app.py

import os
import streamlit as st
import tensorflow as tf
from PIL import Image
import numpy as np

# Load your Keras model from Google Drive
model = tf.keras.models.load_model('/content/drive/MyDrive/your_trained_model.keras')

# Streamlit UI
st.title("Christmas Tree Classifier")
st.write("Upload an image of a Christmas tree to classify it:")

uploaded_file = st.file_uploader("Choose 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)
    st.write("")
    st.write("Classifying...")

    # Preprocess the image
    image = image.resize((224, 224))  # Resize to match your model's input size
    image_array = np.array(image) / 255.0  # Normalize pixel values
    image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension

    # Make prediction
    prediction = model.predict(image_array)
    
    # Get predicted class
    predicted_class = "Decorated" if prediction[0][0] >= 0.5 else "Undecorated"

    # Display the prediction
    st.write(f"Prediction: {predicted_class}")