include examples

app.py

@@ -1,9 +1,9 @@
 import gradio as gr
 from transformers import pipeline, AutoTokenizer
 # Load the Hugging Face model
-model_path = "patrixtano/mt5-small-anaphora_czech_6e"
+model_path = "patrixtano/mt5-base-anaphora_czech_6e"
 model_pipeline = pipeline("text2text-generation", model=model_path)
-tokenizer = AutoTokenizer.from_pretrained("patrixtano/mt5-small-anaphora_czech_3e")
+tokenizer = AutoTokenizer.from_pretrained("patrixtano/mt5-base-anaphora_czech_6e")
 def predict(text_input):
     """
     Generate a prediction for the given input text using the Hugging Face model.
@@ -20,14 +20,21 @@ def predict(text_input):
     except Exception as e:
         return f"Error: {str(e)}"
 
+examples = ["Miluji ženu s vařečkou, <ana>která</ana> umí vařit.", "Zřejmě to musel fotit nějaký chatař od 
+sousedství , nebo by to mohl taky fotit můj manžel, ale <ana>on</ana> se obyčejně k aparátu 
+moc neměl.", "Tomáš se domluvil s Jardou, že <ant>ho</ant> odveze na nádraží."]
+
 # Define the Gradio interface
 interface = gr.Interface(
     fn=predict,
     inputs=gr.Textbox(lines=5, label="Input Text"),
     outputs=gr.Textbox(label="Model Output"),
-    title="Hugging Face Model Demo",
-    description="Enter text into the input box, and the model will generate an output based on your input.",
-    theme="default"
+    title="Anaphora resolution demo",
+    description="Enter text into the "Input Text" box, include <ana> </ana> tags around the anaphora which 
+is to be resolved. The model generates a copy of the text with <ant> </ant> tags marking the predicted 
+antecedent",
+    theme="default",
+    examples=examples
 )
 
 # Launch the Gradio app