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d6f4b07 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | # Step 1
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
## Setup class names
class_names = ["pizza", "steak", "sushi"]
# Step 2
effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=len(class_names))
effnetb2.load_state_dict(torch.load(f="09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth",
map_location=torch.device("cpu"), weights_only = True))
# Step 3
def predict(img) -> Tuple[Dict, float]:
"""Transforms and performs a prediction on img and returns prediction and time taken.
"""
# Timer start
start_time = timer()
# Transform the image and add a batch dimension
img = effnetb2_transforms(img).unsqueeze(0)
# Get model into eval() mode and turn on inference mode
effnetb2.eval()
with torch.inference_mode():
# Pass transformed image through the model and turn pred logits to pred probs
pred_logits = effnetb2(img)
pred_probs = torch.softmax(pred_logits, dim = 1)
# Create pred label and pred prob dict for each pred class (this is the reqd 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 pred time
pred_time = round(timer() - start_time, 5)
# return pred dict and pred time
return pred_labels_and_probs, pred_time
# Step 4
## 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 [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, 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()
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