Add choice between the Tifinagh-IRCAM and the full Tifinagh character set models.

app.py

@@ -4,17 +4,20 @@ from doctr.models import ocr_predictor, from_hub
 import gradio as gr
 
 os.environ['USE_TORCH'] = '1'
-reco_model = from_hub('ayymen/crnn_mobilenet_v3_large_zgh')
+reco_model_zgh = from_hub('ayymen/crnn_mobilenet_v3_large_zgh')
+predictor_zgh = ocr_predictor(reco_arch=reco_model_zgh, pretrained=True)
+
+reco_model = from_hub('ayymen/crnn_mobilenet_v3_large_tifinagh')
 predictor = ocr_predictor(reco_arch=reco_model, pretrained=True)
 
 title = "Tifinagh OCR"
 description = """Upload an image to get the OCR results!
 Thanks to @iseddik for the data!"""
 
-def ocr(img):
+def ocr(img, script):
     img.save("out.jpg")
     doc = DocumentFile.from_images("out.jpg")
-    output = predictor(doc)
+    output = predictor_zgh(doc) if script == "Tifinagh-IRCAM" else predictor(doc)
     res = ""
     for obj in output.pages:
         for obj1 in obj.blocks:
@@ -31,14 +34,21 @@ def ocr(img):
     return res, _output_name
 
 demo = gr.Interface(fn=ocr,
-                    inputs=gr.Image(type="pil"),
+                    inputs=[
+                        gr.Image(type="pil"),
+                        gr.Dropdown(choices=['Tifinagh-IRCAM', 'Tifinagh'], label="Script", value="Tifinagh-IRCAM")
+                    ],
                     outputs=[
                         gr.Textbox(lines=10, label="Full Text"),
                         gr.File(label="Download OCR Results")
                     ],
                     title=title,
                     description=description,
-                    examples=[["Examples/1.jpg"],["Examples/2.jpg"],["Examples/3.png"]]
+                    examples=[
+                        ["Examples/3.jpg", "Tifinagh-IRCAM"],
+                        ["Examples/2.jpg", "Tifinagh-IRCAM"],
+                        ["Examples/1.png", "Tifinagh-IRCAM"]
+                    ]
                     )
 
 demo.launch(debug=True)