Update pages/Entorno de Ejecución.py

pages/Entorno de Ejecución.py

@@ -2,8 +2,7 @@ import streamlit as st
 import tensorflow as tf
 from tensorflow.keras.models import load_model
 from transformers import pipeline
-from PIL import Image
-
+from PIL import Image 
 import os
 import cv2
 import numpy as np
@@ -77,7 +76,8 @@ with cnn:
             if module == 'cv2':
                 img = cv2.imdecode(img, cv2.IMREAD_COLOR)
             elif module == 'pil':
-                img = Image.open(file_uploader.read())
+                img = Image.frombuffer(data = uploaded_file.read())
+                #img = Image.open(io.BytesIO(file_uploader.read()))
             return img
                 
         # Set the image dimensions
@@ -86,7 +86,7 @@ with cnn:
         executed = False
         
     with col_b:
-        
+             
         uploaded_file = st.file_uploader(key = 'convnet_upload', label = 'Sube la imagen a clasificar',type= ['jpg','png', 'jpeg', 'jfif', 'webp', 'heic'])
         
         if st.button(key = 'convnet_button', label ='¿Hay un patacón en la imagen?'):
@@ -96,7 +96,7 @@ with cnn:
             elif uploaded_file is not None:
                 img = preprocess(uploaded_file)
                 if ultra_flag:
-                    with st.spinner('Cargando ultra-predicción...'):
+                    with st.spinner('Cargando ultra-predicción...', key = 'convspinner'):
                         if not executed: 
                             ultraptctrn = [load_model(model_dict[model]) for model in ultraversions]
                             executed = True
@@ -132,7 +132,7 @@ with iforest:
 with gan:
     st.write('Próximamente')
 with vit:
-
+      
     col_a, col_b = st.columns(2)
 
     with col_a:
@@ -141,6 +141,7 @@ with vit:
         uploaded_file = st.file_uploader(key = 'ViT_upload', label = 'Sube la imagen a clasificar',type= ['jpg','png', 'jpeg', 'jfif', 'webp', 'heic'])
         if st.button(key = 'ViT_button', label ='¿Hay un patacón en la imagen?'):
             if uploaded_file is not None:
+                with st.spinner('Cargando predicción...', key = 'vitspinner')
                 classifier = pipeline("image-classification", model="frncscp/patacoptimus-prime")
                 img = preprocess(uploaded_file, module = 'pil')
                 raw_img = img