Update app.py

app.py

@@ -1,9 +1,6 @@
 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
 
@@ -11,11 +8,9 @@ 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
-)
+# Load the TensorFlow SavedModel using TFSMLayer
+saved_model_path = "/home/user/.cache/huggingface/hub/models--keras-io--ocr-for-captcha/snapshots/1d695c4be3c72166292ff61c361d47c96f43cb7f"
+prediction_layer = keras.layers.TFSMLayer(saved_model_path, call_endpoint='serving_default')
 
 with open("vocab.txt", "r") as f:
     vocab = f.read().splitlines()
@@ -51,19 +46,18 @@ def classify_image(img_path):
     # 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)
+    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,
+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",]
+  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()