import gradio as gr
from gradio import components
import tensorflow as tf
from keras.models import load_model
import numpy as np
import cv2
from PIL import Image, ImageOps

js = """
function createGradioAnimation() {
    var container = document.createElement('div');
    container.id = 'gradio-animation';
    container.style.fontSize = '2em';
    container.style.fontWeight = 'bold';
    container.style.textAlign = 'center';
    container.style.marginBottom = '20px';

    var text = 'A-EYE: An Intelligent Eye Disease Classifier';
    for (var i = 0; i < text.length; i++) {
        (function(i){
            setTimeout(function(){
                var letter = document.createElement('span');
                letter.style.opacity = '0';
                letter.style.transition = 'opacity 0.5s';
                letter.innerText = text[i];

                container.appendChild(letter);

                setTimeout(function() {
                    letter.style.opacity = '1';
                }, 50);
            }, i * 250);
        })(i);
    }

    var gradioContainer = document.querySelector('.gradio-container');
    gradioContainer.insertBefore(container, gradioContainer.firstChild);

    return 'Animation created';
}
"""

image = Image.open('background1.png')

#Resize the image
resized_image = image.resize((150, 50), Image.LANCZOS)

background = image.resize((3000, 700))
with gr.Blocks(js=js,theme=gr.themes.Soft(), css=".gradio-container { max-width: 100%;}") as demo:
    with gr.Row():
        gr.Image(background, height = 280, interactive = False, show_download_button = False, show_share_button = False)
    with gr.Row():
        gr.Markdown(
            '''
            <div style="text-align: justify;">
            A-EYE is an intelligent eye disease classifier designed for accurate eye disease classification using deep learning.
            With its enhanced model, A-EYE analyzes fundus images to detect Cataracts, Diabetic Retinopathy, Glaucoma, or Normal conditions.
            Users can easily upload a fundus image, and the system will process it to provide detailed classification results. 
            The results display probability percentages for each condition, ensuring transparency and confidence in the diagnosis.
            The condition with the highest probability is assigned to the image, offering a reliable and efficient tool for eye health assessment.
            A-EYE has the potential to assist ophthalmologists in providing first screening of suspected eye diseases.
            </div>
            '''
        )
    
    class_names = ['cataracts', 'diabetic retinopathy', 'glaucoma', 'normal']
    model = load_model('densenet121_model.keras')

    
    def predict_input_image(img):
        img = cv2.resize(img, (224, 224))
        img = img.reshape((-1, 224, 224, 3))
        img = tf.keras.applications.densenet.preprocess_input(img)
        
        #image_tensor = tf.convert_to_tensor(img)
        # Resize the image to 224x224.
        #image_tensor = tf.image.resize(image_tensor, (224, 224))
        # Cast the data to float32.
        #image_tensor = tf.cast(image_tensor, tf.float32)
        # Add a batch dimension.
        #image_tensor = tf.expand_dims(image_tensor, 0)
        # Normalize the data.
        #image_tensor = image_tensor / 255.0
        
        
        prediction = model.predict(img).flatten()
       
        predicted_class = {class_names[i]: float(prediction[i]) for i in range(4)}
        return predicted_class
        
        title=(
            '''
            <style>h1 {text-align: center;}</style>
            <h1> A-EYE: An Intelligent Eye Disease Classifier </h1>
            '''
        )
                
    gr.Interface(fn = predict_input_image, 
                 inputs = gr.Image(width = 224, height = 224), 
                 outputs = gr.Label(num_top_classes = 4),
                 #title = title,
                 #description = 'This classifier is developed to classify cataracts, diabetic retinopathy, glaucoma and normal eyes through fundus images',
                 cache_examples = False,
                 allow_flagging = 'never',
                )


demo.launch(debug='True', share='True')