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app.py

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+import base64
+import json
+from io import BytesIO
+import pandas as pd
+from PIL import Image
+import gradio as gr
+import requests
+
+def ocr(image):
+
+    image = Image.open(image)
+    img_buffer = BytesIO()
+    image.save(img_buffer, format=image.format)
+    byte_data = img_buffer.getvalue()
+    base64_bytes = base64.b64encode(byte_data)  # bytes
+    base64_str = base64_bytes.decode()
+    url = "https://www.modelscope.cn/api/v1/studio/damo/ofa_ocr_pipeline/gradio/api/predict/"
+    payload = json.dumps({
+        "data": [f"data:image/jpeg;base64,{base64_str}"],
+        "dataType": ["image"]
+    })
+    headers = {
+        'Content-Type': 'application/json'
+    }
+
+    response = requests.request("POST", url, headers=headers, data=payload)
+    jobj = json.loads(response.text)
+    out_img_base64 = jobj['data'][0].replace('data:image/png;base64,','')
+    out_img = Image.open(BytesIO(base64.urlsafe_b64decode(out_img_base64)))
+    ocr_result = jobj['data'][1]['data']
+
+    result = pd.DataFrame(ocr_result, columns=['Box ID', 'Text'])
+
+    return out_img, result
+
+
+title = "图片识别文字"
+io = gr.Interface(fn=ocr, inputs=gr.inputs.Image(type='filepath', label='Image'),
+                  outputs=[gr.outputs.Image(type='pil', label='Image'),
+                           gr.outputs.Dataframe(headers=['Box ID', 'Text'], type='pandas', label='OCR Results')],
+                  title=title)
+io.launch()

requirements.txt

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+torch
+pillow
+pandas
+requests