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import numpy as np
import gradio as gr
from model import api
from PIL import Image
from yolo.model import *
model = yolo_model()
def predict(input_img):
input_img = Image.fromarray(input_img)
_, payload = model.predict(image)
# print('prediction',prediction)
return payload
css = ''
# with gr.Blocks(css=css) as demo:
# gr.HTML("<h1><center>Signsapp: Classify the signs based on the hands sign images<center><h1>")
# gr.Interface(sign,inputs=gr.Image(shape=(200, 200)), outputs=gr.Label())
title = r"yolov3"
description = r"""
<center>
Recognize common objects using the model
<img src="file/det_dog-cycle-car.png" width=350px>
</center>
"""
article = r"""
### Credits
- [Coursera](https://www.coursera.org/learn/convolutional-neural-networks/)
"""
demo = gr.Interface(
title = title,
description = description,
article = article,
fn=predict,
inputs = gr.Image(shape=(200, 200)),
outputs = gr.Image(shape=(200, 200)),
examples=["dog-cycle-car.png"]
# allow_flagging = "manual",
# flagging_options = ['recule', 'tournedroite', 'arretetoi', 'tournegauche', 'gauche', 'avance', 'droite'],
# flagging_dir = "./flag/men"
)
# demo.queue()
demo.launch(debug=True)
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