Create app

app

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+import streamlit as st
+import tensorflow as tf
+from tensorflow.keras.applications import MobileNet
+from tensorflow.keras.applications.mobilenet import preprocess_input, decode_predictions
+from tensorflow.keras.preprocessing import image
+import numpy as np
+
+# Load the pre-trained MobileNet model
+model = MobileNet(weights='imagenet')
+
+# Create a Streamlit web app
+st.title("Image Classification with MobileNet")
+
+# Upload an image through Streamlit
+uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+
+if uploaded_image is not None:
+    # Display the uploaded image
+    st.image(uploaded_image, caption='Uploaded Image', use_column_width=True)
+    
+    # Preprocess the image for MobileNet
+    img = image.load_img(uploaded_image, target_size=(224, 224))
+    img_array = image.img_to_array(img)
+    img_array = preprocess_input(img_array)
+    img_array = np.expand_dims(img_array, axis=0)
+
+    # Classify the image using MobileNet
+    predictions = model.predict(img_array)
+    decoded_predictions = decode_predictions(predictions, top=3)[0]
+
+    st.subheader("Top Predictions:")
+    
+    for i, (imagenet_id, label, score) in enumerate(decoded_predictions):
+        st.write(f"{i + 1}: {label} ({score:.2f})")
+