Create app.py

app.py

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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)