Spaces:
Runtime error
Runtime error
| """ | |
| QuantProdigy AI Agents - Free Version for Hugging Face Spaces | |
| Multi-agent learning platform for CFA preparation using only free resources | |
| """ | |
| import streamlit as st | |
| import json | |
| import plotly.graph_objects as go | |
| from typing import Dict, List, Optional | |
| from datetime import datetime | |
| import sys | |
| import os | |
| # Debug: Print current directory and files | |
| print("Current directory:", os.getcwd()) | |
| print("Files in directory:", os.listdir('.')) | |
| # Add current directory to path for Hugging Face Spaces | |
| sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) | |
| # Suppress warnings | |
| import warnings | |
| warnings.filterwarnings('ignore') | |
| # Import agent system with error handling | |
| try: | |
| # Check if agent files exist | |
| if os.path.exists('agent_orchestrator.py'): | |
| from agent_orchestrator import AgentOrchestrator | |
| else: | |
| print("agent_orchestrator.py not found") | |
| AgentOrchestrator = None | |
| if os.path.exists('knowledge_engine.py'): | |
| from knowledge_engine import KnowledgeEngine | |
| else: | |
| print("knowledge_engine.py not found") | |
| KnowledgeEngine = None | |
| if os.path.exists('monitoring_system.py'): | |
| from monitoring_system import MonitoringSystem | |
| else: | |
| print("monitoring_system.py not found") | |
| MonitoringSystem = None | |
| if os.path.exists('runtime_environment.py'): | |
| from runtime_environment import RuntimeEnvironment | |
| else: | |
| print("runtime_environment.py not found") | |
| RuntimeEnvironment = None | |
| except ImportError as e: | |
| print(f"Import error details: {e}") | |
| # Create dummy classes for demo mode | |
| class DummyOrchestrator: | |
| def __init__(self): | |
| pass | |
| def initialize_agents(self): | |
| return True | |
| def process_query(self, query, user_profile, selected_agents): | |
| return { | |
| 'response': f"This is a demo response to: {query}\n\nIn a full deployment, multiple AI agents would analyze this question and provide comprehensive answers.", | |
| 'primary_agent': 'Demo Agent' | |
| } | |
| class DummyKnowledgeEngine: | |
| def __init__(self): | |
| pass | |
| def retrieve_knowledge(self, query): | |
| return { | |
| 'results': [ | |
| { | |
| 'type': 'concept', | |
| 'content': { | |
| 'name': 'Time Value of Money', | |
| 'definition': 'The concept that money available now is worth more than the same amount in the future due to its potential earning capacity.', | |
| 'formula': 'FV = PV ร (1 + r)โฟ' | |
| }, | |
| 'topics': ['Quantitative Methods'] | |
| } | |
| ] | |
| } | |
| class DummyMonitoring: | |
| def __init__(self): | |
| pass | |
| def get_system_metrics(self): | |
| return { | |
| 'active_agents': 6, | |
| 'avg_response_time': 1.2, | |
| 'success_rate': 0.95, | |
| 'total_queries': 0 | |
| } | |
| def update_metrics(self): | |
| pass | |
| class DummyRuntime: | |
| def __init__(self): | |
| pass | |
| AgentOrchestrator = DummyOrchestrator | |
| KnowledgeEngine = DummyKnowledgeEngine | |
| MonitoringSystem = DummyMonitoring | |
| RuntimeEnvironment = DummyRuntime | |
| # Page configuration for Hugging Face Spaces | |
| st.set_page_config( | |
| page_title="QuantProdigy AI Agents", | |
| page_icon="๐", | |
| layout="wide", | |
| initial_sidebar_state="expanded" | |
| ) | |
| # Initialize session state | |
| def init_session_state(): | |
| """Initialize session state with safe defaults""" | |
| defaults = { | |
| 'orchestrator': None, | |
| 'knowledge_engine': None, | |
| 'monitoring': None, | |
| 'runtime': None, | |
| 'chat_history': [], | |
| 'user_profile': { | |
| 'cfa_level': 'I', | |
| 'study_focus': ['Quantitative Methods', 'Economics'], | |
| 'weak_areas': [], | |
| 'learning_style': 'visual', | |
| 'experience_level': 'beginner', | |
| 'user_id': 'user_' + datetime.now().strftime('%Y%m%d_%H%M%S') | |
| }, | |
| 'active_agents': {}, | |
| 'system_ready': False, | |
| 'demo_mode': True # Added for Hugging Face Spaces | |
| } | |
| for key, value in defaults.items(): | |
| if key not in st.session_state: | |
| st.session_state[key] = value | |
| # Initialize components safely | |
| if st.session_state.orchestrator is None: | |
| try: | |
| st.session_state.orchestrator = AgentOrchestrator() | |
| except: | |
| st.session_state.orchestrator = AgentOrchestrator() if callable(AgentOrchestrator) else None | |
| if st.session_state.knowledge_engine is None: | |
| try: | |
| st.session_state.knowledge_engine = KnowledgeEngine() | |
| except: | |
| st.session_state.knowledge_engine = KnowledgeEngine() if callable(KnowledgeEngine) else None | |
| if st.session_state.monitoring is None: | |
| try: | |
| st.session_state.monitoring = MonitoringSystem() | |
| except: | |
| st.session_state.monitoring = MonitoringSystem() if callable(MonitoringSystem) else None | |
| if st.session_state.runtime is None: | |
| try: | |
| st.session_state.runtime = RuntimeEnvironment() | |
| except: | |
| st.session_state.runtime = RuntimeEnvironment() if callable(RuntimeEnvironment) else None | |
| # Initialize session state | |
| init_session_state() | |
| # Custom CSS for Hugging Face Spaces | |
| st.markdown(""" | |
| <style> | |
| /* Main styling */ | |
| .main-header { | |
| font-size: 2.5rem; | |
| color: #1E3A8A; | |
| text-align: center; | |
| margin-bottom: 1rem; | |
| padding: 1rem; | |
| background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); | |
| -webkit-background-clip: text; | |
| -webkit-text-fill-color: transparent; | |
| background-clip: text; | |
| } | |
| /* Card styling */ | |
| .agent-card { | |
| background: white; | |
| padding: 1.5rem; | |
| border-radius: 15px; | |
| margin: 10px 0; | |
| box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); | |
| border-left: 5px solid #667eea; | |
| transition: transform 0.3s ease; | |
| } | |
| /* Message styling */ | |
| .message-user { | |
| background-color: #E3F2FD; | |
| padding: 15px; | |
| border-radius: 15px 15px 15px 5px; | |
| margin: 10px 0; | |
| text-align: right; | |
| max-width: 80%; | |
| margin-left: auto; | |
| border: 1px solid #BBDEFB; | |
| } | |
| .message-agent { | |
| background-color: #F3E5F5; | |
| padding: 15px; | |
| border-radius: 15px 15px 5px 15px; | |
| margin: 10px 0; | |
| max-width: 80%; | |
| border: 1px solid #E1BEE7; | |
| } | |
| /* Button styling */ | |
| .stButton button { | |
| background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); | |
| color: white; | |
| border: none; | |
| padding: 0.5rem 2rem; | |
| border-radius: 25px; | |
| font-weight: bold; | |
| transition: all 0.3s ease; | |
| } | |
| .stButton button:hover { | |
| transform: translateY(-2px); | |
| box-shadow: 0 5px 15px rgba(102, 126, 234, 0.4); | |
| } | |
| /* Demo banner */ | |
| .demo-banner { | |
| background: linear-gradient(135deg, #ff6b6b 0%, #ff8e53 100%); | |
| color: white; | |
| padding: 10px; | |
| border-radius: 10px; | |
| text-align: center; | |
| margin-bottom: 20px; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| # Sidebar | |
| with st.sidebar: | |
| st.title("๐ QuantProdigy") | |
| # Demo mode indicator | |
| if st.session_state.demo_mode: | |
| st.markdown('<div class="demo-banner">DEMO MODE</div>', unsafe_allow_html=True) | |
| st.markdown("### Free CFA Learning Platform") | |
| st.markdown("---") | |
| # System Status | |
| st.subheader("๐ฅ๏ธ System Status") | |
| if st.session_state.system_ready: | |
| st.success("โ System Ready") | |
| else: | |
| if st.button("Initialize System", type="primary", use_container_width=True): | |
| try: | |
| with st.spinner("Initializing AI Agents..."): | |
| if st.session_state.orchestrator: | |
| st.session_state.orchestrator.initialize_agents() | |
| st.session_state.system_ready = True | |
| st.success("System initialized successfully!") | |
| st.rerun() | |
| except Exception as e: | |
| st.error(f"Initialization failed: {e}") | |
| # User profile | |
| st.subheader("๐ค Your Profile") | |
| with st.expander("Edit Profile", expanded=True): | |
| cfa_level = st.selectbox( | |
| "CFA Level", | |
| ["I", "II", "III"], | |
| index=0 | |
| ) | |
| learning_focus = st.multiselect( | |
| "Learning Focus", | |
| ["Quantitative Methods", "Economics", "Financial Reporting", | |
| "Corporate Finance", "Equity", "Fixed Income", "Derivatives", | |
| "Alternative Investments", "Portfolio Management", "Ethics"], | |
| default=["Quantitative Methods", "Economics"] | |
| ) | |
| learning_style = st.selectbox( | |
| "Learning Style", | |
| ["Visual", "Auditory", "Reading/Writing", "Kinesthetic"], | |
| index=0 | |
| ) | |
| experience_level = st.selectbox( | |
| "Experience Level", | |
| ["Beginner", "Intermediate", "Advanced"], | |
| index=0 | |
| ) | |
| # Update user profile | |
| st.session_state.user_profile.update({ | |
| 'cfa_level': cfa_level, | |
| 'study_focus': learning_focus, | |
| 'learning_style': learning_style.lower(), | |
| 'experience_level': experience_level.lower() | |
| }) | |
| # Quick actions | |
| st.subheader("โก Quick Actions") | |
| quick_topics = ["Time Value of Money", "Statistics", "Portfolio Theory", "Ethics"] | |
| for topic in quick_topics: | |
| if st.button(f"Learn: {topic}", use_container_width=True): | |
| st.session_state.chat_history.append({ | |
| 'role': 'user', | |
| 'content': f"Teach me about {topic}", | |
| 'timestamp': datetime.now().isoformat() | |
| }) | |
| st.rerun() | |
| # Main content | |
| if not st.session_state.system_ready: | |
| # Welcome screen | |
| st.markdown('<h1 class="main-header">Welcome to QuantProdigy AI Agents</h1>', unsafe_allow_html=True) | |
| col1, col2 = st.columns([2, 1]) | |
| with col1: | |
| st.markdown(""" | |
| ## ๐ Free CFA Learning Platform | |
| **QuantProdigy** is an intelligent multi-agent system designed to help you master | |
| CFA concepts using AI-powered assistance - completely free! | |
| ### โจ Features: | |
| - **๐ค 6 Specialized AI Agents** working together | |
| - **๐ Comprehensive CFA Knowledge Base** | |
| - **๐งฎ Built-in Financial Calculators** | |
| - **๐ Progress Tracking & Analytics** | |
| - **๐ฌ Interactive Learning Sessions** | |
| """) | |
| if st.session_state.demo_mode: | |
| st.info(""" | |
| โ ๏ธ **Demo Mode Active** | |
| This is a lightweight demo version for Hugging Face Spaces. | |
| Full features require local deployment with additional AI models. | |
| """) | |
| with col2: | |
| st.markdown(""" | |
| ### ๐ Get Started | |
| Click the button below to initialize the AI agents and start learning: | |
| """) | |
| if st.button("๐ Initialize & Start Learning", type="primary", use_container_width=True): | |
| try: | |
| with st.spinner("๐ค Loading AI Agents..."): | |
| # Initialize all components | |
| if st.session_state.orchestrator: | |
| st.session_state.orchestrator.initialize_agents() | |
| st.session_state.system_ready = True | |
| st.success("โ System Ready!") | |
| st.rerun() | |
| except Exception as e: | |
| st.error(f"Initialization failed: {e}") | |
| # Feature showcase | |
| st.markdown("---") | |
| st.subheader("๐ฏ Featured Learning Agents") | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| st.markdown(""" | |
| <div class="agent-card"> | |
| <h4>๐ง Diagnostician</h4> | |
| <p>Analyzes your learning gaps and identifies weak areas</p> | |
| <small>Ready to use</small> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| with col2: | |
| st.markdown(""" | |
| <div class="agent-card"> | |
| <h4>๐จโ๐ซ Socratic Tutor</h4> | |
| <p>Guides you through questions to build understanding</p> | |
| <small>Ready to use</small> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| with col3: | |
| st.markdown(""" | |
| <div class="agent-card"> | |
| <h4>๐งฎ Calculator</h4> | |
| <p>Performs complex financial calculations instantly</p> | |
| <small>Ready to use</small> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| st.stop() | |
| # Main application (system is ready) | |
| st.markdown('<h1 class="main-header">QuantProdigy AI Learning Agents</h1>', unsafe_allow_html=True) | |
| if st.session_state.demo_mode: | |
| st.info(""" | |
| ๐ก **Demo Mode**: This version runs with simplified AI agents. | |
| For full functionality with local LLMs, download and run locally. | |
| """) | |
| # Tab layout | |
| tab1, tab2, tab3, tab4, tab5 = st.tabs([ | |
| "๐ Learning Session", | |
| "๐ค Agent Dashboard", | |
| "๐ Knowledge Base", | |
| "๐ Progress Analytics", | |
| "๐ ๏ธ Tools & Calculators" | |
| ]) | |
| # Tab 1: Learning Session | |
| with tab1: | |
| st.header("Interactive Learning Session") | |
| # Chat interface | |
| col1, col2 = st.columns([3, 1]) | |
| with col1: | |
| user_input = st.text_area( | |
| "Type your question here:", | |
| height=100, | |
| placeholder="E.g., 'Explain the concept of beta in finance' or 'Help me calculate portfolio variance...'" | |
| ) | |
| with col2: | |
| if st.button("Ask AI Agents", type="primary", use_container_width=True): | |
| if user_input.strip(): | |
| # Add user message to history | |
| st.session_state.chat_history.append({ | |
| 'role': 'user', | |
| 'content': user_input, | |
| 'timestamp': datetime.now().isoformat() | |
| }) | |
| # Process with orchestrator | |
| if st.session_state.orchestrator: | |
| with st.spinner("๐ค AI agents are analyzing your question..."): | |
| try: | |
| response = st.session_state.orchestrator.process_query( | |
| query=user_input, | |
| user_profile=st.session_state.user_profile, | |
| selected_agents=["Diagnostician", "Knowledge Expert"] | |
| ) | |
| # Add agent response to history | |
| st.session_state.chat_history.append({ | |
| 'role': 'agent', | |
| 'content': response['response'], | |
| 'agent': response.get('primary_agent', 'Orchestrator'), | |
| 'timestamp': datetime.now().isoformat() | |
| }) | |
| st.rerun() | |
| except Exception as e: | |
| st.error(f"Error processing query: {e}") | |
| # Add fallback response | |
| st.session_state.chat_history.append({ | |
| 'role': 'agent', | |
| 'content': f"""I'll help you with: "{user_input}" | |
| **Time Value of Money Example:** | |
| If you invest $1,000 at 5% annual interest for 5 years: | |
| - Future Value = $1,000 ร (1 + 0.05)โต = $1,276.28 | |
| **Key Concept:** Money today is worth more than the same amount in the future due to earning potential.""", | |
| 'agent': 'Demo Agent', | |
| 'timestamp': datetime.now().isoformat() | |
| }) | |
| st.rerun() | |
| else: | |
| # Demo response | |
| st.session_state.chat_history.append({ | |
| 'role': 'agent', | |
| 'content': f"""Thanks for your question about "{user_input}"! | |
| **In a full deployment**, multiple AI agents would: | |
| 1. Analyze your learning level and style | |
| 2. Retrieve relevant CFA knowledge | |
| 3. Provide step-by-step explanations | |
| 4. Generate practice problems | |
| 5. Track your progress | |
| **For now, here's a helpful insight:** | |
| In finance, understanding core concepts like risk-return tradeoff, time value of money, and diversification is fundamental to mastering CFA material.""", | |
| 'agent': 'Demo Agent', | |
| 'timestamp': datetime.now().isoformat() | |
| }) | |
| st.rerun() | |
| # Display chat history | |
| st.subheader("๐ Conversation History") | |
| if not st.session_state.chat_history: | |
| st.info("๐ Start by asking a question above!") | |
| else: | |
| for message in st.session_state.chat_history[-10:]: | |
| if message['role'] == 'user': | |
| st.markdown(f""" | |
| <div class="message-user"> | |
| <strong>You:</strong> {message['content']} | |
| <br><small>{message.get('timestamp', '')[:16]}</small> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| else: | |
| st.markdown(f""" | |
| <div class="message-agent"> | |
| <strong>{message.get('agent', 'AI Agent')}:</strong><br> | |
| {message['content']} | |
| <br><small>{message.get('timestamp', '')[:16]}</small> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # Tab 2: Agent Dashboard | |
| with tab2: | |
| st.header("Agent Dashboard") | |
| # Agent status grid | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| st.markdown(""" | |
| <div class="agent-card"> | |
| <h4>๐ง Diagnostician</h4> | |
| <p>Analyzes learning gaps and problems</p> | |
| <small>Active in Demo</small> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| with col2: | |
| st.markdown(""" | |
| <div class="agent-card"> | |
| <h4>๐จโ๐ซ Socratic Tutor</h4> | |
| <p>Guides through questions and reasoning</p> | |
| <small>Active in Demo</small> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| with col3: | |
| st.markdown(""" | |
| <div class="agent-card"> | |
| <h4>๐งฎ Calculator</h4> | |
| <p>Performs complex calculations</p> | |
| <small>Active in Demo</small> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # Tab 3: Knowledge Base | |
| with tab3: | |
| st.header("CFA Knowledge Base") | |
| # Sample knowledge | |
| st.subheader("๐ Sample CFA Concepts") | |
| concepts = [ | |
| { | |
| "name": "Time Value of Money", | |
| "definition": "Money available now is worth more than the same amount in the future due to its potential earning capacity.", | |
| "formula": "FV = PV ร (1 + r)โฟ" | |
| }, | |
| { | |
| "name": "Sharpe Ratio", | |
| "definition": "Measures risk-adjusted return of an investment portfolio.", | |
| "formula": "Sharpe Ratio = (Rp - Rf) / ฯp" | |
| }, | |
| { | |
| "name": "Beta Coefficient", | |
| "definition": "Measures the volatility of an investment relative to the market.", | |
| "formula": "ฮฒ = Cov(Ri, Rm) / Var(Rm)" | |
| } | |
| ] | |
| for concept in concepts: | |
| with st.expander(f"๐ {concept['name']}"): | |
| st.markdown(f"**Definition:** {concept['definition']}") | |
| st.markdown(f"**Formula:** `{concept['formula']}`") | |
| if st.button(f"Learn more about {concept['name']}", key=f"learn_{concept['name']}"): | |
| st.session_state.chat_history.append({ | |
| 'role': 'user', | |
| 'content': f"Explain {concept['name']} in detail", | |
| 'timestamp': datetime.now().isoformat() | |
| }) | |
| st.rerun() | |
| # Tab 4: Progress Analytics | |
| with tab4: | |
| st.header("Learning Progress Analytics") | |
| # Sample progress chart | |
| st.subheader("๐ Sample Progress Data") | |
| fig = go.Figure(data=[ | |
| go.Bar(name='Topics Studied', x=['Week 1', 'Week 2', 'Week 3'], y=[3, 5, 8]), | |
| go.Bar(name='Practice Problems', x=['Week 1', 'Week 2', 'Week 3'], y=[5, 12, 20]) | |
| ]) | |
| fig.update_layout(barmode='group', title='Learning Progress Over Time') | |
| st.plotly_chart(fig, use_container_width=True) | |
| # Tab 5: Tools & Calculators | |
| with tab5: | |
| st.header("๐ ๏ธ Financial Calculators") | |
| # Simple TVM Calculator | |
| st.subheader("๐ฐ Time Value of Money Calculator") | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| pv = st.number_input("Present Value ($)", value=1000.0, min_value=0.0, step=100.0) | |
| rate = st.number_input("Annual Rate (%)", value=5.0, min_value=0.0, max_value=100.0, step=0.5) | |
| periods = st.number_input("Periods (years)", value=5, min_value=1, max_value=100, step=1) | |
| with col2: | |
| if st.button("Calculate Future Value", type="primary", use_container_width=True): | |
| fv = pv * (1 + rate/100) ** periods | |
| st.success(f"**Future Value:** ${fv:,.2f}") | |
| st.info(f"${pv:,.2f} at {rate}% for {periods} years grows to ${fv:,.2f}") | |
| # Footer | |
| st.markdown("---") | |
| st.markdown(""" | |
| <div style="text-align: center; padding: 20px;"> | |
| <p><strong>QuantProdigy AI Agents</strong> โข Free CFA Learning Platform</p> | |
| <small>Demo Version for Hugging Face Spaces โข For full features, run locally</small> | |
| </div> | |
| """, unsafe_allow_html=True) |