import os
import requests
from dotenv import load_dotenv
import logging

load_dotenv()

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class LLMExplainer:
    def __init__(self):
        self.api_key = os.environ.get("MISTRAL_API_KEY")
        self.api_url = "https://api.mistral.ai/v1/chat/completions"
        self.explanation_cache = {}

    def explain_discrepancy(self, row, match_status, books_vendor, gst_vendor, books_amount, gst_amount):
        """
        Generates a human-readable explanation for a discrepancy using Mistral AI.
        """
        if not self.api_key:
            return "API Key not configured. Discrepancy: " + match_status

        is_anomaly = row.get('IsAnomaly', False)
        
        if match_status == "Exact Match" and not is_anomaly:
            return "Records match perfectly."
            
        cache_key = (match_status, books_vendor, gst_vendor, books_amount, gst_amount, is_anomaly)
        if cache_key in self.explanation_cache:
            logger.info("Returning cached LLM explanation.")
            return self.explanation_cache[cache_key]

        prompt = f"""
        You are an expert AI financial auditor. Explain the following transaction in 1-2 concise, professional sentences. Provide a recommended action.
        
        Reconciliation Status: {match_status}
        Is Flagged as Anomaly: {is_anomaly}
        Anomaly Score: {row.get('AnomalyScore', 0.0)}
        Invoice ID: {row.get('InvoiceID', 'Unknown')}
        Books Vendor: {books_vendor}
        GST Vendor: {gst_vendor}
        Books Amount: {books_amount}
        GST Amount: {gst_amount}
        
        Explanation and Recommendation:
        """

        headers = {
            "Content-Type": "application/json",
            "Accept": "application/json",
            "Authorization": f"Bearer {self.api_key}"
        }

        data = {
            "model": "mistral-tiny", # Using tiny for faster responses, can upgrade to small/medium
            "messages": [
                {"role": "user", "content": prompt}
            ],
            "temperature": 0.3
        }

        try:
            response = requests.post(self.api_url, headers=headers, json=data, timeout=10)
            response.raise_for_status()
            result = response.json()
            explanation = result['choices'][0]['message']['content'].strip()
            self.explanation_cache[cache_key] = explanation
            return explanation
        except requests.exceptions.RequestException as e:
            logger.error(f"Error calling Mistral API: {e}")
            return f"API Error ({match_status}). Please check amounts and vendor names manually."
        except (KeyError, IndexError) as e:
             logger.error(f"Unexpected response format from Mistral API: {e}")
             return f"Error parsing AI response ({match_status})."

    def generate_explanations_batch(self, discrepancies_df):
        """
        Generates explanations for a dataframe of discrepancies.
        """
        explanations = []
        for _, row in discrepancies_df.iterrows():
            status = row.get('MatchStatus', 'Unknown')
            b_vendor = row.get('VendorName_books', 'N/A')
            g_vendor = row.get('VendorName_gst', 'N/A')
            b_amount = row.get('Amount_books', 0)
            g_amount = row.get('Amount_gst', 0)
            
            explanation = self.explain_discrepancy(row, status, b_vendor, g_vendor, b_amount, g_amount)
            explanations.append(explanation)
            
        return explanations