from fastapi import FastAPI, Request, UploadFile, File
from fastapi.responses import HTMLResponse, JSONResponse
from fastapi.staticfiles import StaticFiles
from fastapi.templating import Jinja2Templates
from ultralytics import YOLO
import aiofiles
import os
from pathlib import Path
import json
from jinja2 import Environment, FileSystemLoader
import uvicorn
from fastapi.middleware.cors import CORSMiddleware  # Import CORS middleware
# add the fuzzy
from fuzzywuzzy import process
from video_processing.video_frames_opt import extract_frames_and_detect_objects, lik, lik_prediction
import pandas as pd
import numpy as np
import cv2
import logging


# Configure logging to write to a file
logging.basicConfig(filename='uvicorn.log', level=logging.DEBUG)

# Create a JSON file with tracking results
# Define the Jinja2 environment
jinja_env = Environment(loader=FileSystemLoader("templates"))

# Set the environment variable
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"

# Initialize FastAPI
app = FastAPI()

# CORS policy
origins = ["*"]  # Allow requests from any origin
app.add_middleware(
    CORSMiddleware,
    allow_origins=origins,
    allow_credentials=True,
    allow_methods=["GET", "POST", "PUT", "DELETE", "OPTIONS"],
    allow_headers=["*"],
)

# Serve static files
app.mount("/static", StaticFiles(directory="static"), name="static")

# Templates directory
templates = Jinja2Templates(directory="templates")

# Load the exported ONNX model
#onnx_model = YOLO('yolov8n.onnx')

# Directory to save uploaded videos
video_directory = Path("uploaded_videos")

# Chunk size for reading video files
CHUNK_SIZE = 1024 * 1024  # 1 MB

@app.get("/", response_class=HTMLResponse)
async def read_root(request: Request):
    return templates.TemplateResponse("index.html", {"request": request})


@app.get("/upload/image", response_class=HTMLResponse)
async def upload_image_form(request: Request):
    return templates.TemplateResponse("upload_image.html", {"request": request})


@app.post("/upload/video", response_class=HTMLResponse)
async def upload_video(video: UploadFile = File(...)):
    # Ensure the directory exists, create it if it doesn't
    video_directory.mkdir(parents=True, exist_ok=True)

    # Define the path where the video will be saved
    video_path = video_directory / video.filename

    # Open the video file in binary write mode asynchronously and write the video data
    async with aiofiles.open(video_path, "wb") as buffer:
        while True:
            # Read the video data in chunks asynchronously
            chunk = await video.read(CHUNK_SIZE)
            if not chunk:
                break
            # Write the chunk to the file
            await buffer.write(chunk)
    
    results = extract_frames_and_detect_objects(str(video_path))

    ######################################################
    json_date = lik(results)
    #####################################################
    # Process the prediction
    processed_results = lik_prediction(json_date)
    ######################################################
    
    # Create a JSON file with tracking results
    json_results_path = video_directory / f"{video.filename}.json"
    open_path = str(json_results_path)

    with open(open_path, "w") as json_file:
        json_file.write(processed_results)

    # Return the processed JSON data
    return processed_results

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