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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)
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