import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image
import google.generativeai as genai 
import os
import markdown2

# Load the TensorFlow model
model_path = 'model'
model = tf.saved_model.load(model_path)

# Configure Gemini API
api_key = os.getenv("GEMINI_API_KEY")
genai.configure(api_key=api_key)

labels = ['cataract', 'diabetic_retinopathy', 'glaucoma', 'normal']

def get_disease_detail(disease_name):
    if disease_name == "normal":
        prompt = (
            "Create a text that congratulates having healthy eyes and gives bullet point tips to keep eyes healthy."
        )
    else:
        prompt = (
            f"Diagnosis: {disease_name}\n\n"
            "What is it?\n(Description about {disease_name})\n\n"
            "What causes it?\n(Explain what causes {disease_name})\n\n"
            "Suggestion\n(Suggestion to user)\n\n"
            "Reminder: Always seek professional help, such as a doctor."
        )
    try:
        response = genai.GenerativeModel("gemini-1.5-flash").generate_content(prompt)
        return markdown2.markdown(response.text.strip())
    except Exception as e:
        return f"Error: {e}"

def predict_image(image):
    image_resized = image.resize((224, 224))
    image_array = np.array(image_resized).astype(np.float32) / 255.0
    image_array = np.expand_dims(image_array, axis=0)

    predictions = model.signatures['serving_default'](tf.convert_to_tensor(image_array, dtype=tf.float32))['output_0']
    
    # Highest prediction
    top_index = np.argmax(predictions.numpy(), axis=1)[0]
    top_label = labels[top_index]
    top_probability = predictions.numpy()[0][top_index]

    explanation = get_disease_detail(top_label)

    return {top_label: top_probability}, explanation

# Example images
example_images = [
    ["exp_eye_images/0_right_h.png"],
    ["exp_eye_images/03fd50da928d_dr.png"],
    ["exp_eye_images/108_right_h.png"],
    ["exp_eye_images/1062_right_c.png"],
    ["exp_eye_images/1084_right_c.png"],
    ["exp_eye_images/image_1002_g.jpg"]
]

# Custom CSS for HTML height
css = """
.scrollable-html {
    height: 206px;
    overflow-y: auto;  
    border: 1px solid #ccc;  
    padding: 10px;  
    box-sizing: border-box;
}
"""

# Gradio Interface
interface = gr.Interface(
    fn=predict_image,
    inputs=gr.Image(type="pil"),
    outputs=[
        gr.Label(num_top_classes=1, label="Prediction"), 
        gr.HTML(label="Explanation", elem_classes=["scrollable-html"])
    ],
    examples=example_images,
    title="Eye Diseases Classifier",
    description=(
        "Upload an image of an eye fundus, and the model will predict it.\n\n"
        "**Disclaimer:** This model is intended as a form of learning process in the field of health-related machine learning and was trained with a limited amount and variety of data with a total of about 4000 data, so the prediction results may not always be correct. There is still a lot of room for improvisation on this model in the future."
    ),
    allow_flagging="never",
    css=css
)

interface.launch(share=True)