Update app.py

app.py

@@ -19,6 +19,34 @@ from scipy.interpolate import splprep, splev
 from scipy.ndimage import gaussian_filter1d
 import json
 
+# Language translations
+TRANSLATIONS = {
+    "english": {
+        "input_image": "Input Image",
+        "offset_value": "Offset value for Mask(mm)",
+        "coin_diameter": "Diameter of reference coin(mm). Adjust according to coin.",
+        "output_image": "Output Image",
+        "outlines": "Outlines of Objects",
+        "dxf_file": "DXF file",
+        "mask": "Mask",
+        "scaling_factor": "Scaling Factor(mm)",
+        "scaling_placeholder": "Every pixel is equal to mentioned number in millimeters",
+        "language_selector": "Select Language",
+    },
+    "dutch": {
+        "input_image": "Invoer Afbeelding",
+        "offset_value": "Offset waarde voor Masker(mm)",
+        "coin_diameter": "Diameter van referentiemunt(mm). Pas aan volgens munt.",
+        "output_image": "Uitvoer Afbeelding",
+        "outlines": "Contouren van Objecten",
+        "dxf_file": "DXF bestand",
+        "mask": "Masker",
+        "scaling_factor": "Schalingsfactor(mm)",
+        "scaling_placeholder": "Elke pixel is gelijk aan genoemd aantal in millimeters",
+        "language_selector": "Selecteer Taal",
+    }
+}
+
 
 birefnet = AutoModelForImageSegmentation.from_pretrained(
     "zhengpeng7/BiRefNet", trust_remote_code=True
@@ -345,37 +373,90 @@ def predict(image, offset, coin_size_mm):
         scaling_factor,
     )
 
-if __name__ == "__main__": 
+def update_interface(language):
+    """Updates the interface labels based on selected language"""
+    return [
+        gr.Image(label=TRANSLATIONS[language]["input_image"], type="numpy"),
+        gr.Number(label=TRANSLATIONS[language]["offset_value"], value=0.15),
+        gr.Number(label=TRANSLATIONS[language]["coin_diameter"], value=20),
+        gr.Image(label=TRANSLATIONS[language]["output_image"]),
+        gr.Image(label=TRANSLATIONS[language]["outlines"]),
+        gr.File(label=TRANSLATIONS[language]["dxf_file"]),
+        gr.Image(label=TRANSLATIONS[language]["mask"]),
+        gr.Textbox(
+            label=TRANSLATIONS[language]["scaling_factor"],
+            placeholder=TRANSLATIONS[language]["scaling_placeholder"],
+        ),
+    ]
+
+if __name__ == "__main__":
     os.makedirs("./outputs", exist_ok=True)
 
-    # Language selector in UI
-    ifer = gr.Interface(
-        fn=predict,
-        inputs=[
-            gr.Image(label="Input Image", type="numpy"),
-            gr.Number(label="Offset value for Mask(mm)", value=0.15),
-            gr.Number(label="Diameter of reference coin(mm). Adjust according to coin.", value=20),
+    with gr.Blocks() as demo:
+        # Language selector
+        language = gr.Dropdown(
+            choices=["english", "dutch"],
+            value="english",
+            label="Select Language",
+            interactive=True
+        )
         
-        ],
-        outputs=[
-            gr.Image(label="Output Image"),
-            gr.Image(label="Outlines of Objects"),
-            gr.File(label="DXF file"),
-            gr.Image(label="Mask"),
-            gr.Textbox(
-                label="Scaling Factor(mm)",
-                placeholder="Every pixel is equal to mentioned number in millimeters",
-            ),
-        ],
-        examples=[
-            ["./examples/Test20.jpg", 0.15],
-            ["./examples/Test21.jpg", 0.15],
-            ["./examples/Test22.jpg", 0.15],
-            ["./examples/Test23.jpg", 0.15],
-        ],
-    )
-    ifer.launch(share=True)
+        # Initialize interface components
+        input_image = gr.Image(label=TRANSLATIONS["english"]["input_image"], type="numpy")
+        offset = gr.Number(label=TRANSLATIONS["english"]["offset_value"], value=0.15)
+        coin_size = gr.Number(label=TRANSLATIONS["english"]["coin_diameter"], value=20)
+        
+        output_image = gr.Image(label=TRANSLATIONS["english"]["output_image"])
+        outlines = gr.Image(label=TRANSLATIONS["english"]["outlines"])
+        dxf_file = gr.File(label=TRANSLATIONS["english"]["dxf_file"])
+        mask = gr.Image(label=TRANSLATIONS["english"]["mask"])
+        scaling = gr.Textbox(
+            label=TRANSLATIONS["english"]["scaling_factor"],
+            placeholder=TRANSLATIONS["english"]["scaling_placeholder"]
+        )
 
+        # Create submit button
+        submit_btn = gr.Button("Submit")
+        
+        # Handle language change
+        language.change(
+            fn=lambda x: [
+                gr.update(label=TRANSLATIONS[x]["input_image"]),
+                gr.update(label=TRANSLATIONS[x]["offset_value"]),
+                gr.update(label=TRANSLATIONS[x]["coin_diameter"]),
+                gr.update(label=TRANSLATIONS[x]["output_image"]),
+                gr.update(label=TRANSLATIONS[x]["outlines"]),
+                gr.update(label=TRANSLATIONS[x]["dxf_file"]),
+                gr.update(label=TRANSLATIONS[x]["mask"]),
+                gr.update(
+                    label=TRANSLATIONS[x]["scaling_factor"],
+                    placeholder=TRANSLATIONS[x]["scaling_placeholder"]
+                ),
+            ],
+            inputs=[language],
+            outputs=[
+                input_image, offset, coin_size,
+                output_image, outlines, dxf_file,
+                mask, scaling
+            ]
+        )
 
+        # Handle prediction
+        submit_btn.click(
+            fn=predict,
+            inputs=[input_image, offset, coin_size],
+            outputs=[output_image, outlines, dxf_file, mask, scaling]
+        )
 
+        # Add examples
+        gr.Examples(
+            examples=[
+                ["./examples/Test20.jpg", 0.15],
+                ["./examples/Test21.jpg", 0.15],
+                ["./examples/Test22.jpg", 0.15],
+                ["./examples/Test23.jpg", 0.15],
+            ],
+            inputs=[input_image, offset]
+        )
 
+    demo.launch(share=True)