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### 1. Imports and class names setup ###
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
with open("class_names.txt", "r") as f: # reading them in from class_names.txt
class_names = [food_name.strip() for food_name in f.readlines()]
### 2. Model and transforms preparation ###
# Create model
effnetb2, effnetb2_transforms = create_effnetb2_model(
num_classes=101, # could also use len(class_names)
)
# Load saved weights
effnetb2.load_state_dict(
torch.load(
f="effnetb2_food101_20percent.pth",
map_location=torch.device("cpu"), # Huggingface offer cpu only
)
)
### 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 = effnetb2_transforms(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 = "🍟TasteNet🍟👁"
description = "A computer vision model based on EfficientNetB2 designed for the classification of food images into 101 specific classes."
# Create examples list from "examples/" directory
example_list = [["examples/" + example] for example in os.listdir("examples")]
# Create Gradio interface
demo = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil"),
outputs=[
gr.Label(num_top_classes=5, label="Predictions"),
gr.Number(label="Prediction time (s)"),
],
examples=example_list,
title=title,
description=description,
)
# Launch the app!
demo.launch()
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