Delete coatnet_retina_app/app.py

coatnet_retina_app/app.py

@@ -1,38 +0,0 @@
-
-import streamlit as st
-import tensorflow as tf
-import numpy as np
-from PIL import Image
-import os
-
-from utils.gradcam import generate_gradcam
-from utils.lime_explainer import explain_with_lime
-from preprocessing.preprocess import preprocess_image
-
-@st.cache_resource
-def load_model():
-    return tf.keras.models.load_model("model.keras")
-
-model = load_model()
-
-class_names = ["Normal", "Diabetes", "Glaucoma", "Cataract", "AMD", "Hypertension", "Myopia", "Others"]
-
-st.title("🧠 Retinal Disease Classifier (CoAtNet)")
-uploaded_file = st.file_uploader("Upload a retinal image", type=["jpg", "png", "jpeg"])
-
-if uploaded_file:
-    image = Image.open(uploaded_file).convert("RGB")
-    st.image(image, caption="Input Image", use_column_width=True)
-
-    img_array = preprocess_image(image)
-    pred = model.predict(np.expand_dims(img_array, axis=0))
-    predicted_class = class_names[np.argmax(pred)]
-    st.success(f"✅ Predicted Disease: **{predicted_class}**")
-
-    st.subheader("🔍 Grad-CAM Explanation")
-    gradcam = generate_gradcam(model, img_array)
-    st.image(gradcam, caption="Grad-CAM", use_column_width=True)
-
-    st.subheader("🔍 LIME Explanation")
-    lime_expl = explain_with_lime(model, img_array)
-    st.image(lime_expl, caption="LIME Explanation", use_column_width=True)