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Upload app.py
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app.py
CHANGED
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@@ -103,16 +103,93 @@ def transcribe_handwriting(image):
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return output_text[0] if output_text else "文字を認識できませんでした。"
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if __name__ == "__main__":
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demo.launch()
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return output_text[0] if output_text else "文字を認識できませんでした。"
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def process_sketch(image_dict):
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"""ImageEditorからの入力を処理"""
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if image_dict is None:
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return "手書きしてください。"
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# gr.ImageEditorの出力は辞書形式: {"background": ..., "layers": [...], "composite": ...}
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if isinstance(image_dict, dict):
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image = image_dict.get("composite")
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if image is None:
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image = image_dict.get("background")
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else:
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image = image_dict
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if image is None:
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return "手書きしてください。"
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return transcribe_handwriting(image)
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# Gradioインターフェースの構築
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with gr.Blocks(title="手書き文字認識システム") as demo:
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gr.Markdown(
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"""
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# 手書き文字認識システム
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**Qwen3-VL-4B-Instruct** を使用した手書き文字のOCR(光学文字認識)システムです。
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日本語とアルファベットの両方に対応しています。
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"""
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)
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with gr.Tab("画像アップロード"):
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gr.Markdown("手書き文字が書かれた画像をアップロードしてください。")
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with gr.Row():
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with gr.Column():
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upload_image = gr.Image(
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label="画像をアップロード",
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type="pil",
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height=400,
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)
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upload_btn = gr.Button("文字を認識", variant="primary")
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with gr.Column():
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upload_output = gr.Textbox(
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label="認識結果",
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lines=10,
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)
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upload_btn.click(
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fn=transcribe_handwriting,
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inputs=upload_image,
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outputs=upload_output,
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)
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with gr.Tab("手書き入力"):
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gr.Markdown("マウスやタッチで文字を書いてください。")
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with gr.Row():
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with gr.Column():
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sketch_input = gr.ImageEditor(
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label="手書きエリア",
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sources=(),
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brush=gr.Brush(colors=["#000000"], color_mode="fixed"),
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canvas_size=(600, 400),
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type="pil",
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)
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sketch_btn = gr.Button("文字を認識", variant="primary")
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with gr.Column():
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sketch_output = gr.Textbox(
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label="認識結果",
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lines=10,
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)
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sketch_btn.click(
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fn=process_sketch,
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inputs=sketch_input,
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outputs=sketch_output,
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)
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gr.Markdown(
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"""
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---
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### 使い方のヒント
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- **画像アップロード**: スキャンした手書きメモや写真をアップロード
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- **手書き入力**: マウスで直接文字を書いて認識をテスト
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- 認識精度は文字の明瞭さに依存します
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*Powered by Qwen3-VL-4B-Instruct*
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"""
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)
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if __name__ == "__main__":
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demo.launch()
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