Update app.py

app.py

@@ -47,20 +47,8 @@ id2label = config["id2label"]
 label2id = config["label2id"]
 image_size = config["image_size"]
 classes_names = list(label2id.keys())
-'''
-# import labels
-classes_names = ["Acropore_branched", "Acropore_digitised", "Acropore_tabular", "Algae_assembly", 
-               "Algae_limestone", "Algae_sodding", "Dead_coral", "Fish", "Human_object",
-               "Living_coral", "Millepore", "No_acropore_encrusting", "No_acropore_massive",
-              "No_acropore_sub_massive",  "Rock", "Sand",
-               "Scrap", "Sea_cucumber", "Syringodium_isoetifolium",
-               "Thalassodendron_ciliatum",  "Useless"]
-
-classes_nb = list(np.arange(len(classes_names)))
-id2label = {int(classes_nb[i]): classes_names[i] for i in range(len(classes_nb))}
-label2id = {v: k for k, v in id2label.items()}
-'''
 
+# PREDICTIONS
 def sigmoid(_outputs):
     return 1.0 / (1.0 + np.exp(-_outputs))
     
@@ -85,11 +73,11 @@ def predict(input_image):
 # Define style
 title = "DinoVd'eau image classification"
 model_link = "https://huggingface.co/" + checkpoint_name
-description = f"This is a prototype application that demonstrates how artificial intelligence-based systems can recognize what object(s) is present in an underwater image. To use it, simply upload your image, or click one of the example images to load them. For predictions, we use the [open-source model]({model_link})"
+description = f"This application showcases the capability of artificial intelligence-based systems to identify objects within underwater images. To utilize it, you can either upload your own image or select one of the provided examples for analysis. For predictions, we use this [open-source model]({model_link})"
 
 gr.Interface(
     fn=predict,
-    inputs=gr.Image(shape=(224, 224)),
+    inputs=gr.Image(shape=(512, 512)),
     outputs="label",
     title=title, 
     description=description,