Update pages/Entorno de Ejecución.py

pages/Entorno de Ejecución.py

@@ -177,45 +177,45 @@ with vit:
             elif uploaded_file is not None:
                 with st.spinner('Cargando predicción...'):
 
-                    y_gorritoo = query(uploaded_file.read(), model_dict[model_choice[0]]) 
-                    st.write(y_gorritoo)
-                    #classifiers = [pipeline("image-classification", model= model_dict[model_choice[i]]) for i in range(len(model_choice))]
+                    #y_gorritoo = query(uploaded_file.read(), model_dict[model_choice[0]]) 
+                    #st.write(y_gorritoo)
+                    classifiers = [pipeline("image-classification", model= model_dict[model_choice[i]]) for i in range(len(model_choice))]
                     
                     #classifier = pipeline("image-classification", model= model_dict[model_choice[0]])
                     img = preprocess(uploaded_file, module = 'pil')
 
                     models = [model_dict[model] for model in model_choice]
                     #st.write(models)
-                    #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)
 
                     #models = [model_dict[i] for i in range(len(model_choice))]
                     #st.write(type(models), models)
                     
                     #st.write(model_choice)
 
-                    y_gorrito = 0
+                    #y_gorrito = 0
                     #y_gorrito = query(uploaded_file.read(), model_choice[0])[1]["score"] 
-                    i = -1
-                    st.write("loop iniciado")
-                    for model in models:
-                        i+=1
-                        st.write("y gorrito a cargar")
-                        a =  query(uploaded_file.read(), model)
-                        if a == -1:
-                            st.write("Los servidores se encuentrar caídos, intente más tarde")
-                            break
-                        st.write("query terminado")
-                        y_gorrito += a
-                        st.write("y gorrito cargado")
-                    y_gorrito /= i
-                    st.write("loop terminado")
+                    #i = -1
+                    #st.write("loop iniciado")
+                    #for model in models:
+                    #    i+=1
+                    #    st.write("y gorrito a cargar")
+                    #    a =  query(uploaded_file.read(), model)
+                    #    if a == -1:
+                    #        st.write("Los servidores se encuentrar caídos, intente más tarde")
+                    #        break
+                    #    st.write("query terminado")
+                    #    y_gorrito += a
+                    #    st.write("y gorrito cargado")
+                    #y_gorrito /= i
+                    #st.write("loop terminado")
     
                     #st.write("y gorrito calculado", len(model_choice))
                     #classifier = classifier(img) 
@@ -226,7 +226,7 @@ with vit:
                 
                 #y_gorrito = classifier[0]["score"]
                     
-                    #y_gorrito = vit_ensemble(classifiers, img)
+                    y_gorrito = vit_ensemble(classifiers, img)
                     #
                     if round(float(y_gorrito * 100)) >= threshold:
                         st.success("¡Patacón Detectado!")