Create app.py

app.py

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+import gradio as gr
+import CaptchaCracker as cc
+
+
+# Target image data size
+img_width = 200
+img_height = 50
+# Target image label length
+max_length = 6
+# Target image label component
+characters = {'0', '1', '2', '3', '4', '5', '6', '7', '8', '9'}
+
+# Model weight file path
+weights_path = "weights.h5"
+# Creating a model application instance
+AM = cc.ApplyModel(weights_path, img_width, img_height, max_length, characters)
+
+def inference(target_img_path):
+  # Predicted value
+  pred = AM.predict(target_img_path)
+  return pred
+  
+
+
+block = gr.Blocks()
+
+with block:
+    gr.Markdown("Gradio Demo for WooilJeong/CaptchaCracker")
+    
+    with gr.Tabs():
+        with gr.TabItem("CaptchaCracker"):
+            with gr.Row():
+                captchaimg = gr.inputs.Image(type="filepath")
+                text = gr.outputs.Textbox()
+            catch_run = gr.Button("Run")
+            catch_run.click(inference, inputs=captchaimg, outputs=text)
+    
+block.launch()