Create app1.py

app1.py

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+import gradio as gr
+import numpy as np
+from keras.models import load_model
+from PIL import Image
+
+# Load the trained model
+model = load_model("mnist_model.h5")
+
+# Define prediction function
+def predict_digit(image):
+    # Resize and normalize
+    image = image.convert('L').resize((28, 28))  # convert to grayscale and resize
+    img_array = np.array(image).astype("float32") / 255.0
+    img_array = img_array.reshape(1, 28, 28)
+    
+    # Predict
+    prediction = model.predict(img_array)
+    predicted_class = np.argmax(prediction)
+    confidence = float(np.max(prediction))
+    
+    return f"Prediction: {predicted_class} (Confidence: {confidence:.2f})"
+
+# Define Gradio Interface
+interface = gr.Interface(
+    fn=predict_digit,
+    inputs=gr.Image(type="pil", shape=(200, 200), label="Upload a Digit Image"),
+    outputs=gr.Textbox(label="Prediction"),
+    title="Handwritten Digit Recognition",
+    description="Upload a handwritten digit image (0–9) to classify it using a neural network trained on the MNIST dataset."
+)
+
+interface.launch()