app.py

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

# Path to your zipped model file (this will be the local path after downloading)
ZIP_MODEL_PATH = '/app/your_trained_model_resnet50.keras.zip'  
UNZIPPED_MODEL_PATH = '/app/your_trained_model_resnet50.keras'  # Path where the model will be extracted

# Google Drive link to the model file
MODEL_URL = 'https://drive.google.com/uc?export=download&id=1-4p6AZBkooWL1rhN9WwIrfd9fJbhzY0e'

# Download the model if it doesn't exist
if not os.path.exists(ZIP_MODEL_PATH):
    gdown.download(MODEL_URL, ZIP_MODEL_PATH, quiet=False)
    print(f"Model downloaded to {ZIP_MODEL_PATH}")

# Unzip the model if it hasn't been unzipped already
if not os.path.exists(UNZIPPED_MODEL_PATH):
    with zipfile.ZipFile(ZIP_MODEL_PATH, 'r') as zip_ref:
        zip_ref.extractall('/app')
        print(f"Model unzipped to {UNZIPPED_MODEL_PATH}")

# Load the model
try:
    model = tf.keras.models.load_model(UNZIPPED_MODEL_PATH)
    print("Model loaded successfully!")
except Exception as e:
    print(f"Error loading model: {e}")

# Define the function to predict decoration
def predict_decoration(image: Image.Image):
    # Preprocess the image to match the model input format
    image = image.resize((224, 224))  # Resize to match model's expected input size
    image_array = np.array(image) / 255.0  # Normalize the image to [0, 1]
    image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension

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

# Set up Streamlit interface with Christmas theme
st.set_page_config(page_title="Tree Decoration Predictor", page_icon="🎄")

# Custom CSS for Christmas theme
st.markdown("""
    <style>
        body {
            background-color: #fae1dc; /* Soft pink background */
            color: #1b5e20; /* Deep green text */
            font-family: 'Comic Sans MS', cursive, sans-serif;
        }
        .css-18e3th9 {
            background-color: #d32f2f; /* Christmas red button */
            color: white;
        }
        .css-1lcbm2e {
            background-color: #388e3c; /* Christmas green button */
            color: white;
        }
        .stButton>button {
            background-color: #f44336; /* Red button color */
            color: white;
            border-radius: 12px;
            padding: 10px;
            font-size: 16px;
        }
        .stButton>button:hover {
            background-color: #c62828; /* Darker red on hover */
        }
        .stMarkdown {
            font-size: 18px;
        }
        .stTab {
            font-size: 20px;
            font-weight: bold;
            color: #388e3c; /* Christmas green */
        }
        .stImage {
            border: 2px solid #388e3c; /* Green border around images */
        }
    </style>
""", unsafe_allow_html=True)

# Title of the page
st.title("🎄 Tree Decoration Predictor 🎄")

# Create tabs for better organization
tab1, tab2 = st.tabs(["Upload Image", "Tree Image URLs"])

# Upload Image Tab
with tab1:
    uploaded_image = st.file_uploader("Upload an image of a tree", type=["jpg", "jpeg", "png"])

    if uploaded_image:
        image = Image.open(uploaded_image)
        st.image(image, caption="Uploaded Tree Image", use_container_width=True)

        if st.button("Predict Decoration"):
            prediction = predict_decoration(image)
            st.write(f"Prediction: {prediction}")

# Tree Image URLs Tab
with tab2:
    st.subheader("🎄 Tree Image Samples 🎄")
    st.markdown("""
        View some of my decorated and undecorated tree samples for the Model here:
        [View Trees](https://www.dropbox.com/scl/fo/cuzo12z39cxv6joz7gz2o/ACf5xSjT7nHqMRdgh21GYlc?raw=1)
        
        Download the tree samples pictures to test them on the model yourself here:
        [Download Trees](https://www.dropbox.com/scl/fo/cuzo12z39cxv6joz7gz2o/ACf5xSjT7nHqMRdgh21GYlc?raw=1&dl=1)
    """)
    
    # Add download link for images if needed
    st.markdown("[Download the image list](https://raw.githubusercontent.com/willco-afk/tree-samples/main/tree_images.txt)")