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app.py

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+# app.py : Streamlit Web App for CNN Image Classification
+
+import streamlit as st
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# Load trained model
+model = tf.keras.models.load_model("cnn_cifar10_model.h5")
+
+# CIFAR-10 class labels
+class_names = ['Airplane','Automobile','Bird','Cat','Deer',
+               'Dog','Frog','Horse','Ship','Truck']
+
+# Streamlit App UI
+st.title("🖼️ CIFAR-10 Image Classifier")
+st.write("Upload an image (32x32 or larger) to classify it into one of 10 classes.")
+
+# File uploader
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg","jpeg","png"])
+
+if uploaded_file is not None:
+    # Show uploaded image
+    img = Image.open(uploaded_file)
+    st.image(img, caption="Uploaded Image", width=150)
+    
+    # Preprocess image
+    img = img.resize((32,32))
+    img_array = np.array(img)/255.0
+    img_array = np.expand_dims(img_array, axis=0)
+
+    # Prediction
+    prediction = model.predict(img_array)
+    pred_class = class_names[np.argmax(prediction)]
+
+    st.write(f"🎯 **Predicted Class:** {pred_class}")

cnn_cifar10_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a02b1abb1097ee8fb18d37e49009e247309195510d70dfbfc389cdfef7c7b9a1
+size 1518296

requirements.txt

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+streamlit
+tensorflow
+numpy
+pillow
+matplotlib

space.yml

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+sdk: streamlit
+python_version: "3.9"
+app_file: app.py