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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) | |