Update app.py

app.py

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+import numpy as np
+import matplotlib.pyplot as plt
+import tensorflow as tf
+from tensorflow.keras import datasets, layers, models
+import gradio as gr
+from tensorflow.keras.preprocessing import image
+
+# Load CIFAR-10 dataset
+(x_train, y_train), (x_test, y_test) = datasets.cifar10.load_data()
+x_train, x_test = x_train / 255.0, x_test / 255.0
+
+# CIFAR-10 class names
+class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
+               'dog', 'frog', 'horse', 'ship', 'truck']
+
+# Build model
+model = models.Sequential([
+    layers.Conv2D(32, (3,3), activation='relu', input_shape=(32,32,3)),
+    layers.MaxPooling2D((2,2)),
+
+    layers.Conv2D(64, (3,3), activation='relu'),
+    layers.MaxPooling2D((2,2)),
+
+    layers.Conv2D(64, (3,3), activation='relu'),
+
+    layers.Flatten(),
+    layers.Dense(64, activation='relu'),
+    layers.Dense(10, activation='softmax')
+])
+
+# Compile and train briefly (use more epochs for better accuracy)
+model.compile(optimizer='adam',
+              loss='sparse_categorical_crossentropy',
+              metrics=['accuracy'])
+model.fit(x_train, y_train, epochs=1, validation_data=(x_test, y_test))
+
+# Prediction function
+def predict(img):
+    img = image.smart_resize(img, (32, 32))  # Resize to CIFAR-10 size
+    img_array = np.expand_dims(img, axis=0) / 255.0
+    prediction = model.predict(img_array)
+    pred_class = np.argmax(prediction)
+    return {class_names[i]: float(prediction[0][i]) for i in range(10)}
+
+# Gradio interface
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(num_top_classes=3),
+    title="CIFAR-10 Image Classifier",
+    description="Upload an image and the model will predict which CIFAR-10 class it belongs to."
+)
+
+demo.launch()