# File: prompts.py

DOCUMENT_OUTLINE_PROMPT_SYSTEM = """You are a document generator. Provide the outline of the document requested in <prompt></prompt> in JSON format.
Include sections and subsections if required. Use the "Content" field to provide a specific prompt or instruction for generating content for that particular section or subsection.

OUTPUT IN FOLLOWING JSON FORMAT enclosed in <output> tags
<output>
{
    "Document": {
      "Title": "Document Title",
      "Author": "Author Name",
      "Date": "YYYY-MM-DD",
      "Version": "1.0",

      "Sections": [
        {
          "SectionNumber": "1",
          "Title": "Section Title",
          "Content": "Specific prompt or instruction for generating content for this section",
          "Subsections": [
            {
              "SectionNumber": "1.1",
              "Title": "Subsection Title",
              "Content": "Specific prompt or instruction for generating content for this subsection"
            }
          ]
        }
      ]
    }
  }
</output>"""

DOCUMENT_OUTLINE_PROMPT_USER = """<prompt>{query}</prompt>"""

DOCUMENT_SECTION_PROMPT_SYSTEM = """You are a document generator, You need to output only the content requested in the section in the prompt.
FORMAT YOUR OUTPUT AS MARKDOWN ENCLOSED IN <response></response> tags
<overall_objective>{overall_objective}</overall_objective>
<document_layout>{document_layout}</document_layout>"""

DOCUMENT_SECTION_PROMPT_USER = """<prompt>Output the content for the section "{section_or_subsection_title}" formatted as markdown. Follow this instruction: {content_instruction}</prompt>"""

# File: app.py
import os
import json
import re
import asyncio
from typing import List, Dict, Optional, Any, Callable
from openai import OpenAI
import logging
import functools
from fastapi import APIRouter, HTTPException, Request
from fastapi.responses import StreamingResponse
from pydantic import BaseModel
from fastapi_cache.decorator import cache

logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

def log_execution(func: Callable) -> Callable:
    @functools.wraps(func)
    def wrapper(*args: Any, **kwargs: Any) -> Any:
        logger.info(f"Executing {func.__name__}")
        try:
            result = func(*args, **kwargs)
            logger.info(f"{func.__name__} completed successfully")
            return result
        except Exception as e:
            logger.error(f"Error in {func.__name__}: {e}")
            raise
    return wrapper

class AIClient:
    def __init__(self):
        self.client = OpenAI(
            base_url="https://openrouter.ai/api/v1",
            api_key="sk-or-v1-"+os.environ['OPENROUTER_API_KEY']
        )
    
    @log_execution
    def generate_response(
        self,
        messages: List[Dict[str, str]],
        model: str = "openai/gpt-4o-mini",
        max_tokens: int = 32000
    ) -> Optional[str]:
        if not messages:
            return None
        response = self.client.chat.completions.create(
            model=model,
            messages=messages,
            max_tokens=max_tokens,
            stream=False
        )
        return response.choices[0].message.content

class DocumentGenerator:
    def __init__(self, ai_client: AIClient):
        self.ai_client = ai_client
        self.document_outline = None
        self.content_messages = []

    @staticmethod
    def extract_between_tags(text: str, tag: str) -> str:
        pattern = f"<{tag}>(.*?)</{tag}>"
        match = re.search(pattern, text, re.DOTALL)
        return match.group(1).strip() if match else ""

    @staticmethod
    def remove_duplicate_title(content: str, title: str, section_number: str) -> str:
        patterns = [
            rf"^#+\s*{re.escape(section_number)}(?:\s+|\s*:\s*|\.\s*){re.escape(title)}",
            rf"^#+\s*{re.escape(title)}",
            rf"^{re.escape(section_number)}(?:\s+|\s*:\s*|\.\s*){re.escape(title)}",
            rf"^{re.escape(title)}",
        ]
        
        for pattern in patterns:
            content = re.sub(pattern, "", content, flags=re.MULTILINE | re.IGNORECASE)
        
        return content.lstrip()

    @log_execution
    def generate_document_outline(self, query: str, max_retries: int = 3) -> Optional[Dict]:
        messages = [
            {"role": "system", "content": DOCUMENT_OUTLINE_PROMPT_SYSTEM},
            {"role": "user", "content": DOCUMENT_OUTLINE_PROMPT_USER.format(query=query)}
        ]
        
        for attempt in range(max_retries):
            outline_response = self.ai_client.generate_response(messages, model="openai/gpt-4o")
            outline_json_text = self.extract_between_tags(outline_response, "output")
            
            try:
                self.document_outline = json.loads(outline_json_text)
                return self.document_outline
            except json.JSONDecodeError as e:
                if attempt < max_retries - 1:
                    logger.warning(f"Failed to parse JSON (attempt {attempt + 1}): {e}")
                    logger.info("Retrying...")
                else:
                    logger.error(f"Failed to parse JSON after {max_retries} attempts: {e}")
                    return None

    @log_execution
    def generate_content(self, title: str, content_instruction: str, section_number: str) -> str:
        self.content_messages.append({
            "role": "user",
            "content": DOCUMENT_SECTION_PROMPT_USER.format(
                section_or_subsection_title=title,
                content_instruction=content_instruction
            )
        })
        section_response = self.ai_client.generate_response(self.content_messages)
        content = self.extract_between_tags(section_response, "response")
        content = self.remove_duplicate_title(content, title, section_number)
        self.content_messages.append({
            "role": "assistant",
            "content": section_response
        })
        return content

class MarkdownConverter:
    @staticmethod
    def slugify(text: str) -> str:
        return re.sub(r'\W+', '-', text.lower())

    @classmethod
    def generate_toc(cls, sections: List[Dict]) -> str:
        toc = "<div style='page-break-before: always;'></div>\n\n"
        toc += "<h2 style='color: #2c3e50; text-align: center;'>Table of Contents</h2>\n\n"
        toc += "<nav style='background-color: #f8f9fa; padding: 20px; border-radius: 5px; line-height: 1.6;'>\n\n"
        for section in sections:
            section_number = section['SectionNumber']
            section_title = section['Title']
            toc += f"<p><a href='#{cls.slugify(section_title)}' style='color: #3498db; text-decoration: none;'>{section_number}. {section_title}</a></p>\n\n"
            
            for subsection in section.get('Subsections', []):
                subsection_number = subsection['SectionNumber']
                subsection_title = subsection['Title']
                toc += f"<p style='margin-left: 20px;'><a href='#{cls.slugify(subsection_title)}' style='color: #2980b9; text-decoration: none;'>{subsection_number} {subsection_title}</a></p>\n\n"
        
        toc += "</nav>\n\n"
        return toc

    @classmethod
    def convert_to_markdown(cls, document: Dict) -> str:
        markdown = "<div style='text-align: center; padding-top: 33vh;'>\n\n"
        markdown += f"<h1 style='color: #2c3e50; border-bottom: 2px solid #3498db; padding-bottom: 10px; display: inline-block;'>{document['Title']}</h1>\n\n"
        markdown += f"<p style='color: #7f8c8d;'><em>By {document['Author']}</em></p>\n\n"
        markdown += f"<p style='color: #95a5a6;'>Version {document['Version']} | {document['Date']}</p>\n\n"
        markdown += "</div>\n\n"
        
        markdown += cls.generate_toc(document['Sections'])
        
        markdown += "<div style='max-width: 800px; margin: 0 auto; font-family: \"Segoe UI\", Arial, sans-serif; line-height: 1.6;'>\n\n"
        
        for section in document['Sections']:
            markdown += "<div style='page-break-before: always;'></div>\n\n"
            section_number = section['SectionNumber']
            section_title = section['Title']
            markdown += f"<h2 id='{cls.slugify(section_title)}' style='color: #2c3e50; border-bottom: 1px solid #bdc3c7; padding-bottom: 5px;'>{section_number}. {section_title}</h2>\n\n"
            markdown += f"<div style='color: #34495e; margin-bottom: 20px;'>\n\n{section['Content']}\n\n</div>\n\n"
            
            for subsection in section.get('Subsections', []):
                subsection_number = subsection['SectionNumber']
                subsection_title = subsection['Title']
                markdown += f"<h3 id='{cls.slugify(subsection_title)}' style='color: #34495e;'>{subsection_number} {subsection_title}</h3>\n\n"
                markdown += f"<div style='color: #34495e; margin-bottom: 20px;'>\n\n{subsection['Content']}\n\n</div>\n\n"
        
        markdown += "</div>"
        return markdown

router = APIRouter()

class DocumentRequest(BaseModel):
    query: str

class JsonDocumentResponse(BaseModel):
    json_document: Dict

class MarkdownDocumentRequest(BaseModel):
    json_document: Dict
    query: str

async def generate_document_stream(document_generator: DocumentGenerator, document_outline: Dict, query: str):
    document_generator.document_outline = document_outline
    
    overall_objective = query
    document_layout = json.dumps(document_generator.document_outline, indent=2)

    document_generator.content_messages = [
        {
            "role": "system",
            "content": DOCUMENT_SECTION_PROMPT_SYSTEM.format(
                overall_objective=overall_objective,
                document_layout=document_layout
            )
        }
    ]

    for section in document_generator.document_outline["Document"].get("Sections", []):
        section_title = section.get("Title", "")
        section_number = section.get("SectionNumber", "")
        content_instruction = section.get("Content", "")
        logging.info(f"Generating content for section: {section_title}")
        content = document_generator.generate_content(section_title, content_instruction, section_number)
        section["Content"] = content
        yield json.dumps({
            "type": "document_section", 
            "content": {
                "section_number": section_number,
                "section_title": section_title,
                "content": content
            }
        }) + "\n"

        for subsection in section.get("Subsections", []):
            subsection_title = subsection.get("Title", "")
            subsection_number = subsection.get("SectionNumber", "")
            subsection_content_instruction = subsection.get("Content", "")
            logging.info(f"Generating content for subsection: {subsection_title}")
            content = document_generator.generate_content(subsection_title, subsection_content_instruction, subsection_number)
            subsection["Content"] = content
            yield json.dumps({
                "type": "document_section", 
                "content": {
                    "section_number": subsection_number,
                    "section_title": subsection_title,
                    "content": content
                }
            }) + "\n"

    markdown_document = MarkdownConverter.convert_to_markdown(document_generator.document_outline["Document"])
    yield json.dumps({"type": "complete_document", "content": markdown_document}) + "\n"

@cache(expire=600*24*7)
@router.post("/generate-document/json", response_model=JsonDocumentResponse)
async def generate_document_outline_endpoint(request: DocumentRequest):
    ai_client = AIClient()
    document_generator = DocumentGenerator(ai_client)
    
    try:
        json_document = document_generator.generate_document_outline(request.query)
        
        if json_document is None:
            raise HTTPException(status_code=500, detail="Failed to generate a valid document outline")
        
        return JsonDocumentResponse(json_document=json_document)
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@router.post("/generate-document/markdown-stream")
async def generate_markdown_document_stream_endpoint(request: MarkdownDocumentRequest):
    ai_client = AIClient()
    document_generator = DocumentGenerator(ai_client)
    
    async def stream_generator():
        try:
            async for chunk in generate_document_stream(document_generator, request.json_document, request.query):
                yield chunk
        except Exception as e:
            yield json.dumps({"type": "error", "content": str(e)}) + "\n"

    return StreamingResponse(stream_generator(), media_type="application/x-ndjson")

###########################################
class MarkdownDocumentResponse(BaseModel):
    markdown_document: str

@router.post("/generate-document-test", response_model=MarkdownDocumentResponse)
async def test_generate_document_endpoint(request: DocumentRequest):
    try:
        # Load JSON document from file
        json_path = os.path.join("output/document_generator", "ai-chatbot-prd.json")
        with open(json_path, "r") as json_file:
            json_document = json.load(json_file)

        # Load Markdown document from file
        md_path = os.path.join("output/document_generator", "ai-chatbot-prd.md")
        with open(md_path, "r") as md_file:
            markdown_document = md_file.read()

        return MarkdownDocumentResponse(markdown_document=markdown_document)
    except FileNotFoundError:
        raise HTTPException(status_code=404, detail="Test files not found")
    except json.JSONDecodeError:
        raise HTTPException(status_code=500, detail="Error parsing JSON file")
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

class CacheTestResponse(BaseModel):
    result: str
    execution_time: float

@router.get("/test-cache/{test_id}", response_model=CacheTestResponse)
@cache(expire=60)  # Cache for 1 minute
async def test_cache(test_id: int):
    start_time = time.time()
    
    # Simulate some time-consuming operation
    await asyncio.sleep(2)
    
    result = f"Test result for ID: {test_id}"
    
    end_time = time.time()
    execution_time = end_time - start_time
    
    return CacheTestResponse(
        result=result,
        execution_time=execution_time
    )