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import base64
import io
import os
import gradio as gr
import requests
from PIL import Image
API_URL = "https://t7nd0cf3u89ck4bf.aistudio-hub.baidu.com/ocr"
TOKEN = os.getenv("API_TOKEN", "")
def inference(img):
with io.BytesIO() as buffer:
img.save(buffer, format="png")
img_base64 = base64.b64encode(buffer.getvalue()).decode("ascii")
headers = {
"Authorization": f"token {TOKEN}",
"Content-Type": "application/json",
}
response = requests.post(
API_URL,
json={
"file": img_base64,
"fileType": 1,
"useDocOrientationClassify": False,
"useDocUnwarping": False,
"useTextlineOrientation": False,
},
headers=headers,
timeout=1000,
)
response.raise_for_status()
result = response.json()
ocr_img_url = result["result"]["ocrResults"][0]["ocrImage"]
response = requests.get(ocr_img_url, timeout=10)
response.raise_for_status()
ocr_img_base64 = Image.open(io.BytesIO(response.content))
return ocr_img_base64, result["result"]["ocrResults"][0]["prunedResult"]
title = "PP-OCRv5 Online Demo"
description = """
- PP-OCRv5 is the latest generation of the PP-OCR series model, designed to handle a wide range of scene and text types.
- It supports five major text types: Simplified Chinese, Traditional Chinese, Pinyin annotation, English, and Japanese.
- PP-OCRv5 has enhanced recognition capabilities for challenging use cases, including complex handwritten Chinese and English, vertical text, and rare characters.
- To use it, simply upload your image, or click one of the examples to load them. Read more at the links below.
- [Docs](https://paddlepaddle.github.io/PaddleOCR/), [Github Repository](https://github.com/PaddlePaddle/PaddleOCR).
"""
examples = [
["examples/ancient_demo.png"],
["examples/handwrite_ch_demo.png"],
["examples/handwrite_en_demo.png"],
["examples/japan_demo.png"],
["examples/magazine.png"],
["examples/pinyin_demo.png"],
["examples/research.png"],
["examples/tech.png"],
]
css = """
.output_image, .input_image {height: 40rem !important; width: 100% !important;}
h1 {text-align: center !important;}
"""
gr.Interface(
inference,
gr.Image(type="pil", label="Input Image"),
[gr.Image(type="pil", label="Output Image"), gr.JSON(label="Output JSON", show_label=True)],
title=title,
description=description,
examples=examples,
cache_examples=False,
css=css,
).launch(debug=False)
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