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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
# set up class names
with open('class_names.txt', "r") as f:
class_names = [food.strip() for food in f.readlines()]
### 2. model and transforms preparation
# Create model
effnetb2, effnetb2_transforms = create_effnetb2_model(
num_classes= len(class_names)
)
# Load saved weights
effnetb2.load_state_dict(
torch.load(
f="pretrained_effnetb2_feature_extractor_food101.pth",
map_location= torch.device("cpu")
)
)
### 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 = "FoodVision 😋"
description = "An EfficientNetB2 feature extractor Computer Vision model to classify images of Food into [101 different classes](https://huggingface.co/spaces/pritesh29/FoodVision/blob/main/class_names.txt)."
article = "Created by [pritesh tadvi](https://pritesh2000.github.io/)\nTraining code at [kaggle/foodvision](https://www.kaggle.com/code/priteshtadvi/foodvision-a-food-classification-model)"
# 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,
article = article
)
# launch the app
demo.launch()
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