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### 1. Import and class names setup ###
import gradio as gr
import os
import torch
from model import create_effnet_b2_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 preparation ###
effnet_b2, effnet_b2_transforms = create_effnet_b2_model(
num_classes= len(class_names))
# Load save weights
effnet_b2.load_state_dict(
torch.load(f"09_pretrained_effnetb2_feature_extractor_steak_sushi_20_percent.pth",
map_location = torch.device("cpu"))
)
### 3. Predict function ###
def predict(img) -> Tuple[Dict, float]:
# Start a timer
start_time = timer()
# Tranform the input image for use with EffNetB2and add a batch dimension
img = effnet_b2_transforms(img).unsqueeze(0)
# Put model into eval mode, make prediction
effnet_b2.eval()
with torch.inference_mode():
# Pass transformed image through the model and turn the prediction logits into probabilites
pred_probs = torch.softmax(effnet_b2(img), dim =1)
# Create a prediction label, and prediction probability dictionary
pred_labels_and_probs= {class_names[i]: float(pred_probs[0][i]) for i in range (len (class_names))}
# 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 = "FoodVision Mini 🍕🥩🍣"
description = " An [EffNetB2 feature extractor](https://docs.pytorch.org/vision/0.21/models/generated/torchvision.models.efficientnet_b2.html#efficientnet-b2) computer vision model to classify images as pizza, steak or sushi"
article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)"
# Create example list
example_list = [["examples/" + example] for example in os.listdir("examples")]
# Create the Gradio demo
demo = gr.Interface(
fn = predict, # function we want to use
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(debug = False,
share = True)
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