Update app.py

app.py

@@ -1,8 +1,4 @@
 # -*- encoding: utf-8 -*-
-import os
-
-os.system('pip install -r requirements.txt')
-
 import math
 import random
 import time
@@ -93,6 +89,9 @@ def inference(img_path, box_thresh=0.5, unclip_ratio=1.6, text_score=0.5):
     ocr_result, _ = rapid_ocr(img, box_thresh=box_thresh,
                               unclip_ratio=unclip_ratio,
                               text_score=text_score)
+    if not ocr_result:
+        return img_path, '未识别到有效文本'
+
     dt_boxes, rec_res, scores = list(zip(*ocr_result))
     img_save_path = visualize(img_path, dt_boxes, rec_res, scores)
     output_text = [f'{one_rec} {float(score):.4f}'
@@ -100,44 +99,42 @@ def inference(img_path, box_thresh=0.5, unclip_ratio=1.6, text_score=0.5):
     return img_save_path, output_text
 
 
-title = 'RapidOCR Demo (捷智OCR)'
-description = """
-- Docs: [Docs](https://rapidocr.rtfd.io/)
-- Parameters docs: [link](https://github.com/RapidAI/RapidOCR/tree/main/python#configyaml%E4%B8%AD%E5%B8%B8%E7%94%A8%E5%8F%82%E6%95%B0%E4%BB%8B%E7%BB%8D)
-- **box_thresh**: 检测到的框是文本的概率，值越大，框中是文本的概率就越大。存在漏检时，调低该值。取值范围：[0, 1.0]
-- **unclip_ratio**: 控制文本检测框的大小，值越大，检测框整体越大。在出现框截断文字的情况，调大该值。取值范围：[1.5, 2.0]
-- **text_score**: 文本识别结果是正确的置信度，值越大，显示出的识别结果更准确。存在漏检时，调低该值。取值范围：[0, 1.0]
-"""
-
-article = """<p style='text-align: center'> Completely open source, free and support offline deployment of multi-platform and multi-language OCR SDK <a href='https://github.com/RapidAI/RapidOCR'>Github Repo</a></p>
-"""
-css = ".output_image, .input_image {height: 40rem !important; width: 100% !important;}"
-examples = [['images/1.jpg']]
-
 rapid_ocr = RapidOCR()
+examples = [['images/1.jpg']]
 
-gr.Interface(
-    inference,
-    inputs=[
-        gr.inputs.Image(type='filepath', label='Input'),
-        gr.Slider(minimum=0, maximum=1.0, value=0.5,
-                  label='box_thresh', step=0.1,
-                  info='检测到的框是文本的概率，值越大，框中是文本的概率就越大。存在漏检时，调低该值。取值范围：[0, 1.0]'),
-        gr.Slider(minimum=1.5, maximum=2.0, value=1.6,
-                  label='unclip_ratio', step=0.1,
-                  info='控制文本检测框的大小，值越大，检测框整体越大。在出现框截断文字的情况，调大该值。取值范围：[1.5, 2.0]'),
-        gr.Slider(minimum=0, maximum=1.0, value=0.5,
-                  label='text_score', step=0.1,
-                  info='文本识别结果是正确的置信度，值越大，显示出的识别结果更准确。存在漏检时，调低该值。取值范围：[0, 1.0]'),
-    ],
-    outputs=[
-        gr.outputs.Image(type='filepath', label='Output_image'),
-        gr.outputs.Textbox(type='text', label='Output_text')
-    ],
-    title=title,
-    description=description,
-    examples=examples,
-    article=article,
-    css=css,
-    allow_flagging='never',
-    ).launch(debug=True, enable_queue=True)
+with gr.Blocks(title='RapidOCR') as demo:
+    gr.Markdown("""
+        <center><a href="https://github.com/RapidAI/RapidOCR" target="_blank"><img src="https://raw.githubusercontent.com/RapidAI/RapidOCR/main/assets/RapidOCR_LOGO.png"  width="30%"></a></center>
+
+        ## Docs: [Docs](https://rapidocr.rtfd.io/)
+        ## Parameters docs: [link](https://github.com/RapidAI/RapidOCR/tree/main/python#configyaml%E4%B8%AD%E5%B8%B8%E7%94%A8%E5%8F%82%E6%95%B0%E4%BB%8B%E7%BB%8D)
+        #### `box_thresh`: 检测到的框是文本的概率，值越大，框中是文本的概率就越大。存在漏检时，调低该值。
+        #### `unclip_ratio`: 控制文本检测框的大小，值越大，检测框整体越大。在出现框截断文字的情况，调大该值。
+        #### `text_score`: 文本识别结果是正确的置信度，值越大，显示出的识别结果更准确。存在漏检时，调低该值。""")
+    with gr.Box():
+        with gr.Row():
+            box_thresh = gr.Slider(minimum=0, maximum=1.0, value=0.5,
+                                   label='box_thresh', step=0.1,
+                                   interactive=True,
+                                   info='[0, 1.0]')
+            unclip_ratio = gr.Slider(minimum=1.5, maximum=2.0, value=1.6,
+                                     label='unclip_ratio', step=0.1,
+                                     interactive=True,
+                                     info='[1.5, 2.0]')
+            text_score = gr.Slider(minimum=0, maximum=1.0, value=0.5,
+                                   label='text_score', step=0.1,
+                                   interactive=True,
+                                   info='[0, 1.0]')
+
+    input_img = gr.Image(type='filepath', label='Input')
+    out_img = gr.Image(type='filepath', label='Output_image')
+    out_txt = gr.outputs.Textbox(type='text', label='Output_text')
+    button = gr.Button('Submit')
+    button.click(fn=inference,
+                 inputs=[input_img, box_thresh, unclip_ratio, text_score],
+                 outputs=[out_img, out_txt])
+
+    gr.Examples(examples=examples,
+                inputs=[input_img, box_thresh, unclip_ratio, text_score],
+                outputs=[out_img, out_txt], fn=inference)
+demo.launch(debug=True, enable_queue=True)