Delete app.py

app.py

@@ -1,66 +0,0 @@
-import tensorflow as tf
-from tensorflow import keras
-from tensorflow.keras import layers
-from huggingface_hub import snapshot_download
-
-import numpy as np
-import gradio as gr
-
-max_length = 5
-img_width = 200
-img_height = 50
-
-# Download the model from Hugging Face
-model_path = snapshot_download(repo_id="keras-io/ocr-for-captcha")
-
-# Load the TensorFlow SavedModel using TFSMLayer
-prediction_layer = keras.layers.TFSMLayer(model_path, call_endpoint='serving_default')
-
-with open("vocab.txt", "r") as f:
-    vocab = f.read().splitlines()
-
-# Mapping integers back to original characters
-num_to_char = layers.StringLookup(
-    vocabulary=vocab, 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=True)[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_layer(img)  # Use the TFSMLayer for prediction
-    pred_text = decode_batch_predictions(preds)
-    return pred_text[0]
-
-image = gr.inputs.Image(type='filepath')
-text = gr.outputs.Textbox()
-
-iface = gr.Interface(classify_image, image, text,
-  title="CGIP CAPTCHA RECOGNITION OCR",
-  description="Keras Implementation of OCR model for reading captcha 🤖🦹🏻",
-  examples=["dd764.png", "3p4nn.png", "ydd3g.png", "268g2.png", "36nx4.png", "3bnyf.png", "5p8fm.png", "8y6b3.png", "mnef5.png", "yxd7m.png"]
-)
-
-iface.launch()