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import gradio as gr
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)
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() |