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import gradio as gr
from transformers import pipeline
import tempfile
from PIL import Image
# Lade des trainiertes ViT-Modell für die Food101-Klassifikation
vit_classifier = pipeline("image-classification", model="alimoh02/vit-base-food101")
clip_detector = pipeline(model="openai/clip-vit-large-patch14", task="zero-shot-image-classification")
# Labels aus Food101
labels_food101 = [
"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"
]
# Klassifikationsfunktion
def classify_food(image: Image.Image):
vit_results = vit_classifier(image)
vit_output = {}
for result in vit_results:
try:
label_index = int(result['label'])
label_name = labels_food101[label_index]
except:
label_name = str(result['label']) # fallback
vit_output[label_name] = round(result['score'], 4)
with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
image.save(tmp.name)
clip_results = clip_detector(tmp.name, candidate_labels=labels_food101)
clip_output = {str(result['label']): round(result['score'], 4) for result in clip_results}
return {
"ViT Classification": vit_output,
"CLIP Zero-Shot Classification": clip_output
}
# Beispielbilder
example_images = [
["Cheeseburger.jpg"],
["Sushi.jpg"],
["Brownie.jpg"],
["Tiramisu.jpg"],
["Guacamole.jpg"],
["Samosa.jpg"],
["Oysters.jpg"]
]
# UI mit Gradio
iface = gr.Interface(
fn=classify_food,
inputs=gr.Image(type="pil", label="Upload a food image"),
outputs=gr.JSON(),
title="Food Image Classification Comparison",
description="Vergleiche ein trainiertes ViT-Modell (Food101) mit einem CLIP Zero-Shot-Modell.",
cache_examples=False,
examples=example_images
)
iface.launch()
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