import glob
import gradio as gr
from inference import *
from PIL import Image


def gradio_app(image_path):
    """A function that send the file to the inference pipeline, and filters
    some predictions before outputting to gradio interface."""

    predictions = run_inference(image_path)

    out_img = Image.fromarray(predictions.render()[0])

    return out_img


title = "Seamore"
description = "----eyes in the sea----" \
              "seamore is trained on 691 classes using 33,667 localized images from " \
              "MBARI’s Video Annotation and Reference System (VARS). " \
              "We used the PyTorch " \
              "framework and the yolov5 ‘YOLOv5x’ pretrained checkpoint to " \
              "train for 28 epochs with a batch size of 18 and image size of " \
              "."

examples = glob.glob("images/*.png")

gr.Interface(gradio_app,
             inputs=[gr.inputs.Image(type="filepath")],
             outputs=gr.outputs.Image(type="pil"),
             enable_queue=True,
             title=title,
             description=description,
             examples=examples).launch()