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import gradio as gr
import torch
import os
from model import create_effnet_b2
from timeit import default_timer as timer
from typing import Tuple, Dict
from PIL import Image
import numpy as np
class_names = ["pizza", "steak", "sushi"]
effnet_b2_model , effnet_b2_transform = create_effnet_b2()
effnet_b2_model.load_state_dict(torch.load(f = "./effnet_b2.pt", map_location = torch.device("cpu")))
def predict(img) -> Tuple[Dict, float]:
start_time = timer()
# Convert from NumPy array to PIL image
if isinstance(img, np.ndarray):
img = Image.fromarray(img.astype("uint8"), "RGB")
img = effnet_b2_transform(img).unsqueeze(0)
effnet_b2_model.eval()
with torch.inference_mode():
pred_prob = torch.softmax(effnet_b2_model(img), dim=1)
pred_label_probs = {
class_names[i]: float(pred_prob[0][i]) for i in range(len(class_names))
}
end_time = timer()
pred_time = round(end_time - start_time, 4)
return pred_label_probs, pred_time
import os
# Create separate output components
exmaple_list = [["examples/" + example] for example in os.listdir("examples")]
label_output = gr.Label(label="Classification Probabilities")
number_output = gr.Number(label="Inference Time (seconds)") # Changed label to be more accurate
demo = gr.Interface(
fn=predict,
inputs="image",
outputs=[label_output, number_output],
examples=exmaple_list, # Handle case where image_path might be None
title="Food Vision Mini 🍕",
description="Upload an image to see classification probabilities and inference time.Finetuned on effnet_b2 on(pizza,steak,sushi)",
article="Created By sachin",
allow_flagging="never"
)
demo.launch(debug=False, share=True)
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