Create application

app.py

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+import gradio as gr
+import tensorflow as tf
+from huggingface_hub import from_pretrained_keras
+from tensorflow import keras
+
+num_to_char = keras.layers.StringLookup(
+    vocabulary=sorted(
+        set("abcdefghijklmnpqrstuvwxyz123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ".upper())
+    ),
+    mask_token=None,
+    invert=True,
+)
+
+model = from_pretrained_keras("wangxinhe/luogu-captcha-recognition", compile=False)
+
+# Get the prediction model by extracting layers till the output layer
+prediction_model = keras.models.Model(
+    model.input[0], model.get_layer(name="dense2").output
+)
+prediction_model.summary()
+
+
+def ocr(img):
+    # Convert to float32 in [0, 1] range
+    img = tf.image.convert_image_dtype(img, tf.float32)
+    # Transpose the image because we want the time
+    # dimension to correspond to the width of the image.
+    img = tf.transpose(img, perm=[1, 0, 2])
+
+    preds = prediction_model(tf.expand_dims(img, axis=0))
+
+    # Use greedy search. For complex tasks, you can use beam search
+    results = keras.backend.ctc_decode(
+        preds, input_length=[preds.shape[1]], greedy=True
+    )[0][0][:, :4]
+    return tf.strings.reduce_join(num_to_char(results[0])).numpy().decode("ascii")
+
+
+iface = gr.Interface(
+    fn=ocr,
+    inputs=gr.Image(
+        value="https://www.luogu.com.cn/api/verify/captcha",
+        sources=["upload", "clipboard"],
+        label="CAPTCHA image",
+    ),
+    outputs="textbox",
+)
+iface.launch()

requirements.txt

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+gradio<5,>=4
+huggingface-hub
+tensorflow<3,>=2