import gradio as gr
from PIL import Image,ImageDraw, ImageFont
import sys
import os
import torch
from util import Detection

face_model = os.environ.get('FACE_MODEL')
age_model = os.environ.get('AGE_MODEL')

torch.hub.download_url_to_file(face_model, 'face_model.pt')
torch.hub.download_url_to_file(age_model, 'age_model.pt')

sys.path.append("./")
sys.path.append("./yolov5")

age_model_ts = torch.jit.load("age_model.pt")

from yolov5.detect import predict, load_yolo_model

# Model

model, stride, names, pt, jit, onnx, engine = load_yolo_model("face_model.pt", imgsz=[320,320])

def run_yolo(img):

    #img0 = Image.open(img.name).convert("RGB")
    img_path = img.name # ["name"]
    img0 = Image.open(img_path).convert("RGB")
    draw = ImageDraw.Draw(img0)

    predictions = predict(age_model_ts, model, stride, names, pt, jit, onnx, engine, imgsz=[320, 320], conf_thres=0.5, iou_thres=0.45, save_conf=True,
                    exist_ok=True, nosave=True, save_txt=False, source=img_path, 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"],
                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="Input Image")
outputs = gr.outputs.Image(type="pil", label="Output Image")

title = "AgeGuesser"
description = "Guess the age of a person!"
article = """A fully automated system based on YOLOv5 and EfficientNet to perform face detection and age estimation in real-time. 
    Links:
    <ul>
        <li>
            <a href='https://link.springer.com/chapter/10.1007/978-3-030-89131-2_25'>Paper</a>
        </li>
        <li>
            <a href='https://www.researchgate.net/publication/355777953_Real-Time_Age_Estimation_from_Facial_Images_Using_YOLO_and_EfficientNet'>Paper</a> 
        </li>
        <li>
            <a href='https://github.com/ai-hazard/AgeGuesser-train'>Github</a> 
        </li>
    </ul>
"""

examples = [['images/1.jpg'], ['images/2.jpg'], ['images/3.jpg'], ['images/4.jpg'], ['images/5.jpg'], ]

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