import streamlit as st
import pandas as pd
from typing import Optional, Dict
import os

from config import get_settings

@st.cache_resource
def init_ai_model():
    settings = get_settings()
    api_key = settings.groq_api_key
    
    if not api_key:
        return None
    
    try:
        from groq import Groq
        os.environ["GROQ_API_KEY"] = api_key
        return Groq()
    except Exception as e:
        st.error(f"AI configuration failed: {str(e)}")
        return None

def generate_ai_summary(model, df: pd.DataFrame, stats: Dict, outliers: Dict, lang: str = 'English') -> str:
    if not model:
        return "AI analysis unavailable - Groq API key not configured."
    
    language_instruction = "Respond in Norwegian." if lang == 'Norsk' else "Respond in English."
    
    try:
        materials = [k for k in stats.keys() if k != '_total_']
        
        context_parts = [
            "# Production Data Analysis Context",
            "## Overview",
            f"- Total Production: {stats['_total_']['total']:,.0f} kg",
            f"- Production Period: {stats['_total_']['work_days']} working days", 
            f"- Daily Average: {stats['_total_']['daily_avg']:,.0f} kg",
            f"- Materials Tracked: {len(materials)}",
            "",
            "## Material Breakdown:"
        ]
        
        for material in materials:
            info = stats[material]
            context_parts.append(
                f"- {material.title()}: {info['total']:,.0f} kg ({info['percentage']:.1f}%), "
                f"avg {info['daily_avg']:,.0f} kg/day"
            )
        
        daily_data = df.groupby('date')['weight_kg'].sum()
        trend_direction = "increasing" if daily_data.iloc[-1] > daily_data.iloc[0] else "decreasing"
        volatility = daily_data.std() / daily_data.mean() * 100
        
        context_parts.extend([
            "",
            "## Trend Analysis:",
            f"- Overall trend: {trend_direction}",
            f"- Production volatility: {volatility:.1f}% coefficient of variation",
            f"- Peak production: {daily_data.max():,.0f} kg",
            f"- Lowest production: {daily_data.min():,.0f} kg"
        ])
        
        total_outliers = sum(info['count'] for info in outliers.values())
        context_parts.extend([
            "",
            "## Quality Control:",
            f"- Total outliers detected: {total_outliers}",
            f"- Materials with quality issues: {sum(1 for info in outliers.values() if info['count'] > 0)}"
        ])
        
        if 'shift' in df.columns:
            shift_stats = df.groupby('shift')['weight_kg'].sum()
            context_parts.extend([
                "",
                "## Shift Performance:",
                f"- Day shift: {shift_stats.get('day', 0):,.0f} kg",
                f"- Night shift: {shift_stats.get('night', 0):,.0f} kg"
            ])
        
        context_text = "\n".join(context_parts)
        
        prompt = f"""
{language_instruction}

{context_text}

As an expert AI analyst for the Production Monitor platform, provide concise analysis.

Structure your response:

**PRODUCTION ASSESSMENT**
Evaluate status (Excellent/Good/Needs Attention) with brief justification.

**KEY FINDINGS**
Identify 3-4 critical insights. Reference platform features like Quality Check module or Production Trend chart.

**RECOMMENDATIONS**
Provide 2-3 actionable steps for management.

Keep under 300 words.
"""
        
        response = model.chat.completions.create(
            messages=[{"role": "user", "content": prompt}],
            model="llama-3.3-70b-versatile",
            temperature=0.7,
            max_tokens=1000
        )
        return response.choices[0].message.content
        
    except Exception as e:
        error_msg = str(e)
        if '429' in error_msg or 'quota' in error_msg.lower():
            return "AI analysis temporarily unavailable due to API quota limits. Please try again later."
        return f"AI analysis error: {error_msg}"

def query_ai(model, stats: Dict, question: str, df: Optional[pd.DataFrame] = None, lang: str = 'English') -> str:
    if not model:
        return "AI assistant not available - Please configure Groq API key"
    
    language_instruction = "Respond in Norwegian." if lang == 'Norsk' else "Respond in English."
    
    context_parts = [
        "Production Data Summary:",
        *[f"- {mat.title()}: {info['total']:,.0f}kg ({info['percentage']:.1f}%)" 
          for mat, info in stats.items() if mat != '_total_'],
        f"\nTotal Production: {stats['_total_']['total']:,.0f}kg across {stats['_total_']['work_days']} work days"
    ]
    
    if df is not None:
        available_cols = list(df.columns)
        context_parts.append(f"\nAvailable data fields: {', '.join(available_cols)}")
        
        if 'shift' in df.columns:
            shift_stats = df.groupby('shift')['weight_kg'].sum()
            context_parts.append(f"Shift breakdown: {dict(shift_stats)}")
        
        if 'day_name' in df.columns:
            day_stats = df.groupby('day_name')['weight_kg'].mean()
            context_parts.append(f"Average daily production: {dict(day_stats.round(0))}")
    
    context = "\n".join(context_parts) + f"\n\n{language_instruction}\n\nQuestion: {question}\nAnswer based on available data:"
    
    try:
        response = model.chat.completions.create(
            messages=[{"role": "user", "content": context}],
            model="llama-3.3-70b-versatile",
            temperature=0.7,
            max_tokens=500
        )
        return response.choices[0].message.content
    except Exception as e:
        error_msg = str(e)
        if '429' in error_msg or 'quota' in error_msg.lower():
            return "AI assistant temporarily unavailable due to API quota limits. Please try again later."
        return f"Error getting AI response: {error_msg}"