Create app.py

app.py

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+import requests
+import gradio as gr
+import pandas as pd
+from PIL import Image, ImageDraw
+
+def infer(im):
+  im.save('converted.png')
+  url = 'https://ajax.thehive.ai/api/demo/classify?endpoint=text_recognition'
+  files = {
+      'image': ('converted.png', open('converted.png', 'rb'), 'image/png'),
+      'model_type': (None, 'detection'),
+      'media_type': (None, 'photo'),
+  }
+  res = requests.post(url, files=files).json()
+  img = im.convert('RGB')
+
+  words = []
+  draw = ImageDraw.Draw(img,'RGBA')
+  for output in res['response']['output']:
+    for poly in output['bounding_poly']:
+      words += [c['class'] for c in poly['classes']]
+      draw.rectangle((poly['dimensions']['left']-2,poly['dimensions']['top']-2,poly['dimensions']['right']+2,poly['dimensions']['bottom']+2), outline=(0,255,0,255), fill=(0,255,0,50),width=2)
+
+  img.save('result.png')
+  return 'result.png', '\n'.join([o['block_text'] for o in res['response']['output']]), pd.DataFrame(words)
+
+iface = gr.Interface(
+    fn=infer,
+    title="Hive OCR",
+    description="Demo for Hive OCR.Transcribe and analyze media depicting typed, written, or graphic text",
+    inputs=[gr.inputs.Image(label='image', type='pil')],
+    outputs=['image', 'text', gr.outputs.Dataframe(headers=['word'])],
+    examples=[],
+    article="<a href=\"https://thehive.ai/hive-ocr-solutions\">Hive OCR</a>",
+).launch()