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### 1. Imports and class names setup ###
import gradio as gr
import os
import torch
import torchvision.transforms as T
from model import create_effnet_b2
from timeit import default_timer as timer
from typing import Tuple, Dict
# Setup class names
class_names = ['pizza', 'steak', 'sushi']
### 2. Model and transforms preparation ###
test_tsfm = T.Compose([T.Resize((224,224)),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], # 3. A mean of [0.485, 0.456, 0.406] (across each colour channel)
std=[0.229, 0.224, 0.225]) # 4. A standard deviation of [0.229, 0.224, 0.225] (across each colour channel),
])
# Create EffNetB2 Model
effnetb2, test_transform = create_effnet_b2(num_of_class=len(class_names),
transform=test_tsfm,
seed=42)
# saved_path = 'demos\foodvision_mini\09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth'
saved_path = '07_effnetb2_data_50_percent_10_epochs.pth'
print('Loading Model State Dictionary')
# Load saved weights
effnetb2.load_state_dict(
torch.load(f=saved_path,
map_location=torch.device('cpu'), # load to CPU
)
)
print('Model Loaded ...')
### 3. Predict function ###
# Create predict function
from typing import Tuple, Dict
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 = test_tsfm(img).unsqueeze(0)
# Put model into evaluation mode and turn on inference mode
effnetb2.eval()
with torch.inference_mode():
# Pass the transformed image through the model and turn the prediction logits into prediction probabilities
pred_probs = torch.softmax(effnetb2(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 ###
# Create title, description and article strings
title= 'FoodVision Mini 🍕🥩🍣'
description = "An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi."
article = "Created at Chukwuka using Mr. DBourke Tutorial [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."
# 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.inputs.Image(type='pil'), # What are the inputs?
outputs=[gr.Label(num_top_classes=3, label="Predictions"), # what are the outputs?
gr.Number(label='Prediction time (s)')], # Our fn has two outputs, therefore we have two outputs
examples=example_list,
title=title,
description=description,
article=article
)
# Launch the demo
print('Gradio Demo Launched')
demo.launch()
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