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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
##############################################
# 1. Setup class names
##############################################
class_names = ['art_nouveau',
'baroque',
'expressionism',
'impressionism',
'post_impressionism',
'realism',
'renaissance',
'romanticism',
'surrealism',
'ukiyo_e']
##############################################
# 2. Model and transforms preparation
##############################################
# 2.1 Create EfficientNet_B2 model
EfficientNetB2_model, EfficientNetB2_transforms = create_effnetb2_model(num_classes=10,is_TrivialAugmentWide=False)
# 2.2 Load saved weights (from our trained PyTorch model)
EfficientNetB2_model.load_state_dict(
torch.load(
f="EfficientNet_B2_FT.pth",
map_location=torch.device("cpu"), # load to CPU because we will use the free HuggingFace Space CPUs.
)
)
##############################################
# 3. Create prediction function
##############################################
def prediction(img) -> Tuple[Dict, float]:
"""returns prediction probabilities and prediction time.
"""
# Start the timer
start_time = timer()
# Transform the target image and add a batch dimension
img = EfficientNetB2_transforms(img).unsqueeze(0)
# Put model into evaluation mode and turn on inference mode
EfficientNetB2_model.eval()
with torch.inference_mode():
# Get prediction probabilities
pred_probs = torch.softmax(EfficientNetB2_model(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
##############################################
# 4. Gradio app
##############################################
# 4.1 Create title, description and article strings
title = "Artwork Classification 🎨"
description = "An EfficientNetB2 computer vision model to classify artworks."
article = "Created with PyTorch."
# 4.2 Create examples list from "examples/" directory
example_list = [["examples/" + example] for example in os.listdir("examples")]
# 4.3 Create the Gradio demo
demo = gr.Interface(fn=prediction, # mapping function from input to output
inputs=gr.Image(type="pil"),
outputs=[gr.Label(num_top_classes=3, label="Predictions"), # 1st output: pred_probs
gr.Number(label="Prediction time (s)")], # 2nd output: pred_time
# Create examples list from "examples/" directory
examples=example_list,
title=title,
description=description,
article=article)
# 4.4 Launch the Gradio demo!
demo.launch() |