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import gradio as gr
import shutil
import urllib.request
import sys
import os
import urllib.request
import zipfile
sys.path.append(".")
gr.close_all()
urllib.request.urlretrieve("https://storage.googleapis.com/models-gradio/products/products.zip", "products.zip")
with zipfile.ZipFile("products.zip", 'r') as zip_ref:
zip_ref.extractall()
from model import prediction
def predict(img):
prediction_img, text, t_process = prediction(img)
return t_process, str(text), prediction_img,
sample_images = ["dataset/" + name for name in os.listdir("dataset")]
gr.Interface(fn=predict,
inputs=[gr.Image(label="image à tester" ,type="filepath")],
outputs=[gr.Textbox(label="temps"), gr.Textbox(label="analyse"), gr.Image(label ="résultat") ],
css="footer {visibility: hidden} body}, .gradio-container {background-color: white}",
examples=sample_images).launch(server_name="0.0.0.0", share=False)
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