import streamlit as st
import tensorflow as tf
from PIL import Image
import io
import numpy as np
from streamlit_option_menu import option_menu

# Function to add a background image
def add_bg_from_local(image_path):
    with open(image_path, "rb") as image_file:
        encoded_string = base64.b64encode(image_file.read())
    st.markdown(
        f"""
        <style>
        .stApp {{
            background-image: url("data:image/{"png"};base64,{encoded_string.decode()}");
            background-size: cover;
            background-repeat: no-repeat;
            background-attachment: fixed;
        }}
        </style>
        """,
        unsafe_allow_html=True
    )

# Add background image
add_bg_from_local('flower.png')

def load_image():
    uploaded_file = st.file_uploader(label='Pick an image to test')
    if uploaded_file is not None:
        image_data = uploaded_file.getvalue()
        st.image(image_data)
        img = Image.open(io.BytesIO(image_data))
        img = img.resize((224, 224))
        return img
    else:
        return None

def load_model():
    model_name = 'Model/model.h5'
    model = tf.keras.models.load_model(model_name)
    return model

def load_labels():
    with open('Oxford-102_Flower_dataset_labels.txt', 'r') as file:
        data = file.read().splitlines()
    return data

def predict(model, labels, img):
    img_array = tf.keras.preprocessing.image.img_to_array(img)
    img_array = tf.expand_dims(img_array, 0)  # Create a batch

    prediction = model.predict(img_array)
    predicted_class = np.argmax(prediction[0], axis=-1)

    flower = labels[predicted_class]
    closeness = np.round(prediction[0][predicted_class] * 100, 2)

    return flower, closeness

def main():
    with st.sidebar:
        selected = option_menu(
            menu_title="Main Menu",
            options=["About", "Local Classifier", "Extensive Classifier", "Project Details"],
            icons=["info-circle", "camera", "search", "clipboard-list"],
            menu_icon="cast",
            default_index=0,
        )

    if selected == "About":
        st.title('About')
        st.markdown("""
            ### NAME:
            TOLULOPE

            ### CLASS:
            HND2

            ### LEVEL:
            400L
        """)

    elif selected == "Local Classifier":
        st.title('Local Classifier')
        st.write("This is a demo of an image classification model trained on the Oxford Flower Dataset.")
        st.write("To test the model, upload an image of a flower and click the 'Run on image' button.")
        
        model = load_model()
        labels = load_labels()
        image = load_image()
        result = st.button('Run on image')
        
        if result and image is not None:
            st.markdown('**_Calculating results..._**')
            flower, closeness = predict(model, labels, image)
            st.markdown(f'<h4 style="color:blue;">Flower Type: <span style="color:black;">{flower}</span></h4>', unsafe_allow_html=True)
            st.markdown(f'<h4 style="color:green;">Closeness: <span style="color:black;">{closeness}%</span></h4>', unsafe_allow_html=True)

    elif selected == "Extensive Classifier":
        st.title('Extensive Classifier')
        st.write("This section will contain extensive classifier details and options.")
        # Add your extensive classifier code here

    elif selected == "Project Details":
        st.title('Project Details')
        st.markdown("""
            This project is about classifying flowers using deep learning. 
            The Oxford Flower Dataset, consisting of 102 flower categories, 
            is used for training the model. The images have large scale, 
            pose, and light variations. In addition, there are categories 
            that have large variations within the category and several very 
            similar categories. The dataset is visualized using isomap with 
            shape and colour features. Link to the dataset is available 
            @ https://www.kaggle.com/datasets/yousefmohamed20/oxford-102-flower-dataset.
        """)

if __name__ == '__main__':
    main()