import json
import os
from dotenv import load_dotenv
from openai import OpenAI
from tqdm import tqdm
from scripts.regulatory_change_foundation import CONTEXT_CATEGORIES
from scripts.utility_functions import call_nlp_service, render_prompt


# Load environment variables from .env file
load_dotenv()

api_key = os.getenv("OPENAI_API_KEY")
openai_client = OpenAI(api_key=api_key)


def preprocess_text_with_nlp(text, max_chunk_size=512, overlap=50):
    result = call_nlp_service({"text": text}, "preprocess_text_with_nlp_llm")
    return result["chunks"], result["preprocessed_data"]


def create_prompt(chunk, preprocessed_data):
    return render_prompt(chunk, include_nlp=True, preprocessed_data=preprocessed_data)


def search_for_regulatory_changes(chunks, preprocessed_data, subtitle):
    results = []

    for chunk in chunks:
        response = openai_client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[
                {
                    "role": "system",
                    "content": "You are a legal expert specializing in analyzing German regulatory documents with a focus on identifying regulatory changes. Only return JSON output.",
                },
                {"role": "user", "content": create_prompt(chunk, preprocessed_data)},
            ],
            temperature=0.7,
            max_tokens=1024,
        )

        try:
            result = json.loads(response.choices[0].message.content)
            if result.get("changes_detected", False):
                result["location"] = {"subtitle": subtitle}  # Use subtitle as location
                result["source_text"] = chunk
                results.append(result)
        except json.JSONDecodeError:
            continue

    return results


def detect_regulatory_changes(text_content, subtitle):
    """
    Main function to detect regulatory changes from text content.

    Args:
        text_content (str): The raw text content to analyze
        subtitle (str): The subtitle associated with the content

    Returns:
        dict: Structured output containing detected changes and analysis summary
    """

    # Preprocess text with enhanced NLP
    chunks, preprocessed_data = preprocess_text_with_nlp(text_content)

    # Classify changes using NLP insights
    results = search_for_regulatory_changes(chunks, preprocessed_data, subtitle)

    return results


def llm_regulatory_change_detector(hierarchical_structure):
    if hierarchical_structure:
        analysis_summary = {
            "total_changes_detected": 0,
            "changes_by_type": {"additions": 0, "deletions": 0, "modifications": 0},
        }
        subtitles = {}

        # Iterate over sections and analyze content
        for section in tqdm(
            hierarchical_structure["sections"], desc="Analyzing Sections"
        ):
            subtitle = section["subtitle"]
            content = section["content"]
            if isinstance(content, list):
                content = "\n".join(content)

            # Detect changes for this subtitle
            changes = detect_regulatory_changes(content, subtitle)

            # Update analysis summary
            for change in changes:
                analysis_summary["total_changes_detected"] += len(
                    change["classifications"]
                )
                for classification in change["classifications"]:
                    change_type = classification["change_type"]
                    analysis_summary["changes_by_type"][f"{change_type}s"] += 1

            # Group changes by subtitle
            subtitles[subtitle] = []
            for change in changes:
                for classification in change["classifications"]:
                    change_subtype = (
                        "context"
                        if classification["change"] in CONTEXT_CATEGORIES
                        else "scope"
                    )
                    subtitles[subtitle].append(
                        {
                            "change": classification["change"],
                            "change_type": classification["change_type"],
                            "change_subtype": change_subtype,
                            "relevant_text": classification["relevant_text"],
                            "explanation": classification["explanation"],
                            "nlp_evidence": classification["evidence"],
                        }
                    )

        # Combine analysis summary and grouped changes
        final_output = {"analysis_summary": analysis_summary, "results": subtitles}

        return final_output