app.py

import gradio as gr
from transformers import pipeline, CLIPProcessor, CLIPModel
from PIL import Image
import torch

classifier = pipeline("image-classification", model="Skorm/food11-vit")

# Load CLIP model
clip_model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")

# Define CLIP labels
clip_labels = [
    "Bread", "Dairy product", "Dessert", "Egg", "Fried food",
    "Meat", "Noodles-Pasta", "Rice", "Seafood", "Soup", "Vegetable-Fruit"
]

def classify_food(image_path):
    image = Image.open(image_path)

    # ----- ViT prediction -----
    vit_results = classifier(image_path)
    vit_output = {result["label"]: round(result["score"], 4) for result in vit_results}

    # ----- CLIP zero-shot prediction -----
    inputs = clip_processor(text=clip_labels, images=image, return_tensors="pt", padding=True)
    outputs = clip_model(**inputs)
    probs = outputs.logits_per_image.softmax(dim=1)[0]

    clip_output = {label: round(float(score), 4) for label, score in zip(clip_labels, probs)}

    return vit_output, clip_output

# Example image paths
examples = [
    ["example_images/bread.jpg"],
    ["example_images/dessert.jpg"],
    ["example_images/fruits.jpg"],
    ["example_images/noodles.jpeg"],
    ["example_images/ramen.jpg"],
    ["example_images/seafood.jpg"],
]

# Gradio interface
iface = gr.Interface(
    fn=classify_food,
    inputs=gr.Image(type="filepath"),
    outputs=[
        gr.Label(num_top_classes=3, label="ViT (Fine-tuned) Prediction"),
        gr.Label(num_top_classes=3, label="CLIP Zero-Shot Prediction")
    ],
    title="🍽️ Food Classification with ViT and Zero-Shot CLIP",
    description="Upload a food image. The app compares predictions between your fine-tuned ViT model and zero-shot CLIP.",
    examples=examples
)

iface.launch()