import os import sys import json import time import gradio as gr import google.generativeai as genai from intent_detector import detect_intent from rag_utils import retrieve_profiles, retrieve_jobs # 1. Configure Gemini API_KEY = os.getenv("GOOGLE_API_KEY") if not API_KEY: raise ValueError("Error: Set GOOGLE_API_KEY environment variable before running.") genai.configure(api_key=API_KEY) # 2. System prompt with conversational onboarding flow SYSTEM_PROMPT = """ You are a helpful AI assistant that can perform three main tasks: 1. ONBOARD – Help professionals create their profile by asking questions ONE AT A TIME in a natural conversation flow 2. SEARCH – Help find job opportunities based on user requirements 3. POST – Help clients create structured job postings ONBOARDING CONVERSATION FLOW: When onboarding a user, ask questions in this specific order, ONE QUESTION AT A TIME: 1. First, ask for their full name 2. Then ask what their current role/job title is 3. Then ask about their key skills (programming languages, technologies, etc.) 4. Then ask about their years of experience 5. Then ask about their preferred location or if they want remote work 6. Finally, summarize their profile and confirm if everything looks correct IMPORTANT ONBOARDING RULES: - Ask ONLY ONE question at a time - Wait for the user's response before asking the next question - Be conversational and friendly - If they provide multiple pieces of information at once, acknowledge what they shared and ask for what's still missing - Keep questions simple and clear - After collecting all information, show a summary and ask for confirmation SEARCH BEHAVIOR: For job searches, retrieve relevant listings and present them clearly with title, company, and key details. POST BEHAVIOR: For job postings, help create structured JSON format job listings. Always be helpful, conversational, and ask follow-up questions when needed. """ class ChatbotState: """Manages the conversation state for onboarding flow.""" def __init__(self): self.reset() def reset(self): """Reset the conversation state.""" self.mode = None # "onboarding", "search", "post", or None self.onboarding_step = 0 self.user_data = {} self.onboarding_complete = False def is_onboarding(self): """Check if currently in onboarding mode.""" return self.mode == "onboarding" and not self.onboarding_complete def get_next_onboarding_question(self): """Get the next question in the onboarding flow.""" questions = [ "What's your full name?", "What's your current role or job title?", "What are your key skills? (For example: Python, React, project management, etc.)", "How many years of experience do you have in your field?", "What's your preferred work location? (Or would you prefer remote work?)" ] if self.onboarding_step < len(questions): return questions[self.onboarding_step] return None def save_onboarding_data(self, field, value): """Save user response to onboarding data.""" fields = ["name", "role", "skills", "experience", "location"] if self.onboarding_step < len(fields): self.user_data[fields[self.onboarding_step]] = value def get_onboarding_summary(self): """Generate a summary of collected onboarding data.""" summary = "Here's your profile summary:\n" summary += f"• Name: {self.user_data.get('name', 'Not provided')}\n" summary += f"• Role: {self.user_data.get('role', 'Not provided')}\n" summary += f"• Skills: {self.user_data.get('skills', 'Not provided')}\n" summary += f"• Experience: {self.user_data.get('experience', 'Not provided')}\n" summary += f"• Location: {self.user_data.get('location', 'Not provided')}\n" summary += "\nDoes this look correct? (yes/no)" return summary def save_user_profile(user_data, filename="data/user_profiles.jsonl"): """Save user profile to file.""" os.makedirs(os.path.dirname(filename), exist_ok=True) # Create a unique ID for the user user_id = f"user_{int(time.time())}" profile_entry = { "id": user_id, "name": user_data.get("name", ""), "role": user_data.get("role", ""), "skills": user_data.get("skills", "").split(", ") if user_data.get("skills") else [], "experience": user_data.get("experience", ""), "location": user_data.get("location", ""), "timestamp": time.time() } try: with open(filename, "a", encoding="utf-8") as f: f.write(json.dumps(profile_entry) + "\n") return True except Exception as e: print(f"Error saving profile: {e}") return False # Global state for the chatbot chatbot_state = ChatbotState() model = None def initialize_model(model_name="gemini-1.5-flash"): """Initialize the Gemini model.""" global model model = genai.GenerativeModel(model_name) return model def process_message(message, history, model_name): """Process user message and return bot response.""" global chatbot_state, model if model is None: initialize_model(model_name) try: # Handle onboarding flow if chatbot_state.is_onboarding(): # Handle onboarding responses if chatbot_state.onboarding_step < 5: # Still collecting basic info # Save the user's response chatbot_state.save_onboarding_data(None, message) chatbot_state.onboarding_step += 1 # Check if we need to ask another question if chatbot_state.onboarding_step < 5: next_question = chatbot_state.get_next_onboarding_question() return f"Great! {next_question}" else: # All info collected, show summary summary = chatbot_state.get_onboarding_summary() return summary elif chatbot_state.onboarding_step == 5: # Waiting for confirmation if message.lower() in ["yes", "y", "correct", "looks good"]: # Save profile if save_user_profile(chatbot_state.user_data): response = "Perfect! Your profile has been saved successfully. You're now onboarded and ready to search for jobs! 🎉" else: response = "Your profile information has been recorded. You're now onboarded! 🎉" chatbot_state.onboarding_complete = True chatbot_state.mode = None return response elif message.lower() in ["no", "n", "incorrect", "wrong"]: chatbot_state.reset() chatbot_state.mode = "onboarding" chatbot_state.onboarding_step = 0 return "No problem! Let's start over. What's your full name?" else: return "Please answer 'yes' if the information is correct, or 'no' if you'd like to start over." # Regular intent detection for non-onboarding messages intent = detect_intent(message, model=model_name) print(f"[Debug] Detected intent: {intent}") # Handle different intents if intent == "ONBOARD": # Start onboarding flow chatbot_state.reset() chatbot_state.mode = "onboarding" # Check if user already provided some info in their first message first_question = chatbot_state.get_next_onboarding_question() return f"I'd be happy to help you create your professional profile! Let's start with some basic information.\n\n{first_question}" elif intent == "SEARCH": # Handle job search context_block = None job_results = retrieve_jobs(message, top_k=3) if job_results: lines = [] for idx, job in enumerate(job_results, start=1): lines.append(f"{idx}) {job['text']}") block_text = "\n".join(lines) context_block = f"Relevant job listings:\n{block_text}" print(f"[Debug] Found {len(job_results)} relevant jobs") else: print("[Debug] No relevant jobs found") # Prepare prompt for job search search_prompt = f""" {SYSTEM_PROMPT} {"Context Information: " + context_block if context_block else "No relevant jobs found in the database."} User is looking for jobs: {message} Please provide a helpful response about job opportunities. If jobs were found, present them clearly. If no jobs were found, provide helpful guidance on how they might refine their search. """ response = model.generate_content(search_prompt) return response.text elif intent == "POST": # Handle job posting creation post_prompt = f""" {SYSTEM_PROMPT} User wants to create a job posting: {message} Help them create a structured job posting. Ask for any missing information needed to create a complete job post (title, description, required skills, budget, timeline, etc.). """ response = model.generate_content(post_prompt) return response.text else: # General conversation general_prompt = f""" {SYSTEM_PROMPT} User message: {message} Respond helpfully. If they seem to want to get started with onboarding, job searching, or job posting, guide them appropriately. """ response = model.generate_content(general_prompt) return response.text except Exception as e: return f"Sorry, I encountered an error: {str(e)}. Please try again!" def reset_conversation(): """Reset the conversation state.""" global chatbot_state chatbot_state.reset() return [], "" def get_welcome_message(): """Get the initial welcome message.""" return """👋 **Welcome to AI Job Assistant!** I can help you with: • 🎯 **Creating your professional profile** - Let me guide you through a quick onboarding • 🔍 **Finding job opportunities** - Search for jobs that match your skills • 📝 **Creating job postings** - Help employers post structured job listings What would you like to do today?""" # Custom CSS for a modern look custom_css = """ .gradio-container { font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; } .chat-message { padding: 1rem; margin: 0.5rem 0; border-radius: 1rem; max-width: 80%; } .user-message { background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; margin-left: auto; } .bot-message { background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%); color: white; } /* Dark mode support */ .dark .chat-message { box-shadow: 0 4px 6px rgba(0, 0, 0, 0.3); } .gradio-chatbot { height: 500px; } /* Custom button styles */ .primary-button { background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); border: none; color: white; padding: 0.5rem 1rem; border-radius: 0.5rem; font-weight: 500; transition: all 0.3s ease; } .primary-button:hover { transform: translateY(-2px); box-shadow: 0 4px 12px rgba(102, 126, 234, 0.4); } """ def create_interface(): """Create the Gradio interface.""" with gr.Blocks( css=custom_css, theme=gr.themes.Soft( primary_hue="blue", secondary_hue="purple", neutral_hue="slate", ), title="AI Job Assistant" ) as interface: gr.HTML("""

🤖 AI Job Assistant

Your intelligent career companion

""") with gr.Row(): with gr.Column(scale=4): chatbot = gr.Chatbot( value=[{"role": "assistant", "content": get_welcome_message()}], height=500, show_label=False, container=True, avatar_images=("👤", "🤖"), type="messages" ) with gr.Row(): msg = gr.Textbox( placeholder="Type your message here...", show_label=False, scale=4, container=False ) send_btn = gr.Button( "Send", variant="primary", scale=1, elem_classes=["primary-button"] ) with gr.Column(scale=1, min_width=250): gr.HTML("""

🚀 Quick Actions

💼 Get Started:
"I want to create my profile"
🔍 Find Jobs:
"Show me Python developer jobs"
📝 Post Job:
"I want to post a job opening"
""") model_selector = gr.Dropdown( choices=["gemini-1.5-flash", "gemini-1.5-pro"], value="gemini-1.5-flash", label="🧠 AI Model", info="Choose your preferred AI model" ) clear_btn = gr.Button( "🗑️ Clear Chat", variant="secondary", size="sm" ) gr.HTML("""

ℹ️ Tips

""") # Event handlers def respond(message, chat_history, model_name): if not message.strip(): return chat_history, "" bot_message = process_message(message, chat_history, model_name) chat_history.append({"role": "user", "content": message}) chat_history.append({"role": "assistant", "content": bot_message}) return chat_history, "" def clear_chat(): reset_conversation() return [{"role": "assistant", "content": get_welcome_message()}], "" # Wire up the events msg.submit(respond, [msg, chatbot, model_selector], [chatbot, msg]) send_btn.click(respond, [msg, chatbot, model_selector], [chatbot, msg]) clear_btn.click(clear_chat, None, [chatbot, msg]) # Auto-focus on textbox interface.load(lambda: gr.update(focus=True), None, msg) return interface def main(): """Main function to launch the Gradio interface.""" # Initialize the model chosen_model = sys.argv[1] if len(sys.argv) > 1 else "gemini-1.5-flash" initialize_model(chosen_model) # Create and launch the interface interface = create_interface() print("🚀 Launching AI Job Assistant...") print("🌐 Opening in your default browser...") interface.launch( server_name="0.0.0.0", # Allow external access server_port=7860, # Default Gradio port share=False, # Set to True to create public link show_error=True ) if __name__ == "__main__": main()