Create app.py

app.py

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+import streamlit as st
+from tensorflow.keras.applications.vgg16 import VGG16, preprocess_input, decode_predictions
+from tensorflow.keras.preprocessing import image
+import numpy as np
+from PIL import Image
+
+# Load VGG16 model
+@st.cache_resource
+def load_model():
+    model = VGG16(weights='imagenet')
+    return model
+
+model = load_model()
+
+st.title(" Image Classification with VGG16")
+st.write("Upload an image and let VGG16 predict what's in it!")
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file:
+    img = Image.open(uploaded_file).convert("RGB")
+    st.image(img, caption="Uploaded Image", use_column_width=True)
+
+    # Preprocess the image
+    img_resized = img.resize((224, 224))
+    img_array = image.img_to_array(img_resized)
+    img_batch = np.expand_dims(img_array, axis=0)
+    img_preprocessed = preprocess_input(img_batch)
+
+    # Predict
+    preds = model.predict(img_preprocessed)
+    top_preds = decode_predictions(preds, top=3)[0]
+
+    st.subheader("Top Predictions:")
+    for i, (imagenet_id, label, prob) in enumerate(top_preds):
+        st.write(f"**{label}** – {prob:.2%}")