Update app.py

app.py

@@ -15,7 +15,7 @@ import pandas as pd
 from mmocr.utils.ocr import MMOCR
 import os
 
-def inference(img, det, recog):
+def inference(img, is_det, det, recog):
     print(datetime.datetime.now(), 'start')
     # ocr = MMOCR(det='PS_CTW', recog='SAR', kie='SDMGR')
     # ocr = MMOCR(det=det, recog=recog, kie='SDMGR')
@@ -25,16 +25,11 @@ def inference(img, det, recog):
         recog = None
     ocr = MMOCR(det=det, recog=recog)
     print(datetime.datetime.now(), 'start read:', img.name)
-    # results = ocr.readtext(img.name, details=True, output='/tmp')
-    # result_file = '/tmp/out_{}.png'.format(os.path.splitext(os.path.basename(img.name))[0])
-    # print(datetime.datetime.now(), results)
-    # # return result_file, pd.DataFrame(results[0]['result']).iloc[: , 2:]
-    # return result_file, results
-    results = ocr.readtext(img.name, details=True, output=None)
-    # result_file = '/tmp/out_{}.png'.format(os.path.splitext(os.path.basename(img.name))[0])
+    results = ocr.readtext(img.name, details=True, output='/tmp')
+    result_file = '/tmp/out_{}.png'.format(os.path.splitext(os.path.basename(img.name))[0])
     print(datetime.datetime.now(), results)
     # return result_file, pd.DataFrame(results[0]['result']).iloc[: , 2:]
-    return img.name, results
+    return result_file, results
 
 description = 'Gradio demo for MMOCR. MMOCR is an open-source toolbox based on PyTorch and mmdetection for text detection, text recognition, and the corresponding downstream tasks including key information extraction. To use it, simply upload your image or click one of the examples to load them. Read more at the links below.'
 article = "<p style='text-align: center'><a href='https://mmocr.readthedocs.io/en/latest/'>MMOCR is an open-source toolbox based on PyTorch and mmdetection for text detection, text recognition, and the corresponding downstream tasks including key information extraction.</a> | <a href='https://github.com/open-mmlab/mmocr'>Github Repo</a></p>"
@@ -86,7 +81,7 @@ recog = gr.inputs.Dropdown(choices=[
 ], type="value", default='SAR', label='recog')
 
 gr.Interface(inference,
-    [gr.inputs.Image(type='file', label='Input'), det, recog ],
+    [gr.inputs.Image(type='file', label='Input'), "checkbox", det, recog ],
     # [gr.outputs.Image(type='pil', label='Output'), gr.outputs.Dataframe(headers=['text', 'text_score', 'label', 'label_score'])],
     [gr.outputs.Image(type='pil', label='Output'), gr.outputs.Textbox(type='str', label='Prediction')],
     title='MMOCR',
@@ -94,5 +89,5 @@ gr.Interface(inference,
     article=article,
     examples=examples,
     css=".output_image, .input_image {height: 40rem !important; width: 100% !important;}",
-    enable_queue=True
-    ).launch(debug=True, server_name='0.0.0.0', server_port=7860)
+    enable_queue=False
+    ).launch(debug=True)