Spaces:
Sleeping
Sleeping
| import streamlit as st | |
| import json | |
| from typing import Dict, List, Any | |
| import re | |
| def format_project_response(project: dict, include_status: bool = True) -> str: | |
| """Format a project description with proper status handling""" | |
| response = [f"• {project['name']}:"] | |
| response.append(f" - {project['description']}") | |
| if 'skills_used' in project: | |
| response.append(f" - Technologies: {', '.join(project['skills_used'])}") | |
| if include_status and 'status' in project: | |
| if 'development' in project['status'].lower() or 'progress' in project['status'].lower(): | |
| response.append(f" - Currently {project['status']}") | |
| if 'confidentiality_note' in project: | |
| response.append(f" - Note: {project['confidentiality_note']}") | |
| return '\n'.join(response) | |
| def analyze_job_requirements(text: str, knowledge_base: dict) -> Dict[str, List[str]]: | |
| """Analyze job requirements and match with skills""" | |
| text_lower = text.lower() | |
| # Extract skills from knowledge base | |
| my_skills = { | |
| 'technical': [skill.lower() for skill in knowledge_base['skills']['technical_skills']['machine_learning']['core'] + | |
| knowledge_base['skills']['technical_skills']['programming']['primary'] + | |
| knowledge_base['skills']['technical_skills']['data']['databases']], | |
| 'tools': [tool.lower() for tool in knowledge_base['skills']['technical_skills']['programming']['tools'] + | |
| knowledge_base['skills']['technical_skills']['deployment']['web']], | |
| 'soft_skills': [skill['skill'].lower() for skill in knowledge_base['skills']['soft_skills']] | |
| } | |
| # Find matching skills in job description | |
| matches = { | |
| 'technical_matches': [skill for skill in my_skills['technical'] if skill in text_lower], | |
| 'tool_matches': [tool for tool in my_skills['tools'] if tool in text_lower], | |
| 'soft_skill_matches': [skill for skill in my_skills['soft_skills'] if skill in text_lower] | |
| } | |
| return matches | |
| def find_relevant_projects(requirements: str, projects: List[dict]) -> List[dict]: | |
| """Find projects relevant to job requirements""" | |
| req_lower = requirements.lower() | |
| relevant_projects = [] | |
| for project in projects: | |
| # Check if project skills or description match requirements | |
| if any(skill.lower() in req_lower for skill in project['skills_used']) or \ | |
| any(word in project['description'].lower() for word in req_lower.split()): | |
| relevant_projects.append(project) | |
| return relevant_projects[:2] # Return top 2 most relevant projects | |
| def add_relevant_links(response: str, query: str, knowledge_base: dict) -> str: | |
| """Add relevant links based on query context""" | |
| query_lower = query.lower() | |
| links = [] | |
| # Add portfolio link for project-related queries | |
| 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']}") | |
| # Add blog link for technical queries | |
| if any(word in query_lower for word in ['machine learning', 'ml', 'algorithm', 'knn']): | |
| for post in knowledge_base['personal_details']['online_presence']['blog_posts']: | |
| if 'link' in post and any(word in post['title'].lower() for word in query_lower.split()): | |
| links.append(f"\nRelated blog post: {post['link']}") | |
| break | |
| # Add LinkedIn for professional background queries | |
| 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']}") | |
| if links: | |
| response += '\n\n' + '\n'.join(links) | |
| return response | |
| 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)) | |
| # Algorithm Implementation Projects (completed) | |
| response_parts.append("\nCompleted Algorithm Implementation Projects:") | |
| for project in knowledge_base['projects']['algorithm_practice_projects']: | |
| response_parts.append(format_project_response(project, include_status=False)) | |
| 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']): | |
| skill_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:"] | |
| # Technical Skills Match | |
| if skill_matches['technical_matches']: | |
| response_parts.append("\n• Technical Skills Match:") | |
| for skill in skill_matches['technical_matches']: | |
| response_parts.append(f" - Strong proficiency in {skill}") | |
| # Tools and Technologies | |
| if skill_matches['tool_matches']: | |
| response_parts.append("\n• Relevant Tools/Technologies:") | |
| for tool in skill_matches['tool_matches']: | |
| response_parts.append(f" - Experience with {tool}") | |
| # Relevant Projects | |
| if relevant_projects: | |
| response_parts.append("\n• Relevant Project Experience:") | |
| for project in relevant_projects: | |
| response_parts.append(format_project_response(project)) | |
| # Education and Background | |
| response_parts.append("\n• Education and Background:") | |
| response_parts.append(" - Currently pursuing advanced AI/ML education in Canada") | |
| response_parts.append(" - Unique background combining commerce and technology") | |
| response_parts.append(" - Strong foundation in practical ML implementation") | |
| response = '\n'.join(response_parts) | |
| return add_relevant_links(response, query, knowledge_base) | |
| # Handle background/story queries | |
| elif any(word in query_lower for word in ['background', 'journey', 'story', 'transition']): | |
| transition_story = next((qa['answer'] for qa in knowledge_base['frequently_asked_questions'] | |
| if 'transition' in qa['question'].lower()), '') | |
| response_parts = [ | |
| "My Journey from Commerce to ML/AI:", | |
| "• Education Background:", | |
| f" - {knowledge_base['education']['undergraduate']['course_name']} from {knowledge_base['education']['undergraduate']['institution']}", | |
| "• Career Transition:", | |
| " - Started as a Programmer Trainee at Cognizant", | |
| f" - {transition_story[:200]}...", | |
| "• Current Path:", | |
| " - Pursuing AI/ML education in Canada", | |
| " - Building practical ML projects", | |
| "• Future Goals:", | |
| " - Aiming to become an ML Engineer in Canada", | |
| " - Focus on innovative AI solutions" | |
| ] | |
| response = '\n'.join(response_parts) | |
| return add_relevant_links(response, query, knowledge_base) | |
| # Handle skill-specific queries | |
| elif any(word in query_lower for word in ['skill', 'know', 'technology', 'stack']): | |
| tech_skills = knowledge_base['skills']['technical_skills'] | |
| response_parts = ["My Technical Expertise:"] | |
| # ML/AI Skills | |
| response_parts.append("\n• Machine Learning & AI:") | |
| response_parts.append(f" - Core: {', '.join(tech_skills['machine_learning']['core'])}") | |
| response_parts.append(f" - Frameworks: {', '.join(tech_skills['machine_learning']['frameworks'])}") | |
| # Programming & Tools | |
| response_parts.append("\n• Programming & Development:") | |
| response_parts.append(f" - Languages: {', '.join(tech_skills['programming']['primary'])}") | |
| response_parts.append(f" - Tools: {', '.join(tech_skills['programming']['tools'])}") | |
| # Data & Analytics | |
| response_parts.append("\n• Data & Analytics:") | |
| response_parts.append(f" - Databases: {', '.join(tech_skills['data']['databases'])}") | |
| response_parts.append(f" - Visualization: {', '.join(tech_skills['data']['visualization'])}") | |
| response = '\n'.join(response_parts) | |
| return add_relevant_links(response, query, knowledge_base) | |
| # Handle default/unknown queries | |
| 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!") | |
| 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 | |
| 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 or paste a job description..."): | |
| # Add user message | |
| st.session_state.messages.append({"role": "user", "content": 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}) | |
| st.rerun() | |
| 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.rerun() | |
| st.markdown("---") | |
| if st.button("Clear Chat"): | |
| st.session_state.messages = [] | |
| st.rerun() | |
| if __name__ == "__main__": | |
| main() |