import gradio as gr
from PIL import Image,ImageDraw, ImageFont
import sys
import os
model = os.environ.get('MODEL')
import torch
torch.hub.download_url_to_file(model, 'model.pt')

from util import Detection, classes
sys.path.append("./")
sys.path.append("./yolov5")
from yolov5.detect import predict, load_yolo_model

# Model

model, stride, names, pt, jit, onnx, engine = load_yolo_model("model.pt")

def run_yolo(img):

    img0 = Image.open(img.name).convert("RGB")
    draw = ImageDraw.Draw(img0)

    predictions = predict(model, stride, names, pt, jit, onnx, engine, imgsz=[1280, 1280], conf_thres=0.5, iou_thres=0.3, save_conf=True,
                exist_ok=True, save_txt=False, source=img.name, project=None, name=None)
    
    detections : list[Detection] = []
    for k, (bboxes, img)  in enumerate(predictions):
        
        #print(bboxes)
        # exp.imgs.append(img_info)
        for i, bbox in enumerate(bboxes):
            det = Detection(
                (k+1)*(i+1),
                bbox["xmin"],
                bbox["ymin"],
                bbox["xmax"],
                bbox["ymax"],
                bbox["conf"],
                bbox["class"],
                classes[int(bbox["class"])],
                img0.size
            )
            same = list(filter(lambda x: x.xmin == det.xmin and x.ymin == det.ymin or ( det.xmin > x.xmin and det.ymin > x.ymin and det.xmax < x.xmax and det.ymax < x.ymax ) or ( det.xmin < x.xmin and det.ymin < x.ymin and det.xmax > x.xmax and det.ymax > x.ymax ) or Detection.get_iou(det, x) > 0.6, detections))

            if len(same) == 0:
                detections.append(det)
                draw.rectangle(((det.xmin, det.ymin), (det.xmax, det.ymax)), fill=None, outline=(255,255,255))
                draw.rectangle(((det.xmin, det.ymin - 10), (det.xmax, det.ymin)), fill=(255,255,255))
                draw.text((det.xmin, det.ymin - 10), det.class_name, fill=(0,0,0), font=ImageFont.truetype("Roboto-Regular.ttf"))
            
    return img0 


inputs = gr.inputs.Image(type='file', label="Original Image")
outputs = gr.outputs.Image(type="pil", label="Output Image")

title = "Letter Detection"
description = "Object Detection-based OCR. Upload an image or click an example image to use."
article = "<p style='text-align: center'>This is a character-level OCR trained on: <ul><li>Screenshots</li><li>Random photos taken from smartphone</li><li>Synthetic images</li><li>Receipts</li></p>"

examples = [['images/cover1.jpg'], ['images/scontrino1.jpg'], ['images/gradientgpu.jpg'], ['images/sugar.jpg'], ['images/google1.jpg'], ]

gr.Interface(run_yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, theme="huggingface").launch(enable_queue=True)