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### 1. Imports and class names setup ###
import gradio as gr
import os
import torch
from torchvision import transforms
from timeit import default_timer as timer
from typing import Tuple, Dict
### Model and transforms preparation ###
model = torch.load(f="smile_classifier.pth")
transform = transforms.Compose([
transforms.CenterCrop(size=[178, 178]),
transforms.Resize(size=[64, 64]),
transforms.ToTensor()
])
### Predict function ###
# Create predict function
def predict(img) -> Tuple[Dict, float]:
"""Transforms and performs a prediction on img and returns prediction and time taken.
"""
# Start the timer
start_time = timer()
# Transform the target image and add a batch dimension
img = transform(img).unsqueeze(0)
# Put model into evaluation mode and turn on inference mode
model.eval()
with torch.inference_mode():
# Pass the transformed image through the model and turn the prediction logits into prediction probabilities
pred_probs = model(img)[:, 0]
# Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
if pred_probs >= 0.5:
pred_labels_and_probs = {"Smiling": f"{pred_probs.item()}"}
else:
pred_labels_and_probs = {"Not Smiling": f"{pred_probs.item()}"}
# Calculate the prediction time
pred_time = round(timer() - start_time, 5)
# Return the prediction dictionary and prediction time
return pred_labels_and_probs, pred_time
### Gradio app ###
# Create title, description and article strings
title = "Smile Classifier 🙂😊😃"
description = "A Smile classifier computer vision model (trained on [celebA](https://pytorch.org/vision/main/generated/torchvision.datasets.CelebA.html) data) to classify images of people and identify if they are smiling or not."
article = "Please select an image from provided examples and submit, the model will predict if the person in the image \
is smiling or not and will also provide prediction probabilities."
# Create examples list from "examples/" directory
example_list = [["examples/" + example] for example in os.listdir("examples")]
#Create the Gradio demo
demo = gr.Interface(fn=predict, # mapping function from input to output
inputs=gr.Image(type="pil"), # what are the inputs?
outputs=[gr.Label(num_top_classes=2, label="Predictions"), # what are the outputs?
gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
# Create examples list from "examples/" directory
examples=example_list,
title=title,
description=description,
article=article)
# Launch the demo!
demo.launch()
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