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| import streamlit as st | |
| import json | |
| import re | |
| from datetime import datetime | |
| from typing import Dict, List, Optional | |
| import openai | |
| from dataclasses import dataclass, asdict | |
| # Configure the page | |
| st.set_page_config( | |
| page_title="TalentScout - AI Hiring Assistant", | |
| page_icon="🤖", | |
| layout="wide", | |
| initial_sidebar_state="expanded" | |
| ) | |
| class CandidateInfo: | |
| """Data class to store candidate information""" | |
| full_name: str = "" | |
| email: str = "" | |
| phone: str = "" | |
| experience_years: str = "" | |
| desired_positions: str = "" | |
| current_location: str = "" | |
| tech_stack: str = "" | |
| session_start: str = "" | |
| class HiringAssistant: | |
| """Main class for the Hiring Assistant chatbot""" | |
| def __init__(self): | |
| self.conversation_stages = { | |
| "greeting": 0, | |
| "name": 1, | |
| "email": 2, | |
| "phone": 3, | |
| "experience": 4, | |
| "position": 5, | |
| "location": 6, | |
| "tech_stack": 7, | |
| "technical_questions": 8, | |
| "completed": 9 | |
| } | |
| # Tech stack categories for better question generation | |
| self.tech_categories = { | |
| "programming_languages": ["python", "java", "javascript", "c++", "c#", "go", "rust", "php", "ruby", "swift", "kotlin"], | |
| "web_frameworks": ["django", "flask", "fastapi", "react", "angular", "vue", "nodejs", "express", "spring", "laravel"], | |
| "databases": ["mysql", "postgresql", "mongodb", "redis", "sqlite", "oracle", "cassandra", "elasticsearch"], | |
| "cloud_platforms": ["aws", "azure", "gcp", "docker", "kubernetes", "terraform"], | |
| "data_science": ["pandas", "numpy", "scikit-learn", "tensorflow", "pytorch", "matplotlib", "seaborn"], | |
| "mobile": ["react native", "flutter", "android", "ios", "xamarin"] | |
| } | |
| # Conversation ending keywords | |
| self.exit_keywords = ["bye", "goodbye", "exit", "quit", "end", "stop", "thank you", "thanks"] | |
| def initialize_session_state(self): | |
| """Initialize session state variables""" | |
| if "candidate_info" not in st.session_state: | |
| st.session_state.candidate_info = CandidateInfo(session_start=datetime.now().strftime("%Y-%m-%d %H:%M:%S")) | |
| if "conversation_history" not in st.session_state: | |
| st.session_state.conversation_history = [] | |
| if "current_stage" not in st.session_state: | |
| st.session_state.current_stage = 0 | |
| if "technical_questions" not in st.session_state: | |
| st.session_state.technical_questions = [] | |
| if "current_question_index" not in st.session_state: | |
| st.session_state.current_question_index = 0 | |
| if "conversation_ended" not in st.session_state: | |
| st.session_state.conversation_ended = False | |
| def validate_email(self, email: str) -> bool: | |
| """Validate email format""" | |
| pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$' | |
| return re.match(pattern, email) is not None | |
| def validate_phone(self, phone: str) -> bool: | |
| """Validate phone number format""" | |
| # Remove spaces, dashes, and parentheses | |
| cleaned_phone = re.sub(r'[\s\-\(\)]', '', phone) | |
| # Check if it contains only digits and optional + at the beginning | |
| pattern = r'^\+?[1-9]\d{9,14}$' | |
| return re.match(pattern, cleaned_phone) is not None | |
| def check_exit_intent(self, user_input: str) -> bool: | |
| """Check if user wants to end the conversation""" | |
| user_input_lower = user_input.lower().strip() | |
| return any(keyword in user_input_lower for keyword in self.exit_keywords) | |
| def generate_greeting(self) -> str: | |
| """Generate initial greeting message""" | |
| return """ | |
| 🤖 **Hello! Welcome to TalentScout's AI Hiring Assistant!** | |
| I'm here to help streamline your application process. I'll be gathering some basic information about you and then asking a few technical questions based on your expertise. | |
| This conversation will take about 5-10 minutes and will help us better understand your background and skills. | |
| Ready to get started? Please tell me your **full name**. | |
| *(You can type 'exit' or 'bye' at any time to end our conversation)* | |
| """ | |
| def generate_technical_questions(self, tech_stack: str) -> List[str]: | |
| """Generate technical questions based on the candidate's tech stack""" | |
| questions = [] | |
| tech_stack_lower = tech_stack.lower() | |
| # Split tech stack into individual technologies | |
| technologies = [tech.strip() for tech in re.split(r'[,;|\n]', tech_stack_lower)] | |
| question_templates = { | |
| "python": [ | |
| "Explain the difference between list and tuple in Python and when you would use each.", | |
| "What are Python decorators and can you provide a simple example?", | |
| "How do you handle exceptions in Python? Explain try-except blocks.", | |
| "What is the difference between '==' and 'is' operators in Python?", | |
| "Explain list comprehensions in Python and provide an example.", | |
| "What are Python generators and when would you use them?" | |
| ], | |
| "r": [ | |
| "What is the difference between data.frame and matrix in R?", | |
| "Explain the concept of vectorization in R with an example.", | |
| "How do you handle missing values (NA) in R?", | |
| "What are R packages and how do you install them?", | |
| "Explain the apply family of functions in R." | |
| ], | |
| "java": [ | |
| "Explain the concept of Object-Oriented Programming in Java.", | |
| "What is the difference between ArrayList and LinkedList in Java?", | |
| "Explain the concept of inheritance and polymorphism in Java.", | |
| "What are Java interfaces and when would you use them?" | |
| ], | |
| "javascript": [ | |
| "Explain the difference between 'var', 'let', and 'const' in JavaScript.", | |
| "What are JavaScript promises and how do they work?", | |
| "Explain the concept of closures in JavaScript with an example.", | |
| "What is the difference between '==' and '===' in JavaScript?" | |
| ], | |
| "react": [ | |
| "What are React hooks and why are they useful?", | |
| "Explain the difference between state and props in React.", | |
| "What is the virtual DOM and how does it improve performance?", | |
| "How do you handle forms in React applications?" | |
| ], | |
| "django": [ | |
| "Explain the MVC pattern in Django and how it's implemented.", | |
| "What are Django models and how do you define relationships between them?", | |
| "How do you handle user authentication in Django?", | |
| "What is Django ORM and how does it work?" | |
| ], | |
| "sql": [ | |
| "Explain the difference between INNER JOIN and LEFT JOIN.", | |
| "What are database indexes and when should you use them?", | |
| "How do you optimize a slow SQL query?", | |
| "Explain the concept of database normalization.", | |
| "What is the difference between WHERE and HAVING clauses?", | |
| "How do you handle NULL values in SQL queries?" | |
| ], | |
| "mysql": [ | |
| "What are the different storage engines in MySQL?", | |
| "Explain the difference between MyISAM and InnoDB.", | |
| "How do you optimize MySQL queries for better performance?", | |
| "What is database replication in MySQL?" | |
| ], | |
| "postgresql": [ | |
| "What are the advantages of PostgreSQL over other databases?", | |
| "Explain ACID properties in PostgreSQL.", | |
| "What are PostgreSQL indexes and how do they work?", | |
| "How do you handle concurrent transactions in PostgreSQL?" | |
| ], | |
| "aws": [ | |
| "What are the main differences between EC2, ECS, and Lambda?", | |
| "Explain the concept of S3 bucket policies and IAM roles.", | |
| "How do you ensure high availability in AWS architecture?", | |
| "What is the difference between RDS and DynamoDB?" | |
| ], | |
| "pandas": [ | |
| "What is the difference between DataFrame and Series in pandas?", | |
| "How do you handle missing data in pandas?", | |
| "Explain groupby operations in pandas with an example.", | |
| "What are pandas indexes and how do you use them?" | |
| ], | |
| "numpy": [ | |
| "What is the difference between NumPy arrays and Python lists?", | |
| "Explain broadcasting in NumPy with an example.", | |
| "How do you perform matrix operations in NumPy?", | |
| "What are NumPy universal functions (ufuncs)?" | |
| ], | |
| "machine learning": [ | |
| "Explain the difference between supervised and unsupervised learning.", | |
| "What is overfitting and how do you prevent it?", | |
| "Explain the bias-variance tradeoff in machine learning.", | |
| "What are the different types of cross-validation techniques?" | |
| ], | |
| "data science": [ | |
| "What is the typical data science workflow?", | |
| "How do you handle outliers in your data?", | |
| "Explain the difference between correlation and causation.", | |
| "What are the key steps in exploratory data analysis?" | |
| ] | |
| } | |
| # Generate questions based on detected technologies | |
| for tech in technologies: | |
| tech = tech.strip() | |
| # Check for exact matches first | |
| if tech in question_templates: | |
| questions.extend(question_templates[tech][:2]) # Add 2 questions per technology | |
| # Check for partial matches | |
| else: | |
| for key in question_templates: | |
| if key in tech or tech in key: | |
| questions.extend(question_templates[key][:2]) | |
| break | |
| # Special handling for data science related terms | |
| data_science_keywords = ["data", "analytics", "statistics", "ml", "ai", "analysis"] | |
| if any(keyword in tech_stack_lower for keyword in data_science_keywords): | |
| if "data science" in question_templates: | |
| questions.extend(question_templates["data science"][:2]) | |
| if "machine learning" in question_templates: | |
| questions.extend(question_templates["machine learning"][:1]) | |
| # If no specific questions found, generate generic ones based on the tech stack | |
| if not questions: | |
| questions = [ | |
| f"Can you explain your experience with {technologies[0] if technologies else 'your primary technology'}?", | |
| "Describe a challenging technical problem you've solved recently.", | |
| "How do you stay updated with the latest trends in your tech stack?", | |
| "What best practices do you follow in your development process?", | |
| "How do you approach debugging and troubleshooting in your projects?" | |
| ] | |
| return questions[:5] # Return maximum 5 questions | |
| def process_user_input(self, user_input: str) -> str: | |
| """Process user input based on current conversation stage""" | |
| if self.check_exit_intent(user_input): | |
| st.session_state.conversation_ended = True | |
| return self.generate_goodbye_message() | |
| current_stage = st.session_state.current_stage | |
| candidate_info = st.session_state.candidate_info | |
| # Start with greeting if this is the first interaction | |
| if current_stage == 0 and not user_input.strip(): | |
| st.session_state.current_stage = 1 | |
| return self.generate_greeting() | |
| elif current_stage == 1: # Name collection | |
| if len(user_input.strip()) < 2: | |
| return "Please provide your full name (at least 2 characters)." | |
| candidate_info.full_name = user_input.strip() | |
| st.session_state.current_stage = 2 | |
| return f"Nice to meet you, {candidate_info.full_name}! 👋\n\nNow, could you please provide your **email address**?" | |
| elif current_stage == 2: # Email collection | |
| if not self.validate_email(user_input.strip()): | |
| return "Please provide a valid email address (e.g., john@example.com)." | |
| candidate_info.email = user_input.strip() | |
| st.session_state.current_stage = 3 | |
| return "Thank you! Now, please provide your **phone number**." | |
| elif current_stage == 3: # Phone collection | |
| if not self.validate_phone(user_input.strip()): | |
| return "Please provide a valid phone number (e.g., +1234567890 or 123-456-7890)." | |
| candidate_info.phone = user_input.strip() | |
| st.session_state.current_stage = 4 | |
| return "Great! How many **years of experience** do you have in your field?" | |
| elif current_stage == 4: # Experience collection | |
| if not user_input.strip(): | |
| return "Please provide your years of experience (e.g., '3 years' or '0-1 year')." | |
| candidate_info.experience_years = user_input.strip() | |
| st.session_state.current_stage = 5 | |
| return "What **position(s)** are you interested in? (e.g., 'Software Developer', 'Data Scientist', 'Full Stack Developer')" | |
| elif current_stage == 5: # Position collection | |
| if not user_input.strip(): | |
| return "Please specify the position(s) you're interested in." | |
| candidate_info.desired_positions = user_input.strip() | |
| st.session_state.current_stage = 6 | |
| return "What is your **current location**? (City, State/Country)" | |
| elif current_stage == 6: # Location collection | |
| if not user_input.strip(): | |
| return "Please provide your current location." | |
| candidate_info.current_location = user_input.strip() | |
| st.session_state.current_stage = 7 | |
| return """Perfect! Now for the technical part. | |
| Please list your **tech stack** - the programming languages, frameworks, databases, and tools you're proficient in. | |
| *For example: "Python, Django, PostgreSQL, React, AWS, Docker"*""" | |
| elif current_stage == 7: # Tech stack collection | |
| if not user_input.strip(): | |
| return "Please provide your tech stack (programming languages, frameworks, tools, etc.)." | |
| candidate_info.tech_stack = user_input.strip() | |
| # Generate technical questions | |
| questions = self.generate_technical_questions(candidate_info.tech_stack) | |
| st.session_state.technical_questions = questions | |
| st.session_state.current_question_index = 0 | |
| st.session_state.current_stage = 8 | |
| return f"""Excellent! Based on your tech stack: **{candidate_info.tech_stack}** | |
| I'll now ask you {len(questions)} technical questions to assess your proficiency. Don't worry - just answer to the best of your ability! | |
| **Question 1 of {len(questions)}:** | |
| {questions[0]}""" | |
| elif current_stage == 8: # Technical questions | |
| questions = st.session_state.technical_questions | |
| current_q_index = st.session_state.current_question_index | |
| # Store the answer | |
| st.session_state.conversation_history.append({ | |
| "question": questions[current_q_index], | |
| "answer": user_input, | |
| "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S") | |
| }) | |
| current_q_index += 1 | |
| st.session_state.current_question_index = current_q_index | |
| if current_q_index < len(questions): | |
| return f"""Thank you for your answer! | |
| **Question {current_q_index + 1} of {len(questions)}:** | |
| {questions[current_q_index]}""" | |
| else: | |
| st.session_state.current_stage = 9 | |
| return self.generate_completion_message() | |
| return "I didn't understand that. Could you please try again?" | |
| def generate_completion_message(self) -> str: | |
| """Generate completion message""" | |
| candidate_info = st.session_state.candidate_info | |
| return f"""🎉 **Congratulations, {candidate_info.full_name}!** | |
| You've successfully completed the initial screening process with TalentScout's AI Hiring Assistant. | |
| **Here's a summary of what we collected:** | |
| - **Name:** {candidate_info.full_name} | |
| - **Email:** {candidate_info.email} | |
| - **Phone:** {candidate_info.phone} | |
| - **Experience:** {candidate_info.experience_years} | |
| - **Desired Position(s):** {candidate_info.desired_positions} | |
| - **Location:** {candidate_info.current_location} | |
| - **Tech Stack:** {candidate_info.tech_stack} | |
| - **Technical Questions Answered:** {len(st.session_state.technical_questions)} | |
| **Next Steps:** | |
| 1. Our recruitment team will review your responses within 2-3 business days | |
| 2. If your profile matches our current openings, we'll reach out via email or phone | |
| 3. You may be invited for a detailed technical interview or assessment | |
| Thank you for your time and interest in TalentScout! We appreciate your effort in completing this screening process. | |
| *You can now close this window or type 'exit' to end our conversation.*""" | |
| def generate_goodbye_message(self) -> str: | |
| """Generate goodbye message""" | |
| return """👋 **Thank you for using TalentScout's AI Hiring Assistant!** | |
| We appreciate your time. If you'd like to complete the screening process later, please feel free to start a new session. | |
| Have a great day! 🌟""" | |
| def export_candidate_data(self) -> Dict: | |
| """Export candidate data for download""" | |
| return { | |
| "candidate_info": asdict(st.session_state.candidate_info), | |
| "technical_qa": st.session_state.conversation_history, | |
| "session_completed": st.session_state.current_stage == 9, | |
| "export_timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S") | |
| } | |
| def main(): | |
| """Main application function""" | |
| # Custom CSS for better styling | |
| st.markdown(""" | |
| <style> | |
| .main-header { | |
| text-align: center; | |
| padding: 2rem 0; | |
| background: linear-gradient(90deg, #667eea 0%, #764ba2 100%); | |
| color: white; | |
| border-radius: 10px; | |
| margin-bottom: 2rem; | |
| } | |
| .chat-message { | |
| padding: 1rem; | |
| border-radius: 10px; | |
| margin: 1rem 0; | |
| box-shadow: 0 2px 4px rgba(0,0,0,0.1); | |
| } | |
| .user-message { | |
| background-color: #e3f2fd; | |
| border-left: 4px solid #2196f3; | |
| } | |
| .bot-message { | |
| background-color: #f3e5f5; | |
| border-left: 4px solid #9c27b0; | |
| } | |
| .sidebar-info { | |
| background-color: #f0f2f6; | |
| padding: 1rem; | |
| border-radius: 10px; | |
| margin: 1rem 0; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| # Initialize the hiring assistant | |
| assistant = HiringAssistant() | |
| assistant.initialize_session_state() | |
| # Header | |
| st.markdown(""" | |
| <div class="main-header"> | |
| <h1>🤖 TalentScout AI Hiring Assistant</h1> | |
| <p>Streamlining the recruitment process with intelligent screening</p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # Sidebar with candidate information | |
| with st.sidebar: | |
| st.header("📋 Candidate Information") | |
| candidate_info = st.session_state.candidate_info | |
| st.markdown('<div class="sidebar-info">', unsafe_allow_html=True) | |
| info_fields = [ | |
| ("Full Name", candidate_info.full_name), | |
| ("Email", candidate_info.email), | |
| ("Phone", candidate_info.phone), | |
| ("Experience", candidate_info.experience_years), | |
| ("Desired Position", candidate_info.desired_positions), | |
| ("Location", candidate_info.current_location), | |
| ("Tech Stack", candidate_info.tech_stack) | |
| ] | |
| for field, value in info_fields: | |
| if value: | |
| st.write(f"**{field}:** {value}") | |
| else: | |
| st.write(f"**{field}:** *Not provided yet*") | |
| st.markdown('</div>', unsafe_allow_html=True) | |
| # Progress indicator | |
| stage_names = ["Start", "Name", "Email", "Phone", "Experience", "Position", "Location", "Tech Stack", "Questions", "Complete"] | |
| current_stage = min(st.session_state.current_stage, len(stage_names) - 1) | |
| st.header("📈 Progress") | |
| progress = current_stage / (len(stage_names) - 1) | |
| st.progress(progress) | |
| st.write(f"Stage: {stage_names[current_stage]} ({current_stage + 1}/{len(stage_names)})") | |
| # Export data button (only show when completed) | |
| if st.session_state.current_stage == 9: | |
| st.header("💾 Export Data") | |
| if st.button("Download Session Data"): | |
| data = assistant.export_candidate_data() | |
| st.download_button( | |
| label="Download JSON", | |
| data=json.dumps(data, indent=2), | |
| file_name=f"candidate_data_{candidate_info.full_name.replace(' ', '_')}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json", | |
| mime="application/json" | |
| ) | |
| # Main chat interface | |
| st.header("💬 Chat Interface") | |
| # Initialize conversation if not started | |
| if st.session_state.current_stage == 0: | |
| # Show initial greeting automatically | |
| st.markdown('<div class="chat-message bot-message">', unsafe_allow_html=True) | |
| st.markdown("**🤖 TalentScout Assistant:**") | |
| st.markdown(assistant.generate_greeting()) | |
| st.markdown('</div>', unsafe_allow_html=True) | |
| st.session_state.current_stage = 1 # Move to name collection stage | |
| # Display conversation history for technical questions | |
| if st.session_state.current_stage == 8 and st.session_state.conversation_history: | |
| st.subheader("Technical Q&A History") | |
| for i, qa in enumerate(st.session_state.conversation_history): | |
| st.markdown('<div class="chat-message bot-message">', unsafe_allow_html=True) | |
| st.markdown(f"**🤖 Question {i+1}:** {qa['question']}") | |
| st.markdown('</div>', unsafe_allow_html=True) | |
| st.markdown('<div class="chat-message user-message">', unsafe_allow_html=True) | |
| st.markdown(f"**👤 Your Answer:** {qa['answer']}") | |
| st.markdown('</div>', unsafe_allow_html=True) | |
| # Chat input | |
| if not st.session_state.conversation_ended and st.session_state.current_stage < 9: | |
| # Show current prompt based on stage | |
| current_prompts = { | |
| 1: "Please provide your **full name**:", | |
| 2: "Please provide your **email address**:", | |
| 3: "Please provide your **phone number**:", | |
| 4: "How many **years of experience** do you have?", | |
| 5: "What **position(s)** are you interested in?", | |
| 6: "What is your **current location**?", | |
| 7: "Please list your **tech stack** (programming languages, frameworks, tools):", | |
| 8: f"**Question {st.session_state.current_question_index + 1} of {len(st.session_state.technical_questions)}:**" if st.session_state.technical_questions else "Technical Questions:" | |
| } | |
| if st.session_state.current_stage in current_prompts: | |
| st.info(current_prompts[st.session_state.current_stage]) | |
| # Show current technical question if in technical stage | |
| if st.session_state.current_stage == 8 and st.session_state.technical_questions: | |
| current_q_index = st.session_state.current_question_index | |
| if current_q_index < len(st.session_state.technical_questions): | |
| st.markdown(f"**{st.session_state.technical_questions[current_q_index]}**") | |
| user_input = st.text_input( | |
| "Your response:", | |
| key=f"user_input_{st.session_state.current_stage}_{st.session_state.current_question_index}", | |
| placeholder="Type your response here..." | |
| ) | |
| col1, col2 = st.columns([1, 4]) | |
| with col1: | |
| send_button = st.button("Send", type="primary") | |
| with col2: | |
| if st.button("End Conversation"): | |
| st.session_state.conversation_ended = True | |
| st.rerun() | |
| if send_button and user_input: | |
| # Process input and get response | |
| bot_response = assistant.process_user_input(user_input) | |
| # Display user message | |
| st.markdown('<div class="chat-message user-message">', unsafe_allow_html=True) | |
| st.markdown(f"**👤 You:** {user_input}") | |
| st.markdown('</div>', unsafe_allow_html=True) | |
| # Display bot response | |
| st.markdown('<div class="chat-message bot-message">', unsafe_allow_html=True) | |
| st.markdown("**🤖 TalentScout Assistant:**") | |
| st.markdown(bot_response) | |
| st.markdown('</div>', unsafe_allow_html=True) | |
| # Rerun to update the interface | |
| st.rerun() | |
| elif st.session_state.conversation_ended: | |
| st.markdown('<div class="chat-message bot-message">', unsafe_allow_html=True) | |
| st.markdown("**🤖 TalentScout Assistant:**") | |
| st.markdown(assistant.generate_goodbye_message()) | |
| st.markdown('</div>', unsafe_allow_html=True) | |
| if st.button("Start New Session"): | |
| # Reset session state | |
| for key in st.session_state.keys(): | |
| del st.session_state[key] | |
| st.rerun() | |
| elif st.session_state.current_stage == 9: | |
| st.markdown('<div class="chat-message bot-message">', unsafe_allow_html=True) | |
| st.markdown("**🤖 TalentScout Assistant:**") | |
| st.markdown(assistant.generate_completion_message()) | |
| st.markdown('</div>', unsafe_allow_html=True) | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| if st.button("Start New Session"): | |
| # Reset session state | |
| for key in st.session_state.keys(): | |
| del st.session_state[key] | |
| st.rerun() | |
| with col2: | |
| if st.button("End Session"): | |
| st.session_state.conversation_ended = True | |
| st.rerun() | |
| # Footer | |
| st.markdown("---") | |
| st.markdown(""" | |
| <div style="text-align: center; color: #666; padding: 1rem;"> | |
| <p>🚀 <strong>TalentScout AI Hiring Assistant</strong> - Powered by Advanced Language Models</p> | |
| <p><em>Streamlining recruitment through intelligent automation</em></p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| if __name__ == "__main__": | |
| main() |