import streamlit as st
import tensorflow as tf
import numpy as np
from PIL import Image
import os
import uuid
from datetime import datetime

# Load the trained model
model = tf.keras.models.load_model('oct_classification_final_model_lg.keras')

# Define the class labels
class_labels = ['CNV', 'DME', 'DRUSEN', 'NORMAL']

# App title and description
st.title("OCT Retinal Image Analyzer")
st.write("Created for MedDots Company")

# File uploader
uploaded_file = st.file_uploader("Choose an OCT image...", type=["jpg", "jpeg", "png"])

if uploaded_file is not None:
    # Display image
    image = Image.open(uploaded_file)
    st.image(image, caption='Uploaded OCT Image', use_column_width=True)

    # Preprocessing image for model
    img = image.convert('RGB')
    img = img.resize((224, 224))
    img_array = np.array(img) / 255.0
    img_array = np.expand_dims(img_array, axis=0)

    # User input for patient data
    age = st.number_input("Age", min_value=0, max_value=120, value=30)
    gender = st.selectbox("Gender", ["Male", "Female", "Other"])
    hba1c = st.number_input("HbA1c", min_value=0.0, max_value=20.0, value=5.5, step=0.1)
    duration_dm = st.number_input("Duration of Diabetes Mellitus (years)", min_value=0, max_value=80, value=5)
    type_dm = st.selectbox("Type of Diabetes Mellitus", ["Type 1", "Type 2"])
    eye_side = st.selectbox("Eye Side", ["Left", "Right"])
    ivr_injections = st.number_input("Number of IVR Injections", min_value=0, max_value=50, value=0)
    initial_iop = st.number_input("Initial IOP", min_value=0.0, max_value=50.0, value=15.0, step=0.1)
    initial_logmar = st.number_input("Initial LogMAR", min_value=0.0, max_value=2.0, value=0.0, step=0.01)
    type_dr = st.selectbox("Type of Diabetic Retinopathy", ["Severe NPDR", "PDR", "PDR s/p PRP"])

    if st.button("Analyze Image"):
        # Make prediction
        prediction = model.predict(img_array)
        predicted_class = class_labels[np.argmax(prediction)]
        confidence = float(np.max(prediction))

        # Display the result
        st.subheader(f"Diagnosis: {predicted_class}")
        st.write(f"Confidence: {confidence * 100:.2f}%")

        # Display patient data summary
        st.write("### Patient Data:")
        st.write(f"Age: {age}")
        st.write(f"Gender: {gender}")
        st.write(f"HbA1c: {hba1c}")
        st.write(f"Duration of DM: {duration_dm} years")
        st.write(f"Type of DM: {type_dm}")
        st.write(f"Eye Side: {eye_side}")
        st.write(f"Number of IVR Injections: {ivr_injections}")
        st.write(f"Initial IOP: {initial_iop}")
        st.write(f"Initial LogMAR: {initial_logmar}")
        st.write(f"Type of DR: {type_dr}")

        # Provide a recommendation based on the diagnosis
        st.write("### Recommendation:")
        recommendation = {
            "CNV": "Recommended follow-up with retina specialist for potential anti-VEGF therapy.",
            "DME": "Suggested treatment includes laser photocoagulation or intravitreal injections.",
            "DRUSEN": "Regular monitoring advised. Consider lifestyle modifications and AREDS supplements.",
            "NORMAL": "No immediate action required. Continue regular eye check-ups."
        }.get(predicted_class, "Please consult with an ophthalmologist for personalized advice.")
        st.write(recommendation)