Solve dependency issue and Add demo result

app.py

@@ -21,9 +21,8 @@ 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
-)
+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]
@@ -38,6 +37,7 @@ def decode_batch_predictions(pred):
         output_text.append(res)
     return output_text
 
+
 def classify_image(img_path):
     # 1. Read image
     img = tf.io.read_file(img_path)
@@ -54,17 +54,15 @@ def classify_image(img_path):
     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="OCR for CAPTCHA",
-	description = "Keras Implementation of OCR model for reading captcha 🤖🦹🏻",
-        article = "Author: <a href=\"https://huggingface.co/anuragshas\">Anurag Singh</a>. Based on the keras example from <a href=\"https://keras.io/examples/vision/captcha_ocr/\">A_K_Nain</a>",
-        examples = ["dd764.png","3p4nn.png"]
-)
 
 
-iface.launch()
+demo = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="filepath"),
+    outputs=gr.Textbox(),
+    title="CAPTCHA OCR",
+    description="Upload a CAPTCHA image to recognize the text",
+)
 
+if __name__ == "__main__":
+    demo.launch(share=True)

requirements.txt

@@ -1 +1,8 @@
-tensorflow>2.6
+tensorflow>=2.6,<2.15
+keras<3.0.0
+# Local requirements
+huggingface_hub
+numpy<2
+# IMPORTANT: You are using gradio version 3.50.2, however version 4.44.1 is available, please upgrade.
+gradio>=3.50.0,<4.0.0
+httpx[socks]