app.py

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

# Function to load the model from the zip file
def load_model_from_zip(zip_file_path):
    with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
        zip_ref.extractall('.')  # Extract to the current directory
    # Load the SavedModel directly from the current directory (root)
    model = tf.keras.models.load_model('.')  
    return model

# Load the model 
model = load_model_from_zip('my_christmas_tree_model.zip')  

# 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)

    # Preprocess the image
    image = image.resize((150, 150))  # Resize to match your model's input shape
    image_array = np.array(image) / 255.0  # Normalize
    image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension

    # Make prediction
    prediction = model.predict(image_array)

    # Interpret prediction (assuming binary classification)
    class_names = ['Undecorated', 'Decorated']  # Update with your actual class names
    predicted_class_index = 1 if prediction[0][0] >= 0.5 else 0  # Adjust threshold if needed
    predicted_class = class_names[predicted_class_index]

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