agent.py

import os
import re
from datetime import datetime, timedelta
from typing import TypedDict, Annotated
import sympy as sp
from sympy import *
import math
from langchain_openai import ChatOpenAI
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.messages import HumanMessage, SystemMessage
from langgraph.graph import StateGraph, MessagesState, START, END
from langgraph.prebuilt import ToolNode
from langgraph.checkpoint.memory import MemorySaver
import json

# Load environment variables
from dotenv import load_dotenv
load_dotenv()

def read_system_prompt():
    """Read the system prompt from file"""
    try:
        with open('system_prompt.txt', 'r') as f:
            return f.read().strip()
    except FileNotFoundError:
        return """You are a helpful assistant tasked with answering questions using a set of tools. 
Now, I will ask you a question. Report your thoughts, and finish your answer with the following template: 
FINAL ANSWER: [YOUR FINAL ANSWER]. 
YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
Your answer should only start with "FINAL ANSWER: ", then follows with the answer."""

def math_calculator(expression: str) -> str:
    """
    Advanced mathematical calculator that can handle complex expressions,
    equations, symbolic math, calculus, and more using SymPy.
    """
    try:
        # Clean the expression
        expression = expression.strip()
        
        # Handle common mathematical operations and functions
        expression = expression.replace('^', '**')  # Convert ^ to **
        expression = expression.replace('ln', 'log')  # Natural log
        
        # Try to evaluate as a symbolic expression first
        try:
            result = sp.sympify(expression)
            
            # If it's a symbolic expression that can be simplified
            simplified = sp.simplify(result)
            
            # Try to get numerical value
            try:
                numerical = float(simplified.evalf())
                return str(numerical)
            except:
                return str(simplified)
                
        except:
            # Fall back to basic evaluation
            # Replace common math functions
            safe_expression = expression
            for func in ['sin', 'cos', 'tan', 'sqrt', 'log', 'exp', 'abs']:
                safe_expression = safe_expression.replace(func, f'math.{func}')
            
            # Evaluate safely
            result = eval(safe_expression, {"__builtins__": {}}, {
                "math": math,
                "pi": math.pi,
                "e": math.e
            })
            return str(result)
            
    except Exception as e:
        return f"Error calculating '{expression}': {str(e)}"

def date_time_processor(query: str) -> str:
    """
    Process date and time related queries, calculations, and conversions.
    """
    try:
        current_time = datetime.now()
        query_lower = query.lower()
        
        # Current date/time queries
        if 'current' in query_lower or 'today' in query_lower or 'now' in query_lower:
            if 'date' in query_lower:
                return current_time.strftime('%Y-%m-%d')
            elif 'time' in query_lower:
                return current_time.strftime('%H:%M:%S')
            else:
                return current_time.strftime('%Y-%m-%d %H:%M:%S')
        
        # Day of week queries
        if 'day of week' in query_lower or 'what day' in query_lower:
            return current_time.strftime('%A')
        
        # Year queries
        if 'year' in query_lower and 'current' in query_lower:
            return str(current_time.year)
        
        # Month queries
        if 'month' in query_lower and 'current' in query_lower:
            return current_time.strftime('%B')
        
        # Date arithmetic (simple cases)
        if 'days ago' in query_lower:
            days_match = re.search(r'(\d+)\s+days?\s+ago', query_lower)
            if days_match:
                days = int(days_match.group(1))
                past_date = current_time - timedelta(days=days)
                return past_date.strftime('%Y-%m-%d')
        
        if 'days from now' in query_lower or 'days later' in query_lower:
            days_match = re.search(r'(\d+)\s+days?\s+(?:from now|later)', query_lower)
            if days_match:
                days = int(days_match.group(1))
                future_date = current_time + timedelta(days=days)
                return future_date.strftime('%Y-%m-%d')
        
        # If no specific pattern matched, return current datetime
        return f"Current date and time: {current_time.strftime('%Y-%m-%d %H:%M:%S')}"
        
    except Exception as e:
        return f"Error processing date/time query: {str(e)}"

# Define the agent state
class AgentState(TypedDict):
    messages: Annotated[list, "The messages in the conversation"]

class GAIAAgent:
    def __init__(self):
        # Check for required API keys
        openai_key = os.getenv("OPENAI_API_KEY")
        tavily_key = os.getenv("TAVILY_API_KEY")
        
        if not openai_key:
            raise ValueError("OPENAI_API_KEY environment variable is required")
        if not tavily_key:
            raise ValueError("TAVILY_API_KEY environment variable is required")
        
        print("✅ API keys found - initializing agent...")
        
        # Initialize LLM (using OpenAI GPT-4)
        self.llm = ChatOpenAI(
            model="gpt-4o-mini",
            temperature=0,
            openai_api_key=openai_key
        )
        
        # Initialize tools
        self.search_tool = TavilySearchResults(
            max_results=5,
            tavily_api_key=tavily_key
        )
        
        # Create tool list
        self.tools = [self.search_tool]
        
        # Create LLM with tools
        self.llm_with_tools = self.llm.bind_tools(self.tools)
        
        # Build the graph
        self.graph = self._build_graph()
        
        self.system_prompt = read_system_prompt()

    def _build_graph(self):
        """Build the LangGraph workflow"""
        
        def agent_node(state: AgentState):
            """Main agent reasoning node"""
            messages = state["messages"]
            
            # Add system message if not present
            if not any(isinstance(msg, SystemMessage) for msg in messages):
                system_msg = SystemMessage(content=self.system_prompt)
                messages = [system_msg] + messages
            
            # Get the last human message to check if it needs special processing
            last_human_msg = None
            for msg in reversed(messages):
                if isinstance(msg, HumanMessage):
                    last_human_msg = msg.content
                    break
            
            # Check if this is a math problem
            if last_human_msg and self._is_math_problem(last_human_msg):
                math_result = math_calculator(last_human_msg)
                enhanced_msg = f"Math calculation result: {math_result}\n\nOriginal question: {last_human_msg}\n\nProvide your final answer based on this calculation."
                messages[-1] = HumanMessage(content=enhanced_msg)
            
            # Check if this is a date/time problem
            elif last_human_msg and self._is_datetime_problem(last_human_msg):
                datetime_result = date_time_processor(last_human_msg)
                enhanced_msg = f"Date/time processing result: {datetime_result}\n\nOriginal question: {last_human_msg}\n\nProvide your final answer based on this information."
                messages[-1] = HumanMessage(content=enhanced_msg)
            
            response = self.llm_with_tools.invoke(messages)
            return {"messages": messages + [response]}
        
        def tool_node(state: AgentState):
            """Tool execution node"""
            messages = state["messages"]
            last_message = messages[-1]
            
            # Execute tool calls
            tool_node_instance = ToolNode(self.tools)
            result = tool_node_instance.invoke(state)
            return result
        
        def should_continue(state: AgentState):
            """Decide whether to continue or end"""
            last_message = state["messages"][-1]
            
            # If the last message has tool calls, continue to tools
            if hasattr(last_message, 'tool_calls') and last_message.tool_calls:
                return "tools"
            
            # If we have a final answer, end
            if hasattr(last_message, 'content') and "FINAL ANSWER:" in last_message.content:
                return "end"
            
            # Otherwise continue
            return "end"
        
        # Build the graph
        workflow = StateGraph(AgentState)
        
        # Add nodes
        workflow.add_node("agent", agent_node)
        workflow.add_node("tools", tool_node)
        
        # Add edges
        workflow.add_edge(START, "agent")
        workflow.add_conditional_edges("agent", should_continue, {
            "tools": "tools",
            "end": END
        })
        workflow.add_edge("tools", "agent")
        
        # Compile
        memory = MemorySaver()
        return workflow.compile(checkpointer=memory)
    
    def _is_math_problem(self, text: str) -> bool:
        """Check if the text contains mathematical expressions"""
        math_indicators = [
            '+', '-', '*', '/', '^', '=', 'calculate', 'compute', 
            'solve', 'equation', 'integral', 'derivative', 'sum',
            'sqrt', 'log', 'sin', 'cos', 'tan', 'exp'
        ]
        text_lower = text.lower()
        return any(indicator in text_lower for indicator in math_indicators) or \
               re.search(r'\d+[\+\-\*/\^]\d+', text) is not None
    
    def _is_datetime_problem(self, text: str) -> bool:
        """Check if the text contains date/time related queries"""
        datetime_indicators = [
            'date', 'time', 'day', 'month', 'year', 'today', 'yesterday', 
            'tomorrow', 'current', 'now', 'ago', 'later', 'when'
        ]
        text_lower = text.lower()
        return any(indicator in text_lower for indicator in datetime_indicators)
    
    def __call__(self, question: str) -> str:
        """Process a question and return the answer"""
        try:
            print(f"Processing question: {question[:100]}...")
            
            # Create initial state
            initial_state = {
                "messages": [HumanMessage(content=question)]
            }
            
            # Run the graph
            config = {"configurable": {"thread_id": "gaia_thread"}}
            final_state = self.graph.invoke(initial_state, config)
            
            # Extract the final answer
            last_message = final_state["messages"][-1]
            response_content = last_message.content if hasattr(last_message, 'content') else str(last_message)
            
            # Extract just the final answer part
            final_answer = self._extract_final_answer(response_content)
            
            print(f"Final answer: {final_answer}")
            return final_answer
            
        except Exception as e:
            print(f"Error processing question: {e}")
            return f"Error: {str(e)}"
    
    def _extract_final_answer(self, response: str) -> str:
        """Extract the final answer from the response"""
        if "FINAL ANSWER:" in response:
            # Find the final answer part
            parts = response.split("FINAL ANSWER:")
            if len(parts) > 1:
                answer = parts[-1].strip()
                # Remove any trailing punctuation or explanations
                answer = answer.split('\n')[0].strip()
                return answer
        
        # If no FINAL ANSWER format found, return the whole response
        return response.strip()

# Create a function to get the agent (for use in app.py)
def create_agent():
    """Factory function to create the GAIA agent"""
    return GAIAAgent()