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### 1. Imports and class names setup ###
import gradio as gr
import os
import torch
from model import create_effnetb2_model
from timeit import default_timer as timer
from typing import Tuple,Dict
# Setup class names
class_names=["pizza","steak","sushi"]
### 2. Model and transforms perparation ###
effnetb2,effnetb2_transforms=create_effnetb2_model(num_classes=len(class_names))
# Load save weights
effnetb2.load_state_dict(
torch.load(
f="effnetb2_feature_extractor_food101_mini.pth",
map_location=torch.device("cpu") # load the model to the CPU
)
)
### 3. Predict function ###
def predict(img)->Tuple[Dict,float]:
# start a timer
start_time = timer()
# Transform the input image for use with EffNetB2
img=effnetb2_transforms(img).unsqueeze(0) # unsqueeze = add batch dimension on 0th index
# Put model into eval mode, make prediction
effnetb2.eval()
with torch.inference_mode():
# Pass transformed image through the model and turn the prediction logits into probaiblities
pred_probs=torch.softmax(effnetb2(img),dim=1)
# Create a prediction label and prediction probability dictionary
pred_labels_and_probs = {}
# pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
# comment loop below to un-comment line above
for i,class_name in enumerate(class_names):
pred_labels_and_probs[class_name]=pred_probs[0][i]
# 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
### 4. Gradio app ###
# Create title, description and article
title="Food101 Mini Classification"
description = "An [EfficientNetB2 feature extractor](https://pytorch.org/vision/stable/models/generated/torchvision.models.efficientnet_b2.html#torchvision.models.efficientnet_b2) computer vision model to classify images as pizza, steak or sushi."
article = "Created at [Food101 Mini Classification](https://github.com/MRameezU/Food-101-Mini-Classification.git)."
# Create example list
# Create example list
example_list = [["examples/" + example] for example in os.listdir("examples")]
# Create the Gradio demo
demo = gr.Interface(fn=predict, # maps inputs to outputs
inputs=gr.Image(type="pil"),
outputs=[gr.Label(num_top_classes=3, label="Predictions"),
gr.Number(label="Prediction time (s)")],
examples=example_list,
title=title,
description=description,
article=article)
# Launch the demo!
demo.launch()
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