update

app.py

@@ -34,7 +34,7 @@ textrec_model_list = ['ABINet', 'ASTER', 'CRNN', 'MASTER', 'NRTR', 'RobustScanne
 textkie_model_list = ['SDMGR','SDMGR']
 
 
-def ocr_inference(inputs, out_dir, det, det_weights, rec, rec_weights, kie, kie_weights, device, batch_size):
+def ocr_inference(inputs, out_dir, det, det_weights, rec, rec_weights, device, batch_size):
     init_args, call_args = parse_args()
     inputs = np.array(inputs)
     img_path = "demo_text_ocr.jpg"
@@ -49,13 +49,7 @@ def ocr_inference(inputs, out_dir, det, det_weights, rec, rec_weights, kie, kie_
         init_args['det_weights'] = det_weights
         init_args['rec'] = None
         init_args['rec_weights'] = rec_weights
-    if kie is not None:
-        init_args['kie'] = kie
-        init_args['kie_weights'] = None
-    if kie_weights is not None:
-        init_args['kie'] = None
-        init_args['kie_weights'] = kie_weights
-
+    
     call_args['inputs'] = img_path
     call_args['out_dir'] = out_dir
     call_args['batch_size'] = int(batch_size)
@@ -189,17 +183,17 @@ if __name__ == '__main__':
     kie = gr.inputs.Textbox(default='SDMGR')
     # kie = gr.inputs.Dropdown(label="Key Information Extraction Model", choices=[m for m in textkie_model_list],
     #                          default='SDMGR')
-    kie_weights = gr.inputs.Textbox(default=None)
+    # kie_weights = gr.inputs.Textbox(default=None)
     device = gr.inputs.Radio(choices=["cpu", "cuda"], label="Device used for inference", default="cpu")
     batch_size = gr.inputs.Number(default=1, label="Inference batch size")
     output_image = gr.outputs.Image(type="pil", label="Output Image")
     output_json = gr.outputs.Textbox()
     download_test_image()
-    examples = [["demo_text_ocr.jpg", "results", "DBNet", None, "CRNN", None, "SDMGR", None, "cpu", 1],
-                ["demo_text_det.jpg", "results", "FCENet", None, "ASTER", None, "SDMGR", None, "cpu", 1],
-                ["demo_text_recog.jpg", "results", "PANet", None, "MASTER", None, "SDMGR", None, "cpu", 1],
-                ["demo_densetext_det.jpg", "results", "PSENet", None, "CRNN", None, "SDMGR", None, "cpu", 1],
-                ["demo_kie.jpg", "results", "TextSnake", None, "RobustScanner", None, "SDMGR", None, "cpu", 1]
+    examples = [["demo_text_ocr.jpg", "results", "DBNet", None, "CRNN", None, "cpu", 1],
+                ["demo_text_det.jpg", "results", "FCENet", None, "ASTER", None, "cpu", 1],
+                ["demo_text_recog.jpg", "results", "PANet", None, "MASTER", None, "cpu", 1],
+                ["demo_densetext_det.jpg", "results", "PSENet", None, "CRNN", None, "cpu", 1],
+                ["demo_kie.jpg", "results", "TextSnake", None, "RobustScanner", None, "cpu", 1]
                 ]
 
     title = "MMOCR web demo"
@@ -213,8 +207,7 @@ if __name__ == '__main__':
     iface = gr.Interface(
         fn=ocr_inference,
         inputs=[
-            input_image, out_dir, det, det_weights, rec, rec_weights,
-            kie, kie_weights, device, batch_size
+            input_image, out_dir, det, det_weights, rec, rec_weights, device, batch_size
         ],
         outputs=[output_image, output_json], examples=examples,
         title=title, description=description, article=article,