Update app.py

app.py

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+# app.py
+import streamlit as st
+import tensorflow as tf
+from tensorflow import keras
+import numpy as np
+from PIL import Image
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+
+# Load CIFAR-10 dataset
+(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()
+
+# Normalize pixel values to [0, 1]
+x_train, x_test = x_train / 255.0, x_test / 255.0
+
+# Split training data into training and validation sets
+x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.2, random_state=42)
+
+# Define a simple CNN model
+def create_model():
+    model = keras.models.Sequential([
+        keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
+        keras.layers.MaxPooling2D((2, 2)),
+        keras.layers.Conv2D(64, (3, 3), activation='relu'),
+        keras.layers.MaxPooling2D((2, 2)),
+        keras.layers.Conv2D(128, (3, 3), activation='relu'),
+        keras.layers.Flatten(),
+        keras.layers.Dense(128, activation='relu'),
+        keras.layers.Dense(10, activation='softmax')
+    ])
+    return model
+
+# Check if the model is already saved
+import os
+if not os.path.exists("cifar10_cnn_model.h5"):
+    # Create and compile the model
+    model = create_model()
+    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+
+    # Train the model
+    st.write("Training the model...")
+    history = model.fit(x_train, y_train, epochs=100, validation_data=(x_val, y_val))  # Reduced epochs for quick testing
+
+    # Save the model
+    model.save("cifar10_cnn_model.h5")
+    st.write("Model saved as 'cifar10_cnn_model.h5'")
+else:
+    # Load the pre-trained model
+    st.write("Loading pre-trained model...")
+    model = keras.models.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("Image Detection System")
+
+# 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}%")
+model.save("cifar10_cnn_model.keras")