#!/usr/bin/env python3
"""
FinRobot Hugging Face Space Application
A comprehensive AI Agent Platform for Financial Analysis using Large Language Models
"""

import streamlit as st
import autogen
from finrobot.utils import get_current_date, register_keys_from_json
from finrobot.agents.workflow import SingleAssistant, SingleAssistantShadow
import json
import os
from datetime import datetime

# Page configuration
st.set_page_config(
    page_title="FinRobot - AI Agent Platform for Financial Analysis",
    page_icon="🤖",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Custom CSS
st.markdown("""
<style>
    .main-header {
        font-size: 3rem;
        font-weight: bold;
        text-align: center;
        margin-bottom: 2rem;
        background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
        -webkit-background-clip: text;
        -webkit-text-fill-color: transparent;
    }
    .feature-card {
        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
        padding: 1.5rem;
        border-radius: 10px;
        color: white;
        margin: 1rem 0;
    }
    .success-message {
        background-color: #d4edda;
        border: 1px solid #c3e6cb;
        color: #155724;
        padding: 1rem;
        border-radius: 5px;
        margin: 1rem 0;
    }
</style>
""", unsafe_allow_html=True)

def initialize_finrobot():
    """Initialize FinRobot with API configurations"""
    try:
        # Load OpenAI configuration
        with open("OAI_CONFIG_LIST", "r") as f:
            oai_config = json.load(f)
        
        llm_config = {
            "config_list": oai_config,
            "timeout": 120,
            "temperature": 0,
        }
        
        # Register API keys
        register_keys_from_json("config_api_keys")
        
        return llm_config
    except Exception as e:
        st.error(f"Error initializing FinRobot: {str(e)}")
        return None

def create_market_analyst(llm_config):
    """Create Market Analyst agent"""
    return SingleAssistant(
        "Market_Analyst",
        llm_config,
        human_input_mode="NEVER",
    )

def create_financial_analyst(llm_config):
    """Create Financial Analyst agent for report writing"""
    return SingleAssistantShadow(
        "Expert_Investor",
        llm_config,
        max_consecutive_auto_reply=None,
        human_input_mode="TERMINATE",
    )

def main():
    # Header
    st.markdown('<h1 class="main-header">🤖 FinRobot</h1>', unsafe_allow_html=True)
    st.markdown('<h2 style="text-align: center; color: #666;">AI Agent Platform for Financial Analysis using LLMs</h2>', unsafe_allow_html=True)
    
    # Sidebar
    st.sidebar.title("🎛️ Configuration")
    
    # Initialize FinRobot
    if 'llm_config' not in st.session_state:
        with st.spinner("Initializing FinRobot..."):
            st.session_state.llm_config = initialize_finrobot()
    
    if st.session_state.llm_config is None:
        st.error("❌ Failed to initialize FinRobot. Please check your configuration files.")
        return
    
    # Agent selection
    agent_type = st.sidebar.selectbox(
        "Select Agent Type",
        ["Market Forecaster", "Financial Analyst", "Trade Strategist"]
    )
    
    st.sidebar.markdown("---")
    
    # Main content area
    if agent_type == "Market Forecaster":
        st.markdown('<div class="feature-card">', unsafe_allow_html=True)
        st.markdown("### 📈 Market Forecaster Agent")
        st.markdown("Predicts stock movements using company ticker, financials, and market news.")
        st.markdown('</div>', unsafe_allow_html=True)
        
        col1, col2 = st.columns([2, 1])
        
        with col1:
            company_ticker = st.text_input("Company Ticker Symbol", value="AAPL", help="Enter stock ticker (e.g., AAPL, MSFT, NVDA)")
            analysis_type = st.selectbox("Analysis Type", ["Brief Analysis", "Detailed Analysis", "Risk Assessment"])
        
        with col2:
            st.markdown("**Current Date:**")
            st.info(get_current_date())
        
        if st.button("🚀 Analyze Stock", type="primary"):
            if company_ticker:
                with st.spinner("Analyzing stock..."):
                    try:
                        assistant = create_market_analyst(st.session_state.llm_config)
                        
                        message = f"""
                        Use all the tools provided to retrieve information available for {company_ticker} upon {get_current_date()}. 
                        Analyze the positive developments and potential concerns of {company_ticker} with 2-4 most important factors respectively and keep them concise. 
                        Most factors should be inferred from company related news. 
                        Then make a rough prediction (e.g. up/down by 2-3%) of the {company_ticker} stock price movement for next week. 
                        Provide a summary analysis to support your prediction.
                        """
                        
                        # Note: In a real implementation, you would call assistant.chat(message)
                        # For demo purposes, we'll show a placeholder
                        st.success("✅ Analysis completed!")
                        st.markdown(f"""
                        **Analysis for {company_ticker}:**
                        
                        📊 **Positive Developments:**
                        - Strong quarterly earnings growth
                        - New product launches driving revenue
                        - Market expansion in emerging regions
                        
                        ⚠️ **Potential Concerns:**
                        - Supply chain disruptions
                        - Regulatory challenges
                        - Competitive pressure
                        
                        🔮 **Prediction:** Expected 2-3% upward movement next week
                        
                        *Note: This is a demo. In production, the agent would perform real analysis.*
                        """)
                        
                    except Exception as e:
                        st.error(f"Error during analysis: {str(e)}")
            else:
                st.warning("Please enter a company ticker symbol.")
    
    elif agent_type == "Financial Analyst":
        st.markdown('<div class="feature-card">', unsafe_allow_html=True)
        st.markdown("### 📋 Financial Analyst Agent")
        st.markdown("Generates comprehensive equity research reports from 10-K forms and financial data.")
        st.markdown('</div>', unsafe_allow_html=True)
        
        col1, col2 = st.columns([2, 1])
        
        with col1:
            company_name = st.text_input("Company Name", value="Microsoft")
            fiscal_year = st.text_input("Fiscal Year", value="2023")
        
        with col2:
            st.markdown("**Report Features:**")
            st.markdown("• Financial statement analysis")
            st.markdown("• Risk assessment")
            st.markdown("• Performance visualization")
            st.markdown("• PDF generation")
        
        if st.button("📊 Generate Report", type="primary"):
            if company_name and fiscal_year:
                with st.spinner("Generating financial report..."):
                    try:
                        assistant = create_financial_analyst(st.session_state.llm_config)
                        
                        st.success("✅ Report generation completed!")
                        st.markdown(f"""
                        **Financial Report for {company_name} - {fiscal_year}**
                        
                        📈 **Key Metrics:**
                        - Revenue Growth: +15.2%
                        - Net Income: +12.8%
                        - Operating Margin: 42.1%
                        
                        📊 **Financial Highlights:**
                        - Strong cloud revenue growth
                        - Improved operational efficiency
                        - Strategic acquisitions contributing to growth
                        
                        ⚠️ **Risk Factors:**
                        - Market competition
                        - Regulatory changes
                        - Economic uncertainties
                        
                        *Note: This is a demo. In production, the agent would generate a full PDF report.*
                        """)
                        
                    except Exception as e:
                        st.error(f"Error generating report: {str(e)}")
            else:
                st.warning("Please enter both company name and fiscal year.")
    
    elif agent_type == "Trade Strategist":
        st.markdown('<div class="feature-card">', unsafe_allow_html=True)
        st.markdown("### ⚡ Trade Strategist Agent")
        st.markdown("Advanced trading strategies with multimodal capabilities and real-time analysis.")
        st.markdown('</div>', unsafe_allow_html=True)
        
        col1, col2 = st.columns([2, 1])
        
        with col1:
            strategy_type = st.selectbox("Strategy Type", ["Momentum", "Mean Reversion", "Arbitrage", "Portfolio Optimization"])
            risk_level = st.select_slider("Risk Level", options=["Low", "Medium", "High"], value="Medium")
        
        with col2:
            st.markdown("**Strategy Features:**")
            st.markdown("• Real-time data analysis")
            st.markdown("• Risk management")
            st.markdown("• Portfolio optimization")
            st.markdown("• Performance tracking")
        
        if st.button("🎯 Generate Strategy", type="primary"):
            with st.spinner("Generating trading strategy..."):
                try:
                    st.success("✅ Strategy generated successfully!")
                    st.markdown(f"""
                    **{strategy_type} Trading Strategy - {risk_level} Risk**
                    
                    📊 **Strategy Overview:**
                    - Entry signals based on technical indicators
                    - Stop-loss and take-profit levels defined
                    - Position sizing optimized for risk level
                    
                    📈 **Expected Performance:**
                    - Annual Return: 12-18%
                    - Maximum Drawdown: 5-8%
                    - Sharpe Ratio: 1.2-1.8
                    
                    ⚙️ **Implementation:**
                    - Automated execution ready
                    - Real-time monitoring enabled
                    - Risk controls active
                    
                    *Note: This is a demo. In production, the agent would provide detailed strategy parameters.*
                    """)
                    
                except Exception as e:
                    st.error(f"Error generating strategy: {str(e)}")
    
    # Footer
    st.markdown("---")
    st.markdown("""
    <div style="text-align: center; color: #666; margin-top: 2rem;">
        <p>🤖 <strong>FinRobot</strong> - An Open-Source AI Agent Platform for Financial Analysis using Large Language Models</p>
        <p>Built with ❤️ by <a href="https://github.com/AI4Finance-Foundation/FinRobot" target="_blank">AI4Finance Foundation</a></p>
    </div>
    """, unsafe_allow_html=True)

if __name__ == "__main__":
    main()