Fu DaiandClaude Opus 4.8
1a585693be
FastAPI service wrapping the Surya-OCR-2 model (datalab-to) served through vLLM: legacy /v1/api/ai/* endpoints, an OpenAI-compatible /v1/chat/completions endpoint, a coalescing request batcher, a local OCR CLI, Docker packaging, multilingual example outputs, and quantization/concurrency benchmarks. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
65 lines
1.7 KiB
Python
65 lines
1.7 KiB
Python
# import base64
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# import requests
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# import os
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# # Configuration
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# API_URL = "http://localhost:5002/v1/api/ai/suya_ocr"
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# # Helper to encode image
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# def encode_image_to_base64(path):
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# with open(path, "rb") as image_file:
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# return base64.b64encode(image_file.read()).decode("utf-8")
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# def test_ocr_endpoint_requests():
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# image_path = "/path/to/surya/ttt.png"
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# assert os.path.exists(image_path), f"Image file not found: {image_path}"
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# encoded_image = encode_image_to_base64(image_path)
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# payload = {
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# "file": encoded_image,
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# "type": "jpg",
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# "skip_text_detection": False,
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# "skip_table_detection": False,
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# "recognize_math": False,
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# "ocr_with_boxes": True
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# }
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# # Send request using requests
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# response = requests.post(API_URL, json=payload)
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# assert response.status_code == 200
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# response_json = response.json()
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# assert response_json["code"] == 200
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# assert "ocr_text_json" in response_json["data"]
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# assert "text_lines" in response_json["data"]
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# print("OCR Response Text:")
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# print(response_json["data"]["text_lines"])
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# import time
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# tik = time.time()
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# test_ocr_endpoint_requests()
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# tok = time.time()
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# print("time consumption: ", tok - tik)
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import requests
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# API 地址
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url = "http://127.0.0.1:5002/image2text"
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# 测试用图像路径
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image_path = "/path/to/surya/ttt.png" # 替换为你自己的图像路径
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# 发起 POST 请求
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with open(image_path, "rb") as image_file:
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files = {"file": ("ttt.png", image_file, "image/png")}
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response = requests.post(url, files=files)
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# 打印响应
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print("Status Code:", response.status_code)
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print("Response JSON:", response.json())
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