import os
import numpy as np
from requests import get
import streamlit as st
import cv2
from ultralytics import YOLO
import shutil
import easyocr
import imutils



PREDICTION_PATH = os.path.join('.', 'predictions')


@st.cache_resource
def load_od_model():
    finetuned_model = YOLO('cc_detect_best.pt')
    return finetuned_model


@st.cache_resource
def load_easyocr():
    reader = easyocr.Reader(['en'])
    return reader


def decode_text(type: str):
    reader = load_easyocr()
    output_crop_path = os.path.join(PREDICTION_PATH, 'predict', 'crops', type)
    ocr_txt = ''
    if os.path.exists(output_crop_path):
            crop_file = os.listdir(output_crop_path)[0]
            crop_img_path = os.path.join(output_crop_path, crop_file)
            crop_img = cv2.imread(crop_img_path)

            increase = cv2.resize(crop_img, None, fx = 2, fy = 2, interpolation = cv2.INTER_CUBIC)
            if type == 'card_number':
                increase = cv2.resize(crop_img, None, fx = 5, fy = 5, interpolation = cv2.INTER_CUBIC)
        
            gray = cv2.cvtColor(increase, cv2.COLOR_BGR2GRAY)  
            
            value, thresh = cv2.threshold(gray,0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
        
            kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2,2))
            opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=1)
            
            # Find contours and remove small noise
            cnts = cv2.findContours(opening, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
            cnts = cnts[0] if len(cnts) == 2 else cnts[1]
            for c in cnts:
                area = cv2.contourArea(c)
                if area < 50:
                    cv2.drawContours(opening, [c], -1, 0, -1)
            
            # Invert
            result = 255 - opening
            cleaned_image = result
            
            crop_ocr = reader.readtext(cleaned_image)

            cleaned_image = cv2.resize(cleaned_image, None, fx = 0.5, fy = 0.5, interpolation = cv2.INTER_CUBIC)
            if type == 'card_number':
                cleaned_image = cv2.resize(cleaned_image, None, fx = 0.2, fy = 0.2, interpolation = cv2.INTER_CUBIC)
            cv2.imwrite(crop_img_path, cleaned_image) 
            
        
            ocr_txt = ''.join([t for _, t, _ in crop_ocr])
            ocr_txt_conf = np.round(np.mean([p for _, _, p in crop_ocr]), 4)

            if type == 'card_number':
                ocr_txt = ocr_txt.replace(' ', '')
       
            col1, col2 = st.columns(2, gap='small')
            with col1:
                st.markdown(f"<h5>{type.replace('_', ' ').upper()}</h5>", unsafe_allow_html=True)
                st.text(f"{ocr_txt.upper()} ({str(ocr_txt_conf)})")
            with col2:
                st.text(' ')
                if type == 'card_number':
                    st.text(' ')
                st.image(crop_img_path)
                


def inference(input_image_path: str):
    finetuned_model = load_od_model()
    results = finetuned_model.predict(input_image_path, 
                        show=False, 
                        save=True,
                        save_crop=True,
                        imgsz=640, 
                        conf=0.6, 
                        save_txt=True,  
                        project= PREDICTION_PATH, 
                        show_labels=True,
                        show_conf=True,
                        line_width=2,
                        exist_ok=True)

    decode_text('card_number')
    decode_text('holder_name')
    decode_text('exp_date')
        
    st.image(os.path.join(PREDICTION_PATH, 'predict', 'input.jpg'))
        

def files_cleanup(path_: str):
    if os.path.exists(path_):
        os.remove(path_)
    if os.path.exists(PREDICTION_PATH):
        shutil.rmtree(PREDICTION_PATH)


# @st.cache_resource
def get_upload_path():
    upload_file_path = os.path.join('.', 'uploads')
    if not os.path.exists(upload_file_path):
        os.makedirs(upload_file_path)
    upload_filename = "input.jpg"
    upload_file_path = os.path.join(upload_file_path, upload_filename)
    return upload_file_path


def process_input_image(img_url):
    upload_file_path = get_upload_path()
    headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.0.0 Safari/537.36'}
    r = get(img_url, headers=headers)
    arr = np.frombuffer(r.content, np.uint8)
    input_image = cv2.imdecode(arr, cv2.IMREAD_UNCHANGED)
    input_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB)
    input_image = cv2.resize(input_image, (640, 640))
    cv2.imwrite(upload_file_path, cv2.cvtColor(input_image, cv2.COLOR_RGB2BGR)) 
    return upload_file_path


try:
    files_cleanup(get_upload_path())
    st.markdown("<h3>Credit Card Detection</h3>", unsafe_allow_html=True)
    desc = '''YOLOv8 is fine-tuned to detect credit card number, holder's name and expiry date. Dataset used to fine-tune YOLOv8 
    can be found <a href="https://universe.roboflow.com/credit-cards-detection/credit_card_detect-wjmlc/dataset/2" target="_blank">
    here</a>. The detected objects are cropped, processed and passed as inputs to EasyOCR for text recognition.
    '''
    st.markdown(desc, unsafe_allow_html=True)
    img_url = st.text_input("Paste the image URL of a credit card:", "")
    placeholder = st.empty()
    if img_url:
        placeholder.empty()
        img_path = process_input_image(img_url)
        inference(img_path)
        files_cleanup(get_upload_path())
        
        
except Exception as e:
    files_cleanup(get_upload_path())
    st.error(f'An unexpected error occured:  \n{e}')