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| import streamlit as st | |
| import json | |
| from typing import Dict, List, Any | |
| # Initialize Streamlit page configuration | |
| st.set_page_config( | |
| page_title="Manyue's Portfolio Chatbot", | |
| page_icon="🤖", | |
| layout="wide" | |
| ) | |
| # Helper functions for formatting responses | |
| def get_project_details(project: dict) -> str: | |
| """Format project details in a clear, structured way""" | |
| return ( | |
| f"• {project['name']}\n" | |
| f" Description: {project['description']}\n" | |
| f" Skills: {', '.join(project['skills_used'])}\n" | |
| f" Status: {project['status']}" | |
| ) | |
| def get_skills_by_category(knowledge_base: dict) -> Dict[str, List[str]]: | |
| """Organize skills by category with examples""" | |
| skills = knowledge_base['skills'] | |
| projects = knowledge_base['professional_experience']['projects'] | |
| skill_examples = {} | |
| for skill in skills['technical_skills']: | |
| related_projects = [p['name'] for p in projects | |
| if skill.lower() in [s.lower() for s in p['skills_used']]] | |
| if related_projects: | |
| skill_examples[skill] = related_projects[0] | |
| return skill_examples | |
| def format_story_response(knowledge_base: dict) -> str: | |
| """Format the background story in a clear, structured way""" | |
| education = knowledge_base['education'] | |
| story = [ | |
| "Here's my journey from commerce to ML/AI:", | |
| "• Education Background:", | |
| f" - Graduated with a Commerce degree from {education['Undergraduate'][0]['institution']}", | |
| "• Career Transition:", | |
| " - Started as a Programmer Trainee at Cognizant despite no prior coding experience", | |
| " - Excelled in development roles and discovered passion for technology", | |
| "• Current Path:", | |
| f" - Pursuing {education['postgraduate'][0]['course_name']} at {education['postgraduate'][0]['institution']}", | |
| f" - Also enrolled in {education['postgraduate'][1]['course_name']} at {education['postgraduate'][1]['institution']}", | |
| "• Goal:", | |
| " - Combining business acumen with ML/AI expertise to create impactful solutions" | |
| ] | |
| return '\n'.join(story) | |
| def format_project_list(knowledge_base: dict) -> str: | |
| """Format project list in a clear, structured way""" | |
| projects = knowledge_base['professional_experience']['projects'] | |
| response = ["My Portfolio Projects:"] | |
| for project in projects: | |
| response.extend([ | |
| f"\n{project['name']}", | |
| f"• Description: {project['description']}", | |
| f"• Technologies: {', '.join(project['skills_used'])}", | |
| f"• Current Status: {project['status']}", | |
| "---" | |
| ]) | |
| return '\n'.join(response) | |
| def format_standout_qualities(knowledge_base: dict) -> str: | |
| """Format standout qualities in a clear, structured way""" | |
| qualities = [ | |
| "What Makes Me Stand Out:", | |
| "\n1. Unique Background", | |
| " • Successfully transitioned from commerce to tech", | |
| " • Bring both business acumen and technical expertise", | |
| "\n2. Practical Experience", | |
| f" • Developed {len(knowledge_base['professional_experience']['projects'])} significant ML projects", | |
| " • Real-world implementation experience from Cognizant", | |
| "\n3. Canadian Education", | |
| " • Advanced AI/ML education in Canada", | |
| " • Up-to-date with latest industry practices", | |
| "\n4. Technical Expertise", | |
| f" • Strong foundation in {', '.join(knowledge_base['skills']['technical_skills'][:3])}", | |
| " • Hands-on experience with ML model deployment", | |
| "\n5. Business Perspective", | |
| " • Understanding of both technical and business requirements", | |
| " • Can bridge gap between technical and business teams" | |
| ] | |
| return '\n'.join(qualities) | |
| def analyze_job_description(text: str, knowledge_base: dict) -> dict: | |
| """Analyze job description and match with candidate's profile""" | |
| text_lower = text.lower() | |
| # Extract key skills from knowledge base | |
| my_skills = set(s.lower() for s in knowledge_base['skills']['technical_skills']) | |
| # Common ML/AI job related keywords | |
| ml_keywords = { | |
| 'machine learning', 'deep learning', 'artificial intelligence', 'ai', 'ml', | |
| 'neural networks', 'nlp', 'computer vision', 'data science', | |
| 'python', 'pytorch', 'tensorflow', 'scikit-learn' | |
| } | |
| # Find mentioned skills in JD | |
| found_skills = [] | |
| for skill in my_skills: | |
| if skill in text_lower: | |
| found_skills.append(skill) | |
| # Find relevant projects | |
| relevant_projects = [] | |
| for project in knowledge_base['professional_experience']['projects']: | |
| project_skills = set(s.lower() for s in project['skills_used']) | |
| if any(skill in text_lower for skill in project_skills): | |
| relevant_projects.append(project) | |
| return { | |
| 'matching_skills': found_skills, | |
| 'relevant_projects': relevant_projects[:2], | |
| 'is_ml_role': any(keyword in text_lower for keyword in ml_keywords) | |
| } | |
| 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']): | |
| return format_project_list(knowledge_base) | |
| # Handle background/journey queries | |
| elif any(word in query_lower for word in ['background', 'journey', 'story', 'transition']): | |
| return format_story_response(knowledge_base) | |
| # Handle standout/unique qualities queries | |
| elif any(word in query_lower for word in ['stand out', 'unique', 'different', 'special']): | |
| return format_standout_qualities(knowledge_base) | |
| # Handle job descriptions or role requirements | |
| elif len(query.split()) > 20 or any(phrase in query_lower for phrase in | |
| ['requirements', 'qualifications', 'looking for', 'job description', 'responsibilities']): | |
| analysis = analyze_job_description(query, knowledge_base) | |
| if analysis['is_ml_role']: | |
| response_parts = [] | |
| response_parts.append("Based on the job description, here's how my profile aligns:") | |
| if analysis['matching_skills']: | |
| response_parts.append(f"\n• Technical Skills Match:\n - I have experience with: {', '.join(analysis['matching_skills'])}") | |
| if analysis['relevant_projects']: | |
| project = analysis['relevant_projects'][0] | |
| response_parts.append(f"\n• Relevant Project Experience:\n - {project['name']}: {project['description']}") | |
| response_parts.append("\n• Additional Qualifications:\n - Advanced AI/ML education in Canada\n - Unique background combining business and technical expertise") | |
| return '\n'.join(response_parts) | |
| # Handle specific skill queries | |
| elif any(word in query_lower for word in ['skill', 'know', 'experience', 'expert']): | |
| skill_examples = get_skills_by_category(knowledge_base) | |
| response = ["My Technical Skills:"] | |
| for skill, project in skill_examples.items(): | |
| response.append(f"• {skill} - Applied in {project}") | |
| return '\n'.join(response) | |
| # Default response | |
| return (f"I'm {knowledge_base['personal_details']['full_name']}, " | |
| f"{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!") | |
| # Load and cache knowledge base | |
| def load_knowledge_base(): | |
| try: | |
| with open('knowledge_base.json', 'r', encoding='utf-8') as f: | |
| return json.load(f) | |
| except FileNotFoundError: | |
| st.error("Knowledge base file not found.") | |
| return {} | |
| def initialize_session_state(): | |
| """Initialize session state variables""" | |
| if "messages" not in st.session_state: | |
| st.session_state.messages = [] | |
| if "knowledge_base" not in st.session_state: | |
| st.session_state.knowledge_base = load_knowledge_base() | |
| def main(): | |
| st.title("💬 Chat with Manyue's Portfolio") | |
| 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, and I'll show how my profile matches! | |
| """) | |
| # Initialize session state | |
| initialize_session_state() | |
| # Create two columns | |
| col1, col2 = st.columns([3, 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 about Manyue's experience or paste a job description..."): | |
| # Add user message | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| with st.chat_message("user"): | |
| st.markdown(prompt) | |
| # 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}) | |
| with col2: | |
| st.subheader("Quick Questions") | |
| example_questions = [ | |
| "Tell me about your ML projects", | |
| "What are your technical skills?", | |
| "Why should we hire you as an ML Engineer?", | |
| "What's your journey into ML?", | |
| "Paste a job description to see how I match!" | |
| ] | |
| for question in example_questions: | |
| if st.button(question): | |
| st.session_state.messages.append({"role": "user", "content": question}) | |
| st.experimental_rerun() | |
| st.markdown("---") | |
| if st.button("Clear Chat"): | |
| st.session_state.messages = [] | |
| st.experimental_rerun() | |
| if __name__ == "__main__": | |
| main() |