Create app.py

app.py

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+import gradio as gr
+import shutil
+import os
+from MangaTranslator import MangaTranslator
+
+# 1. Initialize the Translator (runs once on startup)
+print("⏳ Initializing models... (This takes 30s)")
+translator = MangaTranslator(
+    yolo_model_path='comic_yolov8m.pt',
+    translation_model="LiquidAI/LFM2-350M-ENJP-MT",
+    font_path="font.ttf"
+)
+print("✅ Models Ready!")
+
+# 2. Define the Processing Function
+def translate_manga(input_image):
+    if input_image is None:
+        return None
+        
+    # Create temp paths for the pipeline
+    temp_in = "temp_input.jpg"
+    temp_out = "temp_output.jpg"
+    
+    # Gradio gives us a PIL image, save it so your script can read it
+    input_image.save(temp_in)
+    
+    # Run your existing pipeline
+    try:
+        translator.process_single_image(
+            image_path=temp_in,
+            output_path=temp_out,
+            series_info=None # No context for the demo
+        )
+        return temp_out
+    except Exception as e:
+        print(f"Error: {e}")
+        return input_image # Return original if fail
+
+# 3. Launch the Interface
+if __name__ == "__main__":
+    iface = gr.Interface(
+        fn=translate_manga,
+        inputs=gr.Image(type="pil", label="Upload Manga Page (Japanese)"),
+        outputs=gr.Image(type="filepath", label="Translated Page (English)"),
+        title="✨ LiquidAI Manga Translator (350M Demo)",
+        description="Running natively on CPU using Liquid LFM2-350M, MangaOCR, and LaMa Inpainting.",
+        examples=[["example.jpg"]] # Optional: If you upload an example image
+    )
+    iface.launch()