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app.py

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+import streamlit as st
+import tensorflow as tf
+from tensorflow.keras.applications import EfficientNetV2B0
+from keras.layers import Flatten,Dense,Dropout,GlobalAveragePooling2D
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import load_img
+from tensorflow.keras.preprocessing.image import img_to_array
+from keras.models import Model
+from tensorflow.keras.optimizers import Adam
+import numpy as np
+from huggingface_hub import hf_hub_url, cached_download
+
+img_shape = (224,224,3)
+model = EfficientNetV2B0(include_top = False,input_shape=img_shape)
+flat_1=GlobalAveragePooling2D()(model.output)
+capa_3 = Dense(1,activation='sigmoid')(flat_1)
+model = Model(inputs=model.inputs,outputs = capa_3)
+model.compile(optimizer=Adam(learning_rate=1e-4),loss="BinaryCrossentropy", metrics=["accuracy"])
+#Subir los pesos del modelo
+repo_id = "ferferefer/ACRIMA"
+filename = "ACRIMA.h5"  # o el path a tu SavedModel
+# Obtener la URL y descargar el archivo (temporalmente)
+model_file = cached_download(hf_hub_url(repo_id, filename))
+# Cargar el modelo
+model.load_weights(model_file)
+
+
+st.title('ACRIMA Glaucoma Image Classifier')
+input_image = st.file_uploader('Upload image')
+
+
+if st.button('PREDICT'):
+    
+    predict = load_img(input_image, target_size=img_shape)
+    predict_modified = img_to_array(predict)
+    predict_modified = np.expand_dims(predict_modified, axis=0)
+    result = model.predict(predict_modified)
+    if result < 0.5:
+        probability = 1 - result[0][0]
+        st.write(f"Healthy with {probability}%")
+
+    else:
+        probability = result[0][0]
+        st.write(f"Glaucoma with {probability}%")
+            
+    image1 = load_img(input_image)
+    image1 = img_to_array(image1)
+    image1 = np.array(image1)
+    image1 = image1/255
+    
+    
+
+    st.image(image1, caption='Uploaded Image', use_column_width=True, clamp=True)
+    

requirements.txt

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+streamlit
+tensorflow
+keras
+transformers
+huggingface_hub
+numpy