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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(
shape=(90, 35),
source="upload",
label="CAPTCHA image",
),
outputs="textbox",
)
iface.launch()
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