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### 1. Imports and class names setup ###
import gradio as gr
import os
import torch
from model import create_effnetb0_model
from timeit import default_timer as timer
from typing import Tuple, Dict
from torchvision import transforms
# Setup class names
class_names = ["Happy", "Disgusted", "Suprised","Angry","Neutral","Sad","Fearful"]
# Create EffNetB2 model
effnetb0, effnetb0_transforms = create_effnetb0_model(
num_classes=7, # len(class_names) would also work
)
# Load saved weights
# Load saved weights
effnetb0.load_state_dict(
torch.load(
f="models/efficientnet_b0.pth",
map_location=torch.device("cpu"), # load to CPU
)
)
### 3. 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 = effnetb0_transforms(img).unsqueeze(0)
# Put model into evaluation mode and turn on inference mode
effnetb0.eval()
with torch.inference_mode():
# Pass the transformed image through the model and turn the prediction logits into prediction probabilities
pred_probs = torch.softmax(effnetb0(img), dim=1)
# Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
# 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
example_list = [["examples/" + example] for example in os.listdir("examples")]
### 4. Gradio app ###
# Create title, description and article strings
title = "Emotion Detection App 😀😐😰😞🤢😲😡"
description = "An EfficientNetB0 computer vision model to classify images of emotions: Happy, Neutral, Sad, fearful, Angry, Suprised, Disgusted."
article = "Reference: [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."
import gradio as gr
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=7, 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()
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