Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import tensorflow as tf
+import cv2
+
+# Load the trained model
+model = tf.keras.models.load_model("Handwritten_model.h5")  # Adjust this if you use the SavedModel format
+
+def predict_digit(image):
+    # Preprocess the image
+    img = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)  # Convert to grayscale
+    img = cv2.resize(img, (28, 28))  # Resize to 28x28 pixels
+    img = np.invert(img)  # Invert the image colors
+    img = img.astype("float32") / 255  # Normalize to [0, 1]
+    img = img.reshape(1, 28, 28)  # Reshape for the model input
+
+    # Make prediction
+    prediction = model.predict(img)
+    predicted_class = np.argmax(prediction)
+    return predicted_class
+
+# Define the Gradio interface
+gr.Interface(
+    fn=predict_digit,
+    inputs=gr.inputs.Image(type="numpy", label="Upload a digit image"),
+    outputs="text",
+    title="Handwritten Digit Recognition",
+    description="Upload an image of a handwritten digit, and the model will predict which digit it is."
+).launch()