# 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 time
import asyncio
from typing import List, Dict, Optional, Any, Callable
from openai import OpenAI
import logging
import functools
from fastapi import APIRouter, HTTPException
from pydantic import BaseModel
from fastapi_cache.decorator import cache
from starlette.responses import StreamingResponse

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)
    async def wrapper(*args: Any, **kwargs: Any) -> Any:
        logger.info(f"Executing {func.__name__}")
        try:
            result = await 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
    async 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
        loop = asyncio.get_event_loop()
        response = await loop.run_in_executor(None, functools.partial(
            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
    async 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 = await 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
    async 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 = await 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

    @log_execution
    async def generate_full_document(self, document_outline: Dict, query: str):
        self.document_outline = document_outline
        
        overall_objective = query
        document_layout = json.dumps(self.document_outline, indent=2)

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

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

            for subsection in section.get("Subsections", []):
                subsection_title = subsection.get("Title", "")
                subsection_number = subsection.get("SectionNumber", "")
                subsection_content_instruction = subsection.get("Content", "")
                logger.info(f"Generating content for subsection: {subsection_title}")
                subsection["Content"] = await self.generate_content(subsection_title, subsection_content_instruction, subsection_number)
                yield json.dumps({"type": "document_subsection", "content": subsection}) + "\n"

        # Generate the complete markdown document
        full_document = self.document_outline
        markdown_document = MarkdownConverter.convert_to_markdown(full_document["Document"])
        yield json.dumps({"type": "complete_document", "content": markdown_document}) + "\n"

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:
        # First page with centered content
        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"
        
        # Table of Contents on the second page
        markdown += cls.generate_toc(document['Sections'])
        
        # Main content
        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

@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:
        # Generate the document outline
        json_document = await 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")
async def generate_markdown_document_endpoint(request: MarkdownDocumentRequest):
    ai_client = AIClient()
    document_generator = DocumentGenerator(ai_client)
    
    async def event_stream():
        try:
            # Generate the full document content and stream it
            async for section in document_generator.generate_full_document(request.json_document, request.query):
                yield section
        except Exception as e:
            yield json.dumps({"type": "error", "message": str(e)}) + "\n"

    return StreamingResponse(event_stream(), media_type="application/json")

@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
    )