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637391c 629eb64 13cd313 062e259 629eb64 a5de306 629eb64 062e259 13cd313 5ef4ab2 0d87a1c 629eb64 062e259 5ef4ab2 629eb64 062e259 629eb64 5ef4ab2 0d87a1c 629eb64 0d87a1c 637391c 0d87a1c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | import spaces
import gradio as gr
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
import torch
print("Loading TrOCR model...")
device = "cuda" if torch.cuda.is_available() else "cpu"
# use_fast=False completely bypasses the transformers tokenizer conversion bug!
processor = TrOCRProcessor.from_pretrained('microsoft/trocr-base-handwritten', use_fast=False)
model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-base-handwritten').to(device)
print("Model loaded successfully!")
@spaces.GPU
def predict(image):
if image is None:
return ""
pixel_values = processor(image, return_tensors="pt").pixel_values.to(model.device)
generated_ids = model.generate(pixel_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
return generated_text
demo = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil"),
outputs="text",
title="TrOCR Hub Backend"
)
demo.launch() |