import gradio as gr
import shutil

import urllib.request
import sys
import os
import urllib.request
import zipfile

sys.path.append(".")

from model import prediction

gr.close_all()

https://storage.googleapis.com/models-gradio/products/products.zip

urllib.request.urlretrieve("https://storage.googleapis.com/models-gradio/products/products.zip")


with zipfile.ZipFile("products.zip", 'r') as zip_ref:
    zip_ref.extractall()

def predict(img):
    name_image = img.split("/")[-1]
    
    prediction_img, text = prediction(img)
    
    return  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="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)