Update pages/Entorno de Ejecución.py

pages/Entorno de Ejecución.py

@@ -1,7 +1,7 @@
 import streamlit as st
 import tensorflow as tf
 from tensorflow.keras.models import load_model
-from transformers import AutoConfig, AutoModel, pipeline, AutoProcessor, AutoModelForZeroShotImageClassification
+from transformers import AutoConfig, AutoModel, pipeline#, AutoProcessor, AutoModelForZeroShotImageClassification
 from PIL import Image 
 import os
 import cv2
@@ -112,7 +112,7 @@ with cnn:
         
     with col_b:
              
-        uploaded_file = st.file_uploader(key = 'convnet_upload', label = 'Sube la imagen a clasificar',type= ['jpg','png', 'jpeg', 'jfif', 'webp', 'heic'])
+        uploaded_file = st.file_uploader(key = 'conv_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?'):
             if len(model_choice) < 1:
@@ -184,14 +184,14 @@ with vit:
                     #img = preprocess(uploaded_file, module = 'pil')
 
 
-                    def vit_ensemble(classifier_list, img):
-                        y_gorrito = 0
-                        for classifier in classifier_list:
-                            classifier = classifier(img)                            
-                            for clase in classifier:
-                                if clase['label'] == 'Patacon-True':
-                                    y_gorrito += clase["score"]
-                        return y_gorrito / len(classifier_list)
+                    #def vit_ensemble(classifier_list, img):
+                    #   y_gorrito = 0
+                    #    for classifier in classifier_list:
+                    #        classifier = classifier(img)                            
+                    #        for clase in classifier:
+                    #            if clase['label'] == 'Patacon-True':
+                    #                y_gorrito += clase["score"]
+                    #    return y_gorrito / len(classifier_list)
 
 
                     y_gorrito = 0
@@ -200,7 +200,8 @@ with vit:
                         api_result = query(uploaded_file.read(), model_dict[model]) 
                         y_gorrito += api_result[1]["score"]                   
                     y_gorrito /= len(model_choice)
-                        
+
+                    st.write("y gorrito calculado")
                     #classifier = classifier(img) 
 
                     #for clase in classifier: