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import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from transformers.image_utils import load_image
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/Sketch-126-DomainNet"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
def sketch_classification(image):
"""Predicts the sketch category for an input image."""
# Convert the input numpy array to a PIL Image and ensure it has 3 channels (RGB)
image = Image.fromarray(image).convert("RGB")
# Process the image and prepare it for the model
inputs = processor(images=image, return_tensors="pt")
# Perform inference without gradient calculation
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
# Convert logits to probabilities using softmax
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
# Mapping from indices to corresponding sketch category labels
labels = {
"0": "aircraft_carrier", "1": "alarm_clock", "2": "ant", "3": "anvil", "4": "asparagus",
"5": "axe", "6": "banana", "7": "basket", "8": "bathtub", "9": "bear",
"10": "bee", "11": "bird", "12": "blackberry", "13": "blueberry", "14": "bottlecap",
"15": "broccoli", "16": "bus", "17": "butterfly", "18": "cactus", "19": "cake",
"20": "calculator", "21": "camel", "22": "camera", "23": "candle", "24": "cannon",
"25": "canoe", "26": "carrot", "27": "castle", "28": "cat", "29": "ceiling_fan",
"30": "cell_phone", "31": "cello", "32": "chair", "33": "chandelier", "34": "coffee_cup",
"35": "compass", "36": "computer", "37": "cow", "38": "crab", "39": "crocodile",
"40": "cruise_ship", "41": "dog", "42": "dolphin", "43": "dragon", "44": "drums",
"45": "duck", "46": "dumbbell", "47": "elephant", "48": "eyeglasses", "49": "feather",
"50": "fence", "51": "fish", "52": "flamingo", "53": "flower", "54": "foot",
"55": "fork", "56": "frog", "57": "giraffe", "58": "goatee", "59": "grapes",
"60": "guitar", "61": "hammer", "62": "helicopter", "63": "helmet", "64": "horse",
"65": "kangaroo", "66": "lantern", "67": "laptop", "68": "leaf", "69": "lion",
"70": "lipstick", "71": "lobster", "72": "microphone", "73": "monkey", "74": "mosquito",
"75": "mouse", "76": "mug", "77": "mushroom", "78": "onion", "79": "panda",
"80": "peanut", "81": "pear", "82": "peas", "83": "pencil", "84": "penguin",
"85": "pig", "86": "pillow", "87": "pineapple", "88": "potato", "89": "power_outlet",
"90": "purse", "91": "rabbit", "92": "raccoon", "93": "rhinoceros", "94": "rifle",
"95": "saxophone", "96": "screwdriver", "97": "sea_turtle", "98": "see_saw", "99": "sheep",
"100": "shoe", "101": "skateboard", "102": "snake", "103": "speedboat", "104": "spider",
"105": "squirrel", "106": "strawberry", "107": "streetlight", "108": "string_bean",
"109": "submarine", "110": "swan", "111": "table", "112": "teapot", "113": "teddy-bear",
"114": "television", "115": "the_Eiffel_Tower", "116": "the_Great_Wall_of_China",
"117": "tiger", "118": "toe", "119": "train", "120": "truck", "121": "umbrella",
"122": "vase", "123": "watermelon", "124": "whale", "125": "zebra"
}
# Create a dictionary mapping each label to its predicted probability (rounded)
predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
return predictions
# Create Gradio interface
iface = gr.Interface(
fn=sketch_classification,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(label="Prediction Scores"),
title="Sketch-126-DomainNet Classification",
description="Upload a sketch to classify it into one of 126 categories."
)
# Launch the app
if __name__ == "__main__":
iface.launch()
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