# app.py

import gradio as gr
import spaces
import torch

from PIL import Image
from transformers import pipeline
import matplotlib.pyplot as plt
import io

model_pipeline = pipeline("object-detection", model="sergiopaniego/detr-resnet-50-dc5-fashionpedia-finetuned")


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 get_output_figure(pil_img, results, threshold):
    plt.figure(figsize=(16, 10))
    plt.imshow(pil_img)
    ax = plt.gca()
    colors = COLORS * 100

    for result in results:
        score = result["score"]
        label = result["label"]
        box = list(result["box"].values())
        if score > threshold:
            c = COLORS[hash(label) % len(COLORS)]
            ax.add_patch(
                plt.Rectangle((box[0], box[1]), box[2] - box[0], box[3] - box[1], fill=False, color=c, linewidth=3)
            )
            text = f"{label}: {score:0.2f}"
            ax.text(box[0], box[1], text, fontsize=15, bbox=dict(facecolor="yellow", alpha=0.5))
    plt.axis("off")

    return plt.gcf()


@spaces.GPU
def detect(image):
    results = model_pipeline(image)
    print(results)

    output_figure = get_output_figure(image, results, threshold=0.7)

    buf = io.BytesIO()
    output_figure.savefig(buf, bbox_inches="tight")
    buf.seek(0)
    output_pil_img = Image.open(buf)

    return output_pil_img


with gr.Blocks() as demo:
    gr.Markdown("# Object detection with DETR fine tuned on detection-datasets/fashionpedia")
    gr.Markdown(
        """
        This application uses a fine tuned DETR (DEtection TRansformers) to detect objects on images.
        This version was trained using detection-datasets/fashionpedia dataset.
        You can load an image and see the predictions for the objects detected.
        """
    )

    gr.Interface(
        fn=detect,
        inputs=gr.Image(label="Input image", type="pil"),
        outputs=[gr.Image(label="Output prediction", type="pil")],
    )

demo.launch(show_error=True)