Create app.py

app.py

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+import streamlit as st
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# Load the model
+model = tf.keras.models.load_model("cifar10_saved_model")
+
+# CIFAR-10 class names
+class_names = [
+    "Airplane", "Automobile", "Bird", "Cat", "Deer",
+    "Dog", "Frog", "Horse", "Ship", "Truck"
+]
+
+# Streamlit app layout
+st.title("CIFAR-10 Image Classifier")
+st.write("Upload an image to classify it into one of the CIFAR-10 categories.")
+
+# File uploader
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+
+if uploaded_file:
+    # Preprocess the uploaded image
+    image = Image.open(uploaded_file).resize((32, 32))
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    # Convert image to array
+    img_array = np.array(image) / 255.0  # Normalize
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+
+    # Predict
+    with st.spinner("Classifying..."):
+        predictions = model.predict(img_array)
+        predicted_class = class_names[np.argmax(predictions)]
+        confidence = np.max(predictions)
+
+    # Display results
+    st.success(f"Prediction: {predicted_class}")
+    st.info(f"Confidence: {confidence:.2f}")