Update app.py

app.py

@@ -55,13 +55,13 @@ class_labels = ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash']
 # Function to predict image label and score
 def predict_image(input):
     # Resize the image to the size expected by the model and convert to numpy array
-    image_array = tf.keras.preprocessing.image.img_to_array(input.resize((224, 244)))
+    image_array = tf.keras.preprocessing.image.img_to_array(input.resize((244, 224)))  # Cambiar el orden de las dimensiones
     # Normalize the image
     image_array = tf.keras.applications.efficientnet.preprocess_input(image_array)
     # Expand the dimensions to create a batch
     image_array = tf.expand_dims(image_array, 0)
     # Predict using the model
-    predictions = model_tl.predict(image_array)
+    predictions = model1.predict(image_array)
     class_labels = ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash']
     category_scores = {}
     for i, class_label in enumerate(class_labels):
@@ -70,7 +70,6 @@ def predict_image(input):
     return category_scores
 
 
-
 loader = WebBaseLoader(["https://www.epa.gov/recycle/frequent-questions-recycling", "https://www.whitehorsedc.gov.uk/vale-of-white-horse-district-council/recycling-rubbish-and-waste/lets-get-real-about-recycling/", "https://www.teimas.com/blog/13-preguntas-y-respuestas-sobre-la-ley-de-residuos-07-2022", "https://www.molok.com/es/blog/gestion-de-residuos-solidos-urbanos-rsu-10-dudas-comunes"])
 data=loader.load()
 # split documents