import gradio as gr
import torch
import torchvision
from model import create_model
from timeit import default_timer as timer

model,transform=create_model()
model=model.to("cpu")
model.load_state_dict(torch.load("deneme_modeli.pth").to("cpu"))
class_names = ["pizza","steak","sushi"]
def predict(model,image):
    start=timer()
    image=transform(image.to("cpu")).unsqueeze(0)
    model=model.to("cpu")
    pred=model(image)
    pred = {class_names[i]:torch.softmax(pred)[0][i] for i in range(3)}
    td=timer()-start
    return pred,td

inputs = gr.Image(type="pil", label = "Resim")
outputs = [gr.Label(num_top_classes=3,label="Tahminler"),gr.Number(label="Süre")]
demo=gr.Interface(fn = predict,
                 inputs=inputs,
                 outputs=outputs,
                 examples=["examples/673127.jpg","examples/690177.jpg"],
                 title="Yeni Model")

demo.launch()