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README.md

@@ -1,12 +1,12 @@
 ---
-title: Ocr Cgip
-emoji: 💬
+title: Optical Char
+emoji: 🚀
 colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 4.36.1
+colorTo: yellow
+sdk: streamlit
+sdk_version: 1.36.0
 app_file: app.py
 pinned: false
 ---
 
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+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
+
+max_length = 5
+img_width = 200
+img_height = 50
+
+model = from_pretrained_keras("keras-io/ocr-for-captcha", compile=False)
+
+prediction_model = keras.models.Model(
+    model.get_layer(name="image").input, model.get_layer(name="dense2").output
+)
+
+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_model.predict(img)
+    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()
+

requirements.txt

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+tensorflow>2.6

vocab.txt

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+[UNK]
+8
+6
+m
+x
+d
+y
+w
+2
+7
+n
+g
+5
+c
+f
+p
+e
+3
+4
+b