Delete app.py

app.py

@@ -1,51 +0,0 @@
-import streamlit as st
-import tensorflow as tf
-from tf.keras.applications import EfficientNetV2B0
-from keras.layers import Flatten,Dense,Dropout,GlobalAveragePooling2D
-from tf.keras.models import load_model
-from tf.keras.preprocessing.image import load_img
-from tf.keras.preprocessing.image import img_to_array
-from keras.models import Model
-from transformers import pipeline
-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=tf.keras.optimizers.Adam(learning_rate=1e-4),loss="BinaryCrossentropy", metrics=["accuracy"])
-#Subir los pesos del modelo
-repo_id = "ferferefer/RIM_ONE_Glaucoma"
-filename = "vgg_rim_checkpoint.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('RIM_ONE 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]
-        print(f"Healthy with {probability}%")
-
-    else:
-        probability = result[0][0]
-        print(f"Glaucoma with {probability}%")
-            
-    image1 = load_img(input_image)
-    image1 = img_to_array(image1)
-    image1 = np.array(image1)
-    
-
-    st.image(image1, width=500)
-