {project['name']}: {project['description']}" response_parts.append(desc) response_parts.append("") # Education and Background response_parts.extend([ "• Education and Background:", " - Advanced AI/ML education in Canada", " - Unique commerce background providing business perspective", " - Strong foundation in practical ML implementation", "" ]) return '\n'.join(response_parts) # Handle perspective/philosophical queries elif any(word in query_lower for word in ['market', 'think', 'believe', 'opinion', 'weather']): return handle_perspective_query(query, knowledge_base) # Handle story/background queries elif any(word in query_lower for word in ['background', 'journey', 'story', 'transition']): return format_story_response(knowledge_base) # Default response return format_default_response(knowledge_base) def main(): st.title("🤖 Meet Rini - AI-Powered Insights on Manyue's World") # 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 there! I'm Rini, Manyue's AI-powered assistant. I'm here to represent Manyue and share insights about: - My journey from commerce to ML/AI - My technical skills and projects - My fit for ML/AI roles - My perspective on the tech industry - You can also paste job descriptions to see how my profile matches! """) st.session_state.displayed_welcome = True # Create two columns with chat history in scrollable container col1, col2 = st.columns([3, 1]) with col1: # Chat container for better scrolling chat_container = st.container() with chat_container: for message in st.session_state.messages: with st.chat_message(message["role"]): st.markdown(message["content"]) # Chat input at bottom 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.subheader("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?", "Your view on the current market?" ] for question in example_questions: if st.button(question): st.session_state.messages.append({"role": "user", "content": question}) st.rerun() st.markdown("---") if st.button("Clear Chat"): st.session_state.messages = [] st.rerun() if __name__ == "__main__": main()