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| import numpy as np | |
| import gradio as gr | |
| from PIL import Image | |
| from yolo.model import * | |
| model = yolo_model() | |
| def predict(input_img): | |
| # input_img = Image.fromarray(input_img) | |
| _, payload = model.predict(input_img) | |
| # 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 and Detect common objects using the pytorch 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(), | |
| outputs = gr.Image(), | |
| 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) | |