app.py

import timm
from PIL import Image
from torchvision import transforms as T
import gradio as gr
import torch

model = timm.create_model("hf_hub:OmAlve/swin_s3_base_224-Foods-101", pretrained=True)
image_size = (224,224)

test_tf = T.Compose([
    T.Resize(image_size),
    T.ToTensor(),
    T.Normalize(
        mean = (0.5,0.5,0.5),
        std = (0.5,0.5,0.5)
    )
])

labels = [
    "apple_pie",
    "baby_back_ribs",
    "baklava",
    "beef_carpaccio",
    "beef_tartare",
    "beet_salad",
    "beignets",
    "bibimbap",
    "bread_pudding",
    "breakfast_burrito",
    "bruschetta",
    "caesar_salad",
    "cannoli",
    "caprese_salad",
    "carrot_cake",
    "ceviche",
    "cheesecake",
    "cheese_plate",
    "chicken_curry",
    "chicken_quesadilla",
    "chicken_wings",
    "chocolate_cake",
    "chocolate_mousse",
    "churros",
    "clam_chowder",
    "club_sandwich",
    "crab_cakes",
    "creme_brulee",
    "croque_madame",
    "cup_cakes",
    "deviled_eggs",
    "donuts",
    "dumplings",
    "edamame",
    "eggs_benedict",
    "escargots",
    "falafel",
    "filet_mignon",
    "fish_and_chips",
    "foie_gras",
    "french_fries",
    "french_onion_soup",
    "french_toast",
    "fried_calamari",
    "fried_rice",
    "frozen_yogurt",
    "garlic_bread",
    "gnocchi",
    "greek_salad",
    "grilled_cheese_sandwich",
    "grilled_salmon",
    "guacamole",
    "gyoza",
    "hamburger",
    "hot_and_sour_soup",
    "hot_dog",
    "huevos_rancheros",
    "hummus",
    "ice_cream",
    "lasagna",
    "lobster_bisque",
    "lobster_roll_sandwich",
    "macaroni_and_cheese",
    "macarons",
    "miso_soup",
    "mussels",
    "nachos",
    "omelette",
    "onion_rings",
    "oysters",
    "pad_thai",
    "paella",
    "pancakes",
    "panna_cotta",
    "peking_duck",
    "pho",
    "pizza",
    "pork_chop",
    "poutine",
    "prime_rib",
    "pulled_pork_sandwich",
    "ramen",
    "ravioli",
    "red_velvet_cake",
    "risotto",
    "samosa",
    "sashimi",
    "scallops",
    "seaweed_salad",
    "shrimp_and_grits",
    "spaghetti_bolognese",
    "spaghetti_carbonara",
    "spring_rolls",
    "steak",
    "strawberry_shortcake",
    "sushi",
    "tacos",
    "takoyaki",
    "tiramisu",
    "tuna_tartare",
    "waffles"
  ]

def predict(img):
  inp = test_tf(img).unsqueeze(0)
  with torch.no_grad():
    predictions = torch.nn.functional.softmax(model(inp)[0], dim=0)
    toplabels = predictions.argsort(descending=True)[:5]
  results = {labels[label] : float(predictions[label]) for label in toplabels}
  return results

description = """
This is a space for Image Classfication using a Swin Transformer finetuned on the Food101 dataset with Timm and 🤗.
You can find the model [here](https://huggingface.co/OmAlve/swin_s3_base_224-Foods-101)
And the Notebook for finetuning [here](https://github.com/Om-Alve/Finetuning-CV-model/blob/main/Swin-Foods-101.ipynb)

"""
gr.Interface(fn=predict,
             inputs=gr.Image(type="pil"),
             outputs="label",
             examples=['./miso soup.jpg','./cupcake.jpg','./pasta.jpg'],
             title="Food Classification with Swin Transformers",
             description=description,
             live=True).launch()