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app.py

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+##subprocess.run(["pip", "install", "--force-reinstall", "protobuf==3.19.6"])
+import os
+os.environ["TF_USE_LEGACY_KERAS"] = "1"
+
+import os
+os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
+
+import os
+import gdown
+import cv2
+import numpy as np
+import tensorflow as tf
+import streamlit as st
+from PIL import Image
+
+IMG_SIZE = (224, 224)
+NUM_CLASSES = 8
+
+from keras_cv_attention_models.coatnet import CoAtNet0
+
+@st.cache_resource
+def load_model():
+    import keras
+    model_path = "model.keras"
+    saved_model_path = "model_saved"
+    if not os.path.exists(model_path):
+        st.info("📥 Downloading model from Google Drive...")
+        url = "https://drive.google.com/uc?id=1Gm2O77uWSUnajL0iFlFJtVk_UEN_wrTN"
+        gdown.download(url, model_path, quiet=False, fuzzy=True)
+    if os.path.getsize(model_path) < 1_000_000:
+        raise ValueError("❌ Downloaded model is too small. Likely failed or incomplete download!")
+    if not os.path.exists(saved_model_path):
+        try:
+            model = keras.models.load_model(
+                model_path,
+                compile=False,
+                custom_objects={
+                    "CoAtNet0": CoAtNet0,
+                    "Functional": tf.keras.models.Model,
+                    "gelu": tf.keras.activations.gelu
+                }
+            )
+            model.save(saved_model_path, save_format="tf")
+            st.success("✅ Converted `.keras` model to SavedModel format.")
+        except Exception as e:
+            st.error(f"❌ Failed to convert .keras model: {e}")
+            raise
+    return tf.keras.models.load_model(saved_model_path, compile=False)
+
+model = load_model()
+
+def crop_circle(img):
+    h, w = img.shape[:2]
+    center = (w // 2, h // 2)
+    radius = min(center[0], center[1])
+    Y, X = np.ogrid[:h, :w]
+    dist = np.sqrt((X - center[0]) ** 2 + (Y - center[1]) ** 2)
+    mask = dist <= radius
+    if img.ndim == 3:
+        mask = np.stack([mask] * 3, axis=-1)
+    img[~mask] = 0
+    return img
+
+def apply_clahe(img):
+    lab = cv2.cvtColor(img, cv2.COLOR_RGB2LAB)
+    l, a, b = cv2.split(lab)
+    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+    cl = clahe.apply(l)
+    merged = cv2.merge((cl, a, b))
+    return cv2.cvtColor(merged, cv2.COLOR_LAB2RGB)
+
+def sharpen_image(img, sigma=10):
+    blur = cv2.GaussianBlur(img, (0, 0), sigma)
+    return cv2.addWeighted(img, 4, blur, -4, 128)
+
+def resize_normalize(img):
+    img = cv2.resize(img, IMG_SIZE)
+    img = img / 255.0
+    return img
+
+def preprocess_image(img):
+    img = crop_circle(img)
+    img = apply_clahe(img)
+    img = sharpen_image(img)
+    img = resize_normalize(img)
+    return img
+
+CLASS_NAMES = ['Normal', 'Diabetes', 'Glaucoma', 'Cataract', 'AMD', 'Hypertension', 'Myopia', 'Others']
+
+st.set_page_config(page_title="🧠 Retina Disease Classifier", layout="centered")
+
+st.title("🧠 Retina Disease Classifier")
+st.markdown("Upload a retinal image and get predicted disease class using CoAtNet model.")
+
+uploaded_file = st.file_uploader("📤 Upload Image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+    bgr_img = cv2.imdecode(file_bytes, 1)
+    rgb_img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2RGB)
+    st.image(rgb_img, caption="Original Image", use_column_width=True)
+    preprocessed = preprocess_image(rgb_img)
+    input_tensor = np.expand_dims(preprocessed, axis=0)
+    preds = model.predict(input_tensor)
+    pred_idx = np.argmax(preds)
+    pred_label = CLASS_NAMES[pred_idx]
+    confidence = np.max(preds) * 100
+    st.success(f"✅ **Prediction:** `{pred_label}`")
+    st.info(f"🔍 Confidence: **{confidence:.2f}%**")
+    st.subheader("🧪 Preprocessed Input")
+    st.image((preprocessed * 255).astype(np.uint8), caption="Model Input", use_column_width=True)

requirements.txt

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+tensorflow-cpu>=2.13
+keras>=3.0.0
+protobuf>=3.20,<5
+streamlit==1.46.1
+gdown
+opencv-python-headless
+Pillow
+scikit-image
+lime
+matplotlib
+numpy==1.23.5
+keras-cv-attention-models