from fastapi import FastAPI, HTTPException, Request
from fastapi.staticfiles import StaticFiles
from pytubefix import YouTube
from pytubefix.exceptions import PytubeFixError
from concurrent.futures import ThreadPoolExecutor
import asyncio
import aiohttp
import aiofiles
import tempfile
import uuid
import base64
import io
import os
import random
import traceback
import string
import json

app = FastAPI()

MODAL_BASE_URL = "https://sxqib--api-fastapi-app.modal.run"

def generate_hash(length=12):
    # Characters that can appear in the hash
    characters = string.ascii_lowercase + string.digits
    # Generate a random string of the specified length
    hash_string = ''.join(random.choice(characters) for _ in range(length))
    return hash_string

@app.get("/")
async def read_root():
    return {"message": "Saqib's API"}

# Create a directory to store MP3 files if it doesn't exist
AUDIO_DIR = "audio_files"
os.makedirs(AUDIO_DIR, exist_ok=True)

# Create a directory for storing output files
OUTPUT_DIR = "output"
os.makedirs(OUTPUT_DIR, exist_ok=True)

# Mount the audio directory
app.mount("/audio", StaticFiles(directory=AUDIO_DIR), name="audio")

# Mount the output directory
app.mount("/output", StaticFiles(directory=OUTPUT_DIR), name="output")

thread_pool = ThreadPoolExecutor(max_workers=2)

async def run_ffmpeg_async(ffmpeg_command):
    loop = asyncio.get_running_loop()
    await loop.run_in_executor(thread_pool, ffmpeg_command)

async def download_file(url: str, suffix: str):
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as response:
            if response.status != 200:
                raise HTTPException(status_code=400, detail=f"Failed to download file from {url}")
            with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as temp_file:
                temp_file.write(await response.read())
                return temp_file.name

@app.post("/add_audio_to_image")
async def add_audio_to_image(request: Request):
    try:
        # Generate a unique filename
        output_filename = f"{uuid.uuid4()}.mp4"
        output_path = os.path.join(OUTPUT_DIR, output_filename)

        # Call the modal API with the request data and download the output file
        data = await request.json()
        async with aiohttp.ClientSession() as session:
            async with session.post(f"{MODAL_BASE_URL}/add_audio_to_image", json=data) as response:
                if response.status != 200:
                    raise HTTPException(status_code=500, detail="Failed to process request")
                output_data = await response.read()
                async with aiofiles.open(output_path, "wb") as f:
                    await f.write(output_data)

        # Return the URL path to the output file
        return f"https://sxqib-api.hf.space/output/{output_filename}"
    except Exception as e:
        print(f"An error occurred: {str(e)}")
        print(traceback.format_exc())
        raise HTTPException(status_code=500, detail=f"An unexpected error occurred: {str(e)}")

@app.post("/concatenate_videos")
async def concatenate_videos(request: Request):
    try:
        # Generate a unique filename for the output
        output_filename = f"{uuid.uuid4()}.mp4"
        output_path = os.path.join(OUTPUT_DIR, output_filename)

        # Call the modal API with the request data and download the output file
        data = await request.json()
        async with aiohttp.ClientSession() as session:
            async with session.post(f"{MODAL_BASE_URL}/concatenate_videos", json=data) as response:
                if response.status != 200:
                    raise HTTPException(status_code=500, detail="Failed to process request")
                output_data = await response.read()
                async with aiofiles.open(output_path, "wb") as f:
                    await f.write(output_data)

        # Return the URL path to the output file
        return f"https://sxqib-api.hf.space/output/{output_filename}"

    except Exception as e:
        print(f"An error occurred: {str(e)}")
        print(traceback.format_exc())
        raise HTTPException(status_code=500, detail=f"An unexpected error occurred: {str(e)}")

@app.get("/get_audio")
async def get_audio(request: Request):  
    try:
        # Generate a unique filename
        unique_filename = f"{uuid.uuid4().hex}.mp3"
        out_file = os.path.join(AUDIO_DIR, unique_filename)

        # Call the modal API with the request parameters and download the output file
        data = request.query_params
        async with aiohttp.ClientSession() as session:
            async with session.get(f"{MODAL_BASE_URL}/get_audio", params=data) as response:
                if response.status != 200:
                    raise HTTPException(status_code=500, detail="Failed to process request")
                audio_data = await response.read()
                async with aiofiles.open(out_file, "wb") as f:
                    await f.write(audio_data)

        # Return the URL path to the output file
        return f"https://sxqib-api.hf.space/audio/{unique_filename}"
    except Exception as e:
        print(f"An error occurred: {str(e)}")
        print(traceback.format_exc())
        raise HTTPException(status_code=500, detail=f"An unexpected error occurred: {str(e)}")

@app.post("/whisper")
async def whisper(request: Request):
    data = await request.json()  # Extracting JSON data from request
    if "audio_url" not in data:
        raise HTTPException(status_code=400, detail="audio_url not found in request")
    url = data["audio_url"]

    headers = {
        'Accept': 'application/json, text/plain, */*',
        'Accept-Language': 'en-US,en;q=0.9',
        'Cache-Control': 'no-cache',
        'Connection': 'keep-alive',
        'Content-Type': 'application/json',
        'DNT': '1',
		'Origin': 'https://deepinfra.com',
        'Pragma': 'no-cache',
		'Referer': 'https://deepinfra.com/',
        'Sec-Fetch-Dest': 'empty',
        'Sec-Fetch-Mode': 'cors',
        'Sec-Fetch-Site': 'same-site',
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/124.0.0.0 Safari/537.36 Edg/124.0.0.0',
        'sec-ch-ua': '"Chromium";v="124", "Microsoft Edge";v="124", "Not-A.Brand";v="99"',
        'sec-ch-ua-mobile': '?0',
        'sec-ch-ua-platform': '"Windows"',
    }

    # Async HTTP request to get the audio file
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as resp:
            if resp.status != 200:
                return f"Failed to download audio: {resp.status}"
            audio_data = await resp.read()

            # Encode the audio data to base64
            audio_base64 = base64.b64encode(audio_data).decode("utf-8")

            json_data = '{"audio": "' + audio_base64 + '"}'

            # Post request to the API
            async with session.post(f'https://api.deepinfra.com/v1/inference/openai/whisper-large', headers=headers, data=json_data) as post_resp:
                if post_resp.status != 200:
                    return f"API request failed: {post_resp.status}"
                return await post_resp.json()

@app.post("/img2location")
async def img2location(request: Request):
    request_json = await request.json()
    image_url = request_json.get("image_url", None)
    
    if not image_url:
        raise HTTPException(status_code=400, detail="image_url not found in request")

    headers = {
        'accept': '*/*',
        'accept-language': 'en-US,en;q=0.9',
        'cache-control': 'no-cache',
        'dnt': '1',
        'origin': 'https://geospy.ai',
        'pragma': 'no-cache',
        'priority': 'u=1, i',
        'referer': 'https://geospy.ai/',
        'sec-ch-ua': '"Chromium";v="124", "Microsoft Edge";v="124", "Not-A.Brand";v="99"',
        'sec-ch-ua-mobile': '?0',
        'sec-ch-ua-platform': '"Windows"',
        'sec-fetch-dest': 'empty',
        'sec-fetch-mode': 'cors',
        'sec-fetch-site': 'cross-site',
        'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/124.0.0.0 Safari/537.36 Edg/124.0.0.0',
    }

    async with aiohttp.ClientSession() as session:
        # Fetch the image from the URL
        async with session.get(image_url) as img_response:
            if img_response.status != 200:
                return f"Failed to fetch image: HTTP {img_response.status}"
            image_data = await img_response.read()

        # Using BytesIO to handle the byte content
        data = aiohttp.FormData()
        data.add_field('image', io.BytesIO(image_data), filename="image.png", content_type='image/png')

        # Sending the POST request
        async with session.post("https://locate-image-7cs5mab6na-uc.a.run.app/", headers=headers, data=data) as response:
            if response.status != 200:
                return f"Failed to upload image: HTTP {response.status}"
            json_response = await response.json()
            if json_response["message"]["latitude"] and json_response["message"]["longitude"]:
                json_response["message"]["latitude"] = str(json_response["message"]["latitude"])
                json_response["message"]["longitude"] = str(json_response["message"]["longitude"])
            return json_response

@app.post("/pixart-sigma")
async def pixart_sigma(request: Request):
    request_json = await request.json()
    prompt = request_json.get("prompt", None)
    negative_prompt = request_json.get("negative_prompt", "")
    style = request_json.get("style", "(No style)")
    use_negative_prompt = request_json.get("use_negative_prompt", True)
    num_imgs = request_json.get("num_imgs", 1)
    seed = request_json.get("seed", 0)
    width = request_json.get("width", 1024)
    height = request_json.get("height", 1024)
    schedule = request_json.get("schedule", "DPM-Solver")
    dpms_guidance_scale = request_json.get("dpms_guidance_scale", 4.5)
    sas_guidance_scale = request_json.get("sas_guidance_scale", 3)
    dpms_inference_steps = request_json.get("dpms_inference_steps", 14)
    sas_inference_steps = request_json.get("sas_inference_steps", 25)
    randomize_seed = request_json.get("randomize_seed", True)

    hash = generate_hash()

    headers = {
        'accept': '*/*'
    }

    params = {
        '__theme': 'light',
    }

    json_data = {
        'data': [
            prompt,
            negative_prompt,
            style,
            use_negative_prompt,
            num_imgs,
            seed,
            width,
            height,
            schedule,
            dpms_guidance_scale,
            sas_guidance_scale,
            dpms_inference_steps,
            sas_inference_steps,
            randomize_seed,
        ],
        'event_data': None,
        'fn_index': 3,
        'trigger_id': 7,
        'session_hash': hash,
    }

    async with aiohttp.ClientSession() as session:
        async with session.post(f'https://pixart-alpha-pixart-sigma.hf.space/queue/join', params=params, headers=headers, json=json_data, ssl=False) as response:
            print(response.status)

        params = {
            'session_hash': hash,
        }

        async with session.get(f'https://pixart-alpha-pixart-sigma.hf.space/queue/data', params=params, headers=headers, ssl=False) as response:
            async for line in response.content:
                try:
                    if line:
                        line = line.decode('utf-8')
                        line = line.replace('data: ', '')
                        line_json = json.loads(line)
                        if line_json["msg"] == "process_completed":
                            image_url = line_json["output"]["data"][0][0]["image"]["url"]
                            return {"image_url": image_url}
                except:
                    pass

# if __name__ == "__main__":
#     import uvicorn
#     uvicorn.run(app, host="0.0.0.0", port=8000)