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### 1. Imports and class names setup ###
import gradio as gr
import os
import torch
import numpy as np
from PIL import Image
from model import create_vit_model
from timeit import default_timer as timer
from typing import Tuple, Dict
# Setup class names
with open("class_names.txt", "r") as f:
class_names = [food_name.strip() for food_name in f.readlines()]
### 2. Model and transforms preparation ###
# Create model
vit, vit_transforms = create_vit_model(num_classes=121)
# Load saved weights
vit.load_state_dict(
torch.load(
f="vit_epoch_2.pth",
map_location=torch.device("cpu"),
)
)
### 3. Predict function ###
from PIL import Image
import numpy as np
def predict(img) -> Tuple[Dict, float]:
"""Transforms and performs a prediction on img and returns prediction and time taken."""
start_time = timer()
if isinstance(img, np.ndarray):
img = img.astype(np.uint8) # ✅ Ensure dtype is uint8
img = Image.fromarray(img, mode="RGB") # ✅ Safe conversion
img = vit_transforms(img).unsqueeze(0)
vit.eval()
with torch.inference_mode():
pred_probs = torch.softmax(vit(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
### 4. Gradio app ###
title = "VisionBite 🍔👁"
description = "A ViT feature extractor computer vision model to classify images of food into 121 categories."
article = "The model has been trained on the Food121 dataset using ViT Base 16."
# ✅ Sort examples for consistent UI (optional)
example_list = [["examples/" + example] for example in sorted(os.listdir("examples")) if example.endswith((".jpg", ".png", ".jpeg"))]
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,
)
demo.launch()
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