# data_service.py
import traceback
from typing import Dict, List, Any
from data_structures import WEB_DATA_REPORTS
from gpt_analyzer import GPTAnalyzer

class DataAssessmentService:
    def __init__(self, api_key: str):
        print("Initializing DataAssessmentService")
        print(f"API Key available: {bool(api_key)}")
        self.analyzer = GPTAnalyzer(api_key)
        self.web_data = WEB_DATA_REPORTS
        print(f"Available categories: {list(self.web_data.keys())}")
        print(f"Web data structure loaded: {bool(self.web_data)}")

    def check_data_availability(self, analysis_result: dict) -> dict:
        print(f"Checking data availability for analysis result")
        print(f"Analysis result: {analysis_result}")
        
        availability = {
            "available_in_webdata": [],
            "needs_datalake": [],
            "not_available": [],
            "access_instructions": []
        }

        try:
            for required in analysis_result.get("required_reports", []):
                print(f"Processing required report: {required}")
                category = required.get("category")
                report_type = required.get("report_type")

                if not category or not report_type:
                    print(f"Missing category or report_type in required report")
                    continue

                print(f"Checking category: {category} for report type: {report_type}")
                if category in self.web_data:
                    matching_reports = []
                    for report_name, report_details in self.web_data[category].items():
                        required_fields = set(required.get("fields_needed", []))
                        available_fields = set(report_details["fields"].keys())

                        print(f"Checking report {report_name}")
                        print(f"Required fields: {required_fields}")
                        print(f"Available fields: {available_fields}")

                        if required_fields.issubset(available_fields):
                            matching_reports.append({
                                "name": report_details["name"],
                                "description": report_details["description"],
                                "access_path": f"/web-data/{category.lower()}/{report_name}",
                                "filters": report_details["filters"]
                            })
                            print(f"Found matching report: {report_name}")

                    if matching_reports:
                        availability["available_in_webdata"].extend(matching_reports)
                        filters_list = ', '.join(matching_reports[0]['filters'])
                        availability["access_instructions"].append(
                            f"Access {category} data through Web Data portal using filters: {filters_list}"
                        )
                    else:
                        availability["needs_datalake"].append({
                            "category": category,
                            "report_type": report_type,
                            "reason": "Required fields not available in Web Data"
                        })
                else:
                    availability["not_available"].append({
                        "category": category,
                        "report_type": report_type,
                        "reason": "Category not found in Web Data"
                    })

        except Exception as e:
            print(f"Error in check_data_availability: {str(e)}")
            print(traceback.format_exc())
            availability["error"] = str(e)

        print(f"Final availability result: {availability}")
        return availability

    def assess_request(self, request_text: str):
        print("=== Debug: Calling GPTAnalyzer ===")
        print(f"Request text: {request_text}")
        try:
            # 1. Analyze the request
            analysis = self.analyzer.analyze_request(request_text, list(self.web_data.keys()))
            print("GPT Response:", analysis)
            return analysis
        except Exception as e:
            print(f"Error during GPT analysis: {str(e)}")
            raise

          # 2. Check data availability
            availability = self.check_data_availability(analysis)
            print(f"Availability check completed: {availability}")

            # 3. Generate response
            response = {
                "status": "success",
                "request_analysis": {
                    "interpretation": analysis.get("interpretation", ""),
                    "confidence": analysis.get("confidence_score", "LOW")
                },
                "data_availability": {
                    "available_reports": availability["available_in_webdata"],
                    "access_instructions": availability["access_instructions"]
                }
            }

            if availability["needs_datalake"]:
                response["data_lake_requirements"] = {
                    "reports_needed": availability["needs_datalake"],
                    "estimated_processing_time": "2-3 business days",
                    "requires_it_support": True
                }

            if availability["not_available"]:
                response["unavailable_data"] = availability["not_available"]

            print(f"Final response prepared: {response}")
            return response
            
        except Exception as e:
            print(f"Error in assess_request: {str(e)}")
            print(traceback.format_exc())
            return {
                "status": "error",
                "message": f"Error processing request: {str(e)}"
            }