chatbot_agent.py

# chatbot_agent.py

import os
import json
import re
from openai import OpenAI
import traceback
import logging
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Get logger
logger = logging.getLogger(__name__)

# Initialize OpenAI client with error handling
def get_openai_client():
    api_key = os.getenv("OPENAI_API_KEY")
    if not api_key:
        raise ValueError("OpenAI API key not found in environment variables")
    return OpenAI(api_key=api_key)

def format_message(role, content):
    """Format message for chatbot history."""
    return {"role": role, "content": content}

def initialize_graph_prompt(graph_data):
    """Initialize the conversation with available node and edge information."""
    try:
        # Get summary info with safe fallbacks
        summary = graph_data.get('summary', {})
        summary_parts = []
        
        # Only include counts that exist
        if 'symbol_count' in summary:
            summary_parts.append(f"Symbols: {summary['symbol_count']}")
        if 'text_count' in summary:
            summary_parts.append(f"Texts: {summary['text_count']}")
        if 'line_count' in summary:
            summary_parts.append(f"Lines: {summary['line_count']}")
        if 'edge_count' in summary:
            summary_parts.append(f"Edges: {summary['edge_count']}")
            
        summary_info = ", ".join(summary_parts) + "."

        # Prepare node details only if they exist
        node_details = ""
        detailed_results = graph_data.get('detailed_results', {})
        if 'symbols' in detailed_results:
            node_details = "Nodes (symbols) in the graph include:\n"
            for symbol in detailed_results['symbols']:
                details = []
                if 'symbol_id' in symbol:
                    details.append(f"ID: {symbol['symbol_id']}")
                if 'class_id' in symbol:
                    details.append(f"Class: {symbol['class_id']}")
                if 'category' in symbol:
                    details.append(f"Category: {symbol['category']}")
                if 'type' in symbol:
                    details.append(f"Type: {symbol['type']}")
                if 'label' in symbol:
                    details.append(f"Label: {symbol['label']}")
                if details:  # Only add if we have any details
                    node_details += ", ".join(details) + "\n"

        initial_prompt = (
            "You have access to a knowledge graph generated from a P&ID diagram. "
            f"The summary information includes:\n{summary_info}\n\n"
            f"{node_details}\n"
            "Answer questions about the P&ID elements using this information."
        )

        return initial_prompt
        
    except Exception as e:
        logger.error(f"Error creating initial prompt: {str(e)}")
        return ("I have access to a P&ID diagram knowledge graph. "
                "I can help answer questions about the diagram elements.")

def get_assistant_response(user_message, json_path):
    """Generate response based on P&ID data and OpenAI."""
    try:
        client = get_openai_client()
        # Load the aggregated data
        with open(json_path, 'r') as f:
            data = json.load(f)
            
        # Process the user's question
        question = user_message.lower()
        
        # Use rule-based responses for specific questions
        if "valve" in question or "valves" in question:
            valve_count = sum(1 for symbol in data.get('symbols', []) 
                            if 'class' in symbol and 'valve' in symbol['class'].lower())
            return f"I found {valve_count} valves in this P&ID."
            
        elif "pump" in question or "pumps" in question:
            pump_count = sum(1 for symbol in data.get('symbols', [])
                           if 'class' in symbol and 'pump' in symbol['class'].lower())
            return f"I found {pump_count} pumps in this P&ID."
            
        elif "equipment" in question or "components" in question:
            equipment_types = {}
            for symbol in data.get('symbols', []):
                if 'class' in symbol:
                    eq_type = symbol['class']
                    equipment_types[eq_type] = equipment_types.get(eq_type, 0) + 1
            
            response = "Here's a summary of the equipment I found:\n"
            for eq_type, count in equipment_types.items():
                response += f"- {eq_type}: {count}\n"
            return response
            
        # For other questions, use OpenAI
        else:
            # Prepare the conversation context
            graph_data = {
                "summary": {
                    "symbol_count": len(data.get('symbols', [])),
                    "text_count": len(data.get('texts', [])),
                    "line_count": len(data.get('lines', [])),
                    "edge_count": len(data.get('edges', [])),
                },
                "detailed_results": data
            }
            
            initial_prompt = initialize_graph_prompt(graph_data)
            conversation = [
                {"role": "system", "content": initial_prompt},
                {"role": "user", "content": user_message}
            ]

            response = client.chat.completions.create(
                model="gpt-4-turbo",
                messages=conversation
            )
            return response.choices[0].message.content
            
    except Exception as e:
        logger.error(f"Error in get_assistant_response: {str(e)}")
        logger.error(traceback.format_exc())
        return "I apologize, but I encountered an error analyzing the P&ID data. Please try asking a different question."

# Testing and Usage block
if __name__ == "__main__":
    # Load the knowledge graph data from JSON file
    json_file_path = "results/0_aggregated_detections.json"
    try:
        with open(json_file_path, 'r') as file:
            graph_data = json.load(file)
    except FileNotFoundError:
        print(f"Error: File not found at {json_file_path}")
        graph_data = None
    except json.JSONDecodeError:
        print("Error: Failed to decode JSON. Please check the file format.")
        graph_data = None

    # Initialize conversation history with assistant's welcome message
    history = [format_message("assistant", "Hello! I am ready to answer your questions about the P&ID knowledge graph. The graph includes nodes (symbols), edges, linkers, and text tags, and I have detailed information available about each. Please ask any questions related to these elements and their connections.")]

    # Print the assistant's welcome message
    print("Assistant:", history[0]["content"])

    # Individual Testing Options
    if graph_data:
        # Option 1: Test the graph prompt initialization
        print("\n--- Test: Graph Prompt Initialization ---")
        initial_prompt = initialize_graph_prompt(graph_data)
        print(initial_prompt)

        # Option 2: Simulate a conversation with a test question
        print("\n--- Test: Simulate Conversation ---")
        test_question = "Can you tell me about the connections between the nodes?"
        history.append(format_message("user", test_question))
        
        print(f"\nUser: {test_question}")
        for response in get_assistant_response(test_question, json_file_path):
            print("Assistant:", response)
            history.append(format_message("assistant", response))

        # Option 3: Manually input questions for interactive testing
        while True:
            user_question = input("\nYou: ")
            if user_question.lower() in ["exit", "quit"]:
                print("Exiting chat. Goodbye!")
                break

            history.append(format_message("user", user_question))
            for response in get_assistant_response(user_question, json_file_path):
                print("Assistant:", response)
                history.append(format_message("assistant", response))
    else:
        print("Unable to load graph data. Please check the file path and format.")