app.py

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})")