Delete dog_breed.py

dog_breed.py

@@ -1,161 +0,0 @@
-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()