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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
with open("class_names.txt","r") as f:
class_names=[food_name.strip() for food_name in f.readlines()]
effnetb2, effnetb2_transforms=create_effnetb2_model(
num_classes=101
)
effnetb2.load_state_dict(
torch.load(
f="foodvision_big.pth",
map_location=torch.device("cpu")
)
)
def predict(img)->Tuple[Dict, float]:
start_time=timer()
img=effnetb2_transforms(img).unsqueeze(0)
effnetb2.eval()
with torch.inference_mode():
pred_probs=torch.softmax(effnetb2(img), dim=1)
pred_labels_and_probs={class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
pred_time=round(timer()-start_time,5)
return pred_labels_and_probs, pred_time
title="FoodVision Big"
description="Images of food as an input and the image class as output using efficient net b2"
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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