import os
import sys
import json
import math
import string
import random
import argparse
import logging
from typing import List, Tuple, Optional, AsyncGenerator

import aiohttp
import uvicorn
import requests
import gradio as gr
from fastapi import FastAPI, Request
from starlette.responses import HTMLResponse

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s [%(levelname)s] %(message)s',
    handlers=[
        logging.StreamHandler(sys.stdout)
    ]
)
logger = logging.getLogger(__name__)

# Environment Variables
API_BASE = os.getenv("API_BASE", "env")
API_KEY = os.getenv("API_KEY")
OAI_API_KEY = os.getenv("OPENAI_API_KEY")
BASE_URL = os.getenv("OPENAI_BASE_URL", "https://api.openai.com/v1")
DEF_MODELS = [
    "chatgpt-4o-latest", "gpt-4-0125-preview", "gpt-4-0613", "gpt-4-1106-preview",
    "gpt-4-turbo-2024-04-09", "gpt-4-turbo-preview", "gpt-4-turbo", "gpt-4",
    "gpt-4o-2024-05-13", "gpt-4o-2024-08-06", "gpt-4o-2024-11-20",
    "gpt-4o-mini-2024-07-18", "gpt-4o-mini", "gpt-4o"
]

models = []
model_list = {}

# Exception for API Key handling
class APIKeyError(Exception):
    pass

def get_api_key(call: str = 'api_key') -> str:
    key = API_KEY if call == 'api_key' else (OAI_API_KEY if call == 'oai_api_key' else API_KEY)
    if ',' in key:
        selected_key = random.choice(key.split(','))
        logger.debug(f"Selected API key: {selected_key}")
        return selected_key
    return key

def encode_chat(messages: List[dict]) -> str:
    encoded = "\n".join(
        f"<|im_start|>{msg['role']}{f' [{msg['name']}]' if 'name' in msg else ''}\n{msg['content']}<|end_of_text|>"
        for msg in messages
    )
    logger.debug(f"Encoded chat: {encoded}")
    return encoded

def check_models():
    global BASE_URL, API_KEY
    if API_BASE == "env":
        try:
            response = requests.get(
                f"{BASE_URL}/models",
                headers={"Authorization": f"Bearer {get_api_key()}"}
            )
            response.raise_for_status()
            data = response.json()
            if 'data' not in data:
                logger.warning("No 'data' in response. Falling back to default BASE_URL and API_KEY.")
                BASE_URL = "https://api.openai.com/v1"
                API_KEY = OAI_API_KEY
            else:
                logger.info("Successfully fetched models from API_BASE.")
        except requests.RequestException as e:
            logger.error(f"Error testing API endpoint: {e}. Falling back to default BASE_URL and API_KEY.")
            BASE_URL = "https://api.openai.com/v1"
            API_KEY = OAI_API_KEY
    else:
        BASE_URL = "https://api.openai.com/v1"
        API_KEY = OAI_API_KEY
        logger.info("Using default BASE_URL and OAI_API_KEY.")

def load_models():
    global models, model_list
    models = sorted(DEF_MODELS)
    model_list = {
        "object": "list",
        "data": [{"id": model_id, "object": "model", "created": 0, "owned_by": "system"} for model_id in models]
    }
    logger.info(f"Loaded models: {models}")

def handle_api_keys():
    global API_KEY
    valid_keys = []
    keys = API_KEY.split(',') if ',' in API_KEY else [API_KEY]
    for key in keys:
        try:
            response = requests.get(
                f"{BASE_URL}/models",
                headers={"Authorization": f"Bearer {key.strip()}"}
            )
            response.raise_for_status()
            if 'data' in response.json():
                valid_keys.append(key.strip())
                logger.debug(f"Valid API key: {key.strip()}")
            else:
                logger.warning(f"API key {key.strip()} is invalid.")
        except requests.RequestException as e:
            logger.error(f"API key {key.strip()} is not valid or an error occurred: {e}")

    if not valid_keys:
        raise APIKeyError("No valid API keys are available.")
    API_KEY = ",".join(valid_keys)
    logger.info(f"Using API keys: {API_KEY}")

def moderate(messages: List[dict]) -> Optional[dict]:
    try:
        response = requests.post(
            f"{BASE_URL}/moderations",
            headers={
                "Content-Type": "application/json",
                "Authorization": f"Bearer {get_api_key('api_key')}"
            },
            json={"input": encode_chat(messages)}
        )
        response.raise_for_status()
        moderation_result = response.json()
        logger.debug(f"Moderation result: {moderation_result}")
    except requests.RequestException as e:
        logger.error(f"Moderation request failed: {e}. Trying fallback URL.")
        try:
            response = requests.post(
                "https://api.openai.com/v1/moderations",
                headers={
                    "Content-Type": "application/json",
                    "Authorization": f"Bearer {get_api_key('oai_api_key')}"
                },
                json={"input": encode_chat(messages)}
            )
            response.raise_for_status()
            moderation_result = response.json()
            logger.debug(f"Moderation result from fallback: {moderation_result}")
        except requests.RequestException as ex:
            logger.error(f"Fallback moderation request failed: {ex}")
            return None

    try:
        if isinstance(moderation_result, list):
            flagged = any(result.get("flagged", False) for result in moderation_result)
        else:
            flagged = moderation_result.get("flagged", False)
        if flagged:
            logger.info("Content flagged by moderation.")
            return moderation_result
    except KeyError as e:
        logger.error(f"Key error during moderation processing: {e}")
        return None

    return None

async def stream_chat(params: dict):
    async with aiohttp.ClientSession() as session:
        for attempt, url in enumerate([f"{BASE_URL}/chat/completions", "https://api.openai.com/v1/chat/completions"], start=1):
            try:
                async with session.post(
                    url,
                    headers={
                        "Authorization": f"Bearer {get_api_key('api_key')}",
                        "Content-Type": "application/json"
                    },
                    json=params
                ) as resp:
                    resp.raise_for_status()
                    async for line in resp.content:
                        if line:
                            line_str = line.decode('utf-8').strip()
                            if line_str.startswith("data: "):
                                line_str = line_str[6:]
                            if line_str == "[DONE]":
                                break
                            try:
                                message = json.loads(line_str)
                                yield message
                            except json.JSONDecodeError:
                                logger.warning("Failed to decode JSON from line.")
                                continue
                break  # Successful request, exit the loop
            except aiohttp.ClientError as e:
                logger.error(f"Stream chat request failed on attempt {attempt}: {e}")
                if attempt == 2:
                    return

def rnd(length: int = 8) -> str:
    result = ''.join(random.choices(string.ascii_letters + string.digits, k=length))
    logger.debug(f"Generated random string: {result}")
    return result

async def respond(
    message: str,
    history: List[Tuple[str, str]],
    model_name: str,
    max_tokens: int,
    temperature: float,
    top_p: float,
) -> AsyncGenerator[str, None]:
    messages = []
    for user_msg, assistant_msg in history:
        if user_msg:
            messages.append({"role": "user", "content": user_msg})
        if assistant_msg:
            messages.append({"role": "assistant", "content": assistant_msg})

    if message:
        messages.append({"role": "user", "content": message})
        moderation = moderate(messages)
        if moderation:
            reasons = []
            categories = moderation[0].get('categories', {}) if isinstance(moderation, list) else moderation.get('categories', {})
            for category, flagged in categories.items():
                if flagged:
                    reasons.append(category)
            if reasons:
                response = "[MODERATION] I'm sorry, but I can't assist with that.\n\nReasons:\n```\n" + "\n".join(f"{i+1}. {reason}" for i, reason in enumerate(reasons)) + "\n```"
            else:
                response = "[MODERATION] I'm sorry, but I can't assist with that."
            logger.info("Message flagged by moderation.")
            yield response
            return

    params = {
        "model": model_name,
        "messages": messages,
        "max_tokens": max_tokens,
        "temperature": temperature,
        "top_p": top_p,
        "user": rnd(),
        "stream": True
    }

    response_text = ""
    async for token in stream_chat(params):
        if token and 'choices' in token:
            delta = token['choices'][0].get('delta', {})
            content = delta.get("content", delta.get("refusal", ""))
            response_text += content
            yield response_text

def create_gradio_interface() -> gr.ChatInterface:
    return gr.ChatInterface(
        respond,
        title="gpt-4o-mini",
        description="The chat is back online for a not-so-long time.",
        additional_inputs=[
            gr.Dropdown(choices=models, value="gpt-4o-mini", label="Model"),
            gr.Slider(minimum=1, maximum=4096, value=4096, step=1, label="Max new tokens"),
            gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.05, label="Temperature"),
            gr.Slider(minimum=0.05, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
        ],
        css="footer{display:none !important}",
        head="""<script>
            if(!confirm("By using our application, which integrates with OpenAI's API, you acknowledge and agree to the following terms regarding the data you provide:\\n\\n1. Data Collection: This application may log the following data through the Gradio endpoint or the API endpoint: message requests (including messages, responses, model settings, and images sent along with the messages), images that were generated (including only the prompt and the image), search tool calls (including query, search results, summaries, and output responses), and moderation checks (including input and output).\\n2. Data Retention and Removal: Data is retained until further notice or until a specific request for removal is made.\\n3. Data Usage: The collected data may be used for various purposes, including but not limited to, administrative review of logs, AI training, and publication as a dataset.\\n4. Privacy: Please avoid sharing any personal information.\\n\\nBy continuing to use our application, you explicitly consent to the collection, use, and potential sharing of your data as described above. If you disagree with our data collection, usage, and sharing practices, we advise you not to use our application.")) location.href="/declined";
        </script>"""
    )

def create_fastapi_app() -> FastAPI:
    app = FastAPI()

    @app.get("/declined")
    def declined():
        return HTMLResponse(content="""
            <html>
                <head>
                    <title>Declined</title>
                </head>
                <body>
                    <p>Ok, you can go back to Hugging Face. I just didn't have any idea how to handle decline so you are redirected here.</p><br/>
                    <a href="/">Go back</a>
                </body>
            </html>
        """)

    gradio_app = create_gradio_interface()
    app = gr.mount_gradio_app(app, gradio_app, path="/")
    return app

class ArgParser(argparse.ArgumentParser):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.add_argument("-s", "--server", type=str, default="0.0.0.0", help="Server host.")
        self.add_argument("-p", "--port", type=int, default=7860, help="Server port.")
        self.add_argument("-d", "--dev", action="store_true", help="Run in development mode.")
        self.args = self.parse_args(sys.argv[1:])

def main():
    try:
        handle_api_keys()
        load_models()
        check_models()
    except APIKeyError as e:
        logger.critical(e)
        sys.exit(1)

    app = create_fastapi_app()
    args = ArgParser().args

    uvicorn.run(
        "main:app",
        host=args.server,
        port=args.port,
        reload=args.dev
    )

if __name__ == "__main__":
    main()