Create app.py

app.py

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+# -*- coding: utf-8 -*-
+"""ocrforcaptcha.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/161aX_CGT4Q3zAMkLeLSOZVDhMyMtsqPx
+"""
+
+# Commented out IPython magic to ensure Python compatibility.
+# %%capture
+# !pip install gradio
+# !pip install huggingface-hub
+
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+
+from huggingface_hub import from_pretrained_keras
+
+import numpy as np
+import gradio as gr
+
+characters = {'d', 'w', 'y', '4', 'f', '6', 'g', 'e', '3', '5', 'p', 'x', '2', 'c', '7', 'n', 'b', '8', 'm'}
+max_length = 5
+img_width = 200
+img_height = 50
+
+model = from_pretrained_keras("keras-io/ocr-for-captcha")
+
+prediction_model = keras.models.Model(
+    model.get_layer(name="image").input, model.get_layer(name="dense2").output
+)
+
+# Mapping characters to integers
+char_to_num = layers.StringLookup(
+    vocabulary=list(characters), mask_token=None
+)
+
+# Mapping integers back to original characters
+num_to_char = layers.StringLookup(
+    vocabulary=char_to_num.get_vocabulary(), mask_token=None, invert=True
+)
+
+def decode_batch_predictions(pred):
+    input_len = np.ones(pred.shape[0]) * pred.shape[1]
+    # Use greedy search. For complex tasks, you can use beam search
+    results = keras.backend.ctc_decode(pred, input_length=input_len, greedy=False)[0][0][
+        :, :max_length
+    ]
+    # Iterate over the results and get back the text
+    output_text = []
+    for res in results:
+        res = tf.strings.reduce_join(num_to_char(res)).numpy().decode("utf-8")
+        output_text.append(res)
+    return output_text
+
+def classify_image(img_path):
+    # 1. Read image
+    img = tf.io.read_file(img_path)
+    # 2. Decode and convert to grayscale
+    img = tf.io.decode_png(img, channels=1)
+    # 3. Convert to float32 in [0, 1] range
+    img = tf.image.convert_image_dtype(img, tf.float32)
+    # 4. Resize to the desired size
+    img = tf.image.resize(img, [img_height, img_width])
+    # 5. 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])
+    img = tf.expand_dims(img, axis=0)
+    preds = prediction_model.predict(img)
+    pred_text = decode_batch_predictions(preds)
+    return pred_text
+  
+image = gr.inputs.Image(type='filepath')
+text = gr.outputs.Textbox()
+
+iface = gr.Interface(classify_image,image,text,
+  title="OCR for CAPTCHA",
+	description = "Keras Implementation of OCR model for reading captcha 🤖🦹🏻",
+        article = "Author: <a href=\"https://huggingface.co/anuragshas\">Anurag Singh</a>"
+)
+
+
+iface.launch()
+