import os
import streamlit as st
import tensorflow as tf
from PIL import Image
import numpy as np
from huggingface_hub import login, hf_hub_download

# Authenticate with Hugging Face token (if available)
hf_token = os.environ.get("HF_TOKEN") 
if hf_token:
    login(token=hf_token)

# Download and load the model from the Hugging Face Hub
repo_id = os.environ.get("MODEL_ID", "willco-afk/tree-test-x")  # Get repo ID from secret or default
filename = "your_trained_model.keras"  # Updated filename
cache_dir = "./models"  # Local directory to cache the model
os.makedirs(cache_dir, exist_ok=True)  
model_path = hf_hub_download(repo_id=repo_id, filename=filename, cache_dir=cache_dir)

# Load the model
model = tf.keras.models.load_model(model_path) 


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

    # Create tabs here (after the main UI elements)
tab1, tab2 = st.tabs(["Christmas Tree Classifier", "Sample Images"])

# Tab 1: Christmas Tree Classifier
with tab1:
    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
    if uploaded_file is not None:
        # ... (Rest of the code for image processing and prediction) ...

# Tab 2: Sample Images
with tab2:
    # ... (Code for Tab 2 remains the same) ...
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}")