import io
import gradio as gr
import matplotlib.pyplot as plt
import requests, validators
import torch
import pathlib
from PIL import Image
from transformers import AutoFeatureExtractor, DetrForObjectDetection
import os

# colors for visualization
COLORS = [
    [0.000, 0.447, 0.741],
    [0.850, 0.325, 0.098],
    [0.929, 0.694, 0.125],
    [0.494, 0.184, 0.556],
    [0.466, 0.674, 0.188],
    [0.301, 0.745, 0.933]
]

def make_prediction(img, feature_extractor, model):
    inputs = feature_extractor(img, return_tensors="pt")
    outputs = model(**inputs)
    img_size = torch.tensor([tuple(reversed(img.size))])
    processed_outputs = feature_extractor.post_process(outputs, img_size)
    return processed_outputs[0]

def fig2img(fig):
    buf = io.BytesIO()
    fig.savefig(buf)
    buf.seek(0)
    img = Image.open(buf)
    return img


def visualize_prediction(pil_img, output_dict, threshold=0.7, id2label=None):
    keep = output_dict["scores"] > threshold
    boxes = output_dict["boxes"][keep].tolist()
    scores = output_dict["scores"][keep].tolist()
    labels = output_dict["labels"][keep].tolist()
    print(labels)
    if id2label is not None:
        labels = [id2label[x] for x in labels]
    res = dict(zip(labels, scores))
    plt.figure(figsize=(16, 10))
    plt.imshow(pil_img)
    ax = plt.gca()
    colors = COLORS * 100
    for score, (xmin, ymin, xmax, ymax), label, color in zip(scores, boxes, labels, colors):
        ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, fill=False, color=color, linewidth=3))
        ax.text(xmin, ymin, f"{label}: {score:0.2f}", fontsize=15, bbox=dict(facecolor="yellow", alpha=0.5))
    plt.axis("off")
    return fig2img(plt.gcf()),res

def detect_objects(model_name,image_input,threshold):
    print(type(image_input))

    #Extract model and feature extractor
    feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
    
    if 'detr' in model_name:    
        model = DetrForObjectDetection.from_pretrained(model_name)     
        
    if image_input:
      if isinstance(image_input,str):
        if validators.url(image_input):
          image = Image.open(requests.get(image_input, stream=True).raw)
      else:
        image = image_input
    
    #Make prediction
    processed_outputs = make_prediction(image, feature_extractor, model)
    
    #Visualize prediction
    viz_img,labels = visualize_prediction(image, processed_outputs, threshold, model.config.id2label)
    
    return viz_img,labels
        
def set_example_image(example: list) -> dict:
    return gr.Image.update(value=example[0])

def set_example_url(example: list) -> dict:
    return gr.Textbox.update(value=example[0])


models = ["facebook/detr-resnet-50","facebook/detr-resnet-101"]
#examples = ['1daaadc1e83fcecc7bfa920ed2773653.jpeg']
css = '''
h1#title {
  text-align: center;
}
'''
demo = gr.Blocks()

with demo:
    #r.Markdown(title)
    #gr.Markdown(description)
    options = gr.Dropdown(choices=models,label='Select Object Detection Model',show_label=True)
    slider_input = gr.Slider(minimum=0.2,maximum=1,value=0.7,label='Prediction Threshold')
    
    with gr.Tabs():
        with gr.TabItem('Image URL'):
          with gr.Row():
            url_input = gr.Textbox(lines=2,label='Enter valid image URL here..')
            img_output_from_url = gr.Image(shape=(650,650))
            
          with gr.Row():
            example_url = gr.Dataset(components=[url_input],samples=[['https://miro.medium.com/max/960/1*ACc03086R6H_LyLydy8Z4g.jpeg'],['https://www.exposit.com/wp-content/uploads/2021/12/Blog_cover-52-scaled.jpeg']])
          
          url_but = gr.Button('Detect')  
        
        with gr.TabItem('Image Upload'):
          with gr.Row():
            img_input = gr.Image(type='pil')
            img_output_from_upload= gr.Image(shape=(650,650))
                
          with gr.Row():
            example_images = gr.Dataset(components=[img_input],
                                            samples=[["airport.jpg"],['football-match.jpg']])
          img_but = gr.Button('Detect')

        with gr.TabItem('Labels'):
          with gr.Row():
            label = gr.Label(label = 'Labels')
        
    url_but.click(detect_objects,inputs=[options,url_input,slider_input],outputs=[img_output_from_url,label],queue=True)
    img_but.click(detect_objects,inputs=[options,img_input,slider_input],outputs=[img_output_from_upload,label],queue=True)
    example_images.click(fn=set_example_image,inputs=[example_images],outputs=[img_input])
    example_url.click(fn=set_example_url,inputs=[example_url],outputs=[url_input])
    

    #gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=nickmuchi-object-detection-with-detr-and-yolos)")

    
demo.launch(enable_queue=True,show_api=False)