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import torch
import torchvision
from torch import nn
from timeit import default_timer as timer
from typing import Tuple,Dict
import os
from model import create_effnetb2
# gettingthe classnames
class_names = ["pizza","steak","sushi"]
#lets verify if we can get list of example
foodvision_mini_examples_path = "examples/"
example_list = ["examples/" + example for example in os.listdir(foodvision_mini_examples_path)]
# getting model and its trasofrm
effnetb2_2, effnetb2_transforms_2 = create_effnetb2() # we dont need "model.craete_effnetb2" as its directly imported by us
# load the saved weigths
effnetb2_2.load_state_dict(
torch.load(
f = "09_pretrained_effnetb2_feature_extractor_20_percent.pth",
map_location= torch.device("cpu")
)
)
# craeting the predict function
def predict(img) -> Tuple[Dict,float]:
# start a timer
start_time = timer()
# transfomr the input image for use with EffNetB2
transformed_img = effnetb2_transforms_2(img).unsqueeze(0).to("cpu")
# put the mdoel to eva; mode and make predictions
effnetb2_2.eval()
with torch.inference_mode():
logits = effnetb2_2(transformed_img)
pred_probs = torch.softmax(logits, dim = 1)
# print(pred_probs)
pred_class = class_names[torch.argmax(pred_probs, dim = 1)]
pred_labels_and_probs = {class_names[i] : float(pred_probs[0][i]) for i in range(len(class_names))}
# calculate pred time
end_time = timer()
pred_time = round(end_time - start_time, 4)
# return pred dict AND PRED TIME
return pred_labels_and_probs, pred_time
# interface zone
import gradio as gr
# craete title , description and article
title = "Food Vision Mini 🍕🥩🍣"
description = "An [EfficientNetB2 Feature Extractor Computer Vision Model](https://pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_b2.html) to classify images as pizza, steak and sushi"
article = "Created at [09 PyTorch model deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)"
# craeting gradio demo
demo = gr.Interface(fn = predict,inputs = gr.Image(type = "pil"),
outputs = [gr.Label(num_top_classes=3, label = "predictions") , gr.Number(label = "prediciton time(s)")],
examples = example_list,
title = title , description = description, article = article)
demo.launch() # to avoid showing of error, we dont need share = true as it cant be handled by higging face spaces
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