Upload app.py

app.py

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+
+import gradio as gr
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+import numpy as np
+from PIL import Image
+import cv2
+
+# Load the trained OCR model
+model = load_model("path/to/your_model.h5")
+
+# Define a function to preprocess the image
+def preprocess_image(image):
+    image = np.array(image.convert('L'))  # Convert to grayscale
+    image = cv2.resize(image, (width, height))  # Resize to expected input shape
+    image = image / 255.0  # Normalize pixel values
+    image = np.expand_dims(image, axis=-1)  # Add channel dimension if necessary
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+    return image
+
+# Define the prediction function
+def predict(image):
+    preprocessed_image = preprocess_image(image)
+    prediction = model.predict(preprocessed_image)
+    predicted_text = decode_prediction(prediction)  # Implement your decoding function here
+    return predicted_text
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Image(type="pil"),
+    outputs="text",
+    title="Captcha OCR",
+    description="An OCR model to read captcha text from images."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    interface.launch()