import sys
from pathlib import Path

sys.path.insert(0, str(Path(__file__).parent))

import chainlit as cl

from src.components.model_nlp_intent import predict_intent
from src.components.model_nlp_ner import extract_entities_pipeline
from src.components.model_risk_predictor import predict_risk
from src.components.recommendation_engine import generate_recommendation


@cl.on_chat_start
async def welcome():
    
    welcome_msg = cl.Message(
        content=(
            "# 🌐 Welcome to AI-Powered Supply Chain Risk Advisor\n\n"
            "I provide **real-time risk analysis** and **mitigation strategies** "
            "based on:\n"
            "- 🌍 **Regional factors** (port congestion, infrastructure)\n"
            "- ⚠️ **Active events** (strikes, typhoons, disruptions)\n"
            "- 🚢 **Route analysis** (origin to destination)\n"
            "- 🤖 **ML-powered predictions** (trained on historical data)\n\n"
            "### 💬 Example Questions:\n\n"
            "- \"Is there any delay in vessels from USA to UAE?\"\n"
            "- \"What should I do about the port strike in Shanghai?\"\n"
            "- \"Are there weather problems affecting shipments to Germany?\"\n"
            "- \"Risk level for Mumbai to Singapore route?\"\n\n"
            "**Ask me anything about your supply chain risks!** 🚀"
        )
    )
    await welcome_msg.send()


@cl.on_message
async def handle_message(msg: cl.Message):
    
    query = msg.content
    
 
    loading_msg = cl.Message(content="🔄 Analyzing your query...")
    await loading_msg.send()

    try:
        
        intent_result = predict_intent(query)
        intent = intent_result["intent"]
        confidence = intent_result["confidence"]

       
        entities = extract_entities_pipeline(query)

  
        region = None
        origin = None
        destination = None
        
        if entities.get("location"):
            locations = entities["location"]
            if isinstance(locations, list) and len(locations) > 0:
                region = locations[0]
                
                if len(locations) > 1:
                    origin = locations[0]
                    destination = locations[1]
            else:
                region = locations
        
        if not region:
            region = "Mumbai"

      
        incidents = []
        event_type = None
        
        if entities.get("event"):
            events = entities["event"]
            if isinstance(events, list):
                incidents = events
                event_type = events[0] if events else None
            else:
                incidents = [events]
                event_type = events
        
     
        risk_score = predict_risk(
            region=region,
            days=5,
            origin=origin,
            destination=destination,
            event_type=event_type,
            incidents=incidents
        )

     
        recent_incidents = incidents if incidents else ["port strike", "supplier outage"]
        weather_alert = "Typhoon warning" if region.lower() == "shanghai" else None
        
        advice = generate_recommendation(
            risk_score=risk_score,
            region=region,
            recent_incidents=recent_incidents,
            weather_alert=weather_alert,
            intent=intent
        )

     
        if risk_score >= 0.7:
            risk_emoji = "🔴"
            risk_level = "High"
        elif risk_score >= 0.4:
            risk_emoji = "🟡"
            risk_level = "Medium"
        else:
            risk_emoji = "🟢"
            risk_level = "Low"

      
        response = (
            f"### 📊 Supply Chain Risk Analysis\n\n"
            f"**Region:** {region}\n"
            f"**Intent:** {intent} (Confidence: {confidence:.2%})\n"
            f"**Entities:** {entities}\n"
        )
        
        if origin and destination:
            response += f"**Route:** {origin} → {destination}\n"
        
        if incidents:
            response += f"**⚠️ Detected Events:** {', '.join(incidents)}\n"
        
        response += f"**Risk Score:** {risk_emoji} **{risk_level}** ({risk_score:.2f})\n\n"
        response += f"**💡 Recommendation:**\n{advice['message']}\n"

  
        await loading_msg.remove()

      
        await cl.Message(content=response).send()


        alert_emoji = "🚨" if risk_score >= 0.7 else "⚠️" if risk_score >= 0.4 else "✅"
        await cl.Message(
            content=f"{alert_emoji} **Alert Level:** {advice['action'].upper()}"
        ).send()

    except Exception as e:
       
        print(f"Error processing query: {str(e)}")
        import traceback
        traceback.print_exc()
        
       
        try:
            await loading_msg.remove()
        except:
            pass
        

        await cl.Message(
            content=(
                f"❌ **Error:** An error occurred while processing your request.\n\n"
                f"**Details:** {str(e)}\n\n"
                f"Please try:\n"
                f"- Rephrasing your question\n"
                f"- Being more specific about locations\n"
                f"- Asking a different question\n\n"
                f"Example: \"What is the risk level for shipments from Mumbai to Singapore?\""
            )
        ).send()