Update app.py

app.py

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+# app.py
+import streamlit as st
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+import numpy as np
+from PIL import Image
+
+# Load the pre-trained model
+model = load_model("cifar10_cnn_model.h5")
+
+# Class names for CIFAR-10 dataset
+class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
+
+# Streamlit app title
+st.title("CIFAR-10 Image Classification")
+
+# Upload image
+uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Display the uploaded image
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    # Preprocess the image
+    image = image.resize((32, 32))  # Resize to match CIFAR-10 input size
+    image = np.array(image) / 255.0  # Normalize pixel values
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+
+    # Make prediction
+    predictions = model.predict(image)
+    predicted_class = np.argmax(predictions)
+    confidence = np.max(predictions) * 100
+
+    # Display results
+    st.write(f"**Prediction:** {class_names[predicted_class]}")
+    st.write(f"**Confidence:** {confidence:.2f}%")