dog_breed.py

import gradio as gr
import spaces
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/Dog-Breed-120"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

@spaces.GPU
def dog_breed_classification(image):
    """Predicts the dog breed for an image."""
    image = Image.fromarray(image).convert("RGB")
    inputs = processor(images=image, return_tensors="pt")
    
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
    
    labels = {
        "0": "affenpinscher",
        "1": "afghan_hound",
        "2": "african_hunting_dog",
        "3": "airedale",
        "4": "american_staffordshire_terrier",
        "5": "appenzeller",
        "6": "australian_terrier",
        "7": "basenji",
        "8": "basset",
        "9": "beagle",
        "10": "bedlington_terrier",
        "11": "bernese_mountain_dog",
        "12": "black-and-tan_coonhound",
        "13": "blenheim_spaniel",
        "14": "bloodhound",
        "15": "bluetick",
        "16": "border_collie",
        "17": "border_terrier",
        "18": "borzoi",
        "19": "boston_bull",
        "20": "bouvier_des_flandres",
        "21": "boxer",
        "22": "brabancon_griffon",
        "23": "briard",
        "24": "brittany_spaniel",
        "25": "bull_mastiff",
        "26": "cairn",
        "27": "cardigan",
        "28": "chesapeake_bay_retriever",
        "29": "chihuahua",
        "30": "chow",
        "31": "clumber",
        "32": "cocker_spaniel",
        "33": "collie",
        "34": "curly-coated_retriever",
        "35": "dandie_dinmont",
        "36": "dhole",
        "37": "dingo",
        "38": "doberman",
        "39": "english_foxhound",
        "40": "english_setter",
        "41": "english_springer",
        "42": "entlebucher",
        "43": "eskimo_dog",
        "44": "flat-coated_retriever",
        "45": "french_bulldog",
        "46": "german_shepherd",
        "47": "german_short-haired_pointer",
        "48": "giant_schnauzer",
        "49": "golden_retriever",
        "50": "gordon_setter",
        "51": "great_dane",
        "52": "great_pyrenees",
        "53": "greater_swiss_mountain_dog",
        "54": "groenendael",
        "55": "ibizan_hound",
        "56": "irish_setter",
        "57": "irish_terrier",
        "58": "irish_water_spaniel",
        "59": "irish_wolfhound",
        "60": "italian_greyhound",
        "61": "japanese_spaniel",
        "62": "keeshond",
        "63": "kelpie",
        "64": "kerry_blue_terrier",
        "65": "komondor",
        "66": "kuvasz",
        "67": "labrador_retriever",
        "68": "lakeland_terrier",
        "69": "leonberg",
        "70": "lhasa",
        "71": "malamute",
        "72": "malinois",
        "73": "maltese_dog",
        "74": "mexican_hairless",
        "75": "miniature_pinscher",
        "76": "miniature_poodle",
        "77": "miniature_schnauzer",
        "78": "newfoundland",
        "79": "norfolk_terrier",
        "80": "norwegian_elkhound",
        "81": "norwich_terrier",
        "82": "old_english_sheepdog",
        "83": "otterhound",
        "84": "papillon",
        "85": "pekinese",
        "86": "pembroke",
        "87": "pomeranian",
        "88": "pug",
        "89": "redbone",
        "90": "rhodesian_ridgeback",
        "91": "rottweiler",
        "92": "saint_bernard",
        "93": "saluki",
        "94": "samoyed",
        "95": "schipperke",
        "96": "scotch_terrier",
        "97": "scottish_deerhound",
        "98": "sealyham_terrier",
        "99": "shetland_sheepdog",
        "100": "shih-tzu",
        "101": "siberian_husky",
        "102": "silky_terrier",
        "103": "soft-coated_wheaten_terrier",
        "104": "staffordshire_bullterrier",
        "105": "standard_poodle",
        "106": "standard_schnauzer",
        "107": "sussex_spaniel",
        "108": "test",
        "109": "tibetan_mastiff",
        "110": "tibetan_terrier",
        "111": "toy_poodle",
        "112": "toy_terrier",
        "113": "vizsla",
        "114": "walker_hound",
        "115": "weimaraner",
        "116": "welsh_springer_spaniel",
        "117": "west_highland_white_terrier",
        "118": "whippet",
        "119": "wire-haired_fox_terrier",
        "120": "yorkshire_terrier"
    }
    
    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
    return predictions

# Create Gradio interface
iface = gr.Interface(
    fn=dog_breed_classification,
    inputs=gr.Image(type="numpy"),
    outputs=gr.Label(label="Prediction Scores"),
    title="Dog Breed Classification",
    description="Upload an image to classify it into one of the 121 dog breed categories."
)

# Launch the app
if __name__ == "__main__":
    iface.launch()