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Running on Zero
Running on Zero
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7b23073 6557a23 7b23073 6557a23 7b23073 6557a23 7b23073 a186d6f 7b23073 | 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 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | import gradio as gr
import spaces
import torch
from PIL import Image
from transformers import AutoModelForCausalLM, AutoProcessor
from upsampler_theme import UPSAMPLER_THEME, UPSAMPLER_CSS, footer_html, header_html
MODEL_ID = "PaddlePaddle/PaddleOCR-VL"
# Pinned to the last transformers-4.x-compatible revision: the repo's current
# code straddles the v5 API break (v5 create_causal_mask naming with v4 rope
# tables) and imports on no released transformers version.
MODEL_REV = "7faba727a1c11ec07247fd41a52868ed966c7cb3"
PROMPTS = {
"Text (OCR)": "OCR:",
"Table": "Table Recognition:",
"Formula": "Formula Recognition:",
"Chart": "Chart Recognition:",
}
model = (
AutoModelForCausalLM.from_pretrained(
MODEL_ID, revision=MODEL_REV, trust_remote_code=True, torch_dtype=torch.bfloat16
)
.to("cuda")
.eval()
)
processor = AutoProcessor.from_pretrained(MODEL_ID, revision=MODEL_REV, trust_remote_code=True)
def get_duration(image, task, progress=None):
# 0.9B model: a page is well under 30s; scale a little with input size and
# never over-request (anonymous quota pre-checks the requested duration).
if image is None:
return 10
mp = (image.width * image.height) / 1_000_000
return min(60, int(20 + 10 * mp))
@spaces.GPU(duration=get_duration)
def extract_text(image, task, progress=gr.Progress(track_tqdm=True)):
if image is None:
raise gr.Error("Please upload an image first.")
image = image.convert("RGB")
# Bound the longest side; the NaViT encoder handles native ratios, and
# very large scans only slow decoding without helping accuracy.
if max(image.size) > 2048:
scale = 2048 / max(image.size)
image = image.resize(
(int(image.width * scale), int(image.height * scale)), Image.LANCZOS
)
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": image},
{"type": "text", "text": PROMPTS.get(task, "OCR:")},
],
}
]
inputs = processor.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_dict=True,
return_tensors="pt",
).to("cuda")
with torch.inference_mode():
outputs = model.generate(**inputs, max_new_tokens=2048, do_sample=False)
generated = outputs[:, inputs["input_ids"].shape[1] :]
text = processor.batch_decode(generated, skip_special_tokens=True)[0]
return text.strip()
with gr.Blocks(theme=UPSAMPLER_THEME, css=UPSAMPLER_CSS) as demo:
gr.HTML(
header_html(
"PaddleOCR-VL: Image to Text",
"Extract text, tables, formulas, and chart data from any image",
)
)
with gr.Row():
with gr.Column(scale=1):
input_image = gr.Image(type="pil", label="Image")
task = gr.Dropdown(
choices=list(PROMPTS.keys()),
value="Text (OCR)",
label="What to extract",
)
run = gr.Button("Extract Text", variant="primary")
with gr.Column(scale=1):
output = gr.Textbox(
label="Extracted text",
lines=18,
show_copy_button=True,
)
run.click(extract_text, inputs=[input_image, task], outputs=output, api_name="extract_text")
gr.HTML(
footer_html(
"PaddleOCR-VL is a 0.9B vision-language OCR model by PaddlePaddle that "
"converts images to text in 109 languages, including handwriting, and "
"reads tables, mathematical formulas, and charts from photos, scans, and "
"screenshots.",
"https://upsampler.com/free-image-to-text-no-signup",
"free image to text converter",
)
)
if __name__ == "__main__":
demo.launch(ssr_mode=False)
|