Update app.py

app.py

@@ -3,12 +3,12 @@ from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 import pandas as pd
 
 # Charger le modèle et le tokenizer
-checkpoint = "Propicto/t2p-t5-large-orfeo"
+checkpoint = "your-model-checkpoint"
 tokenizer = AutoTokenizer.from_pretrained(checkpoint)
 model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
 
 # Lire le lexique
-@st.cache
+@st.cache_data
 def read_lexicon(lexicon):
     df = pd.read_csv(lexicon, sep='\t')
     df['keyword_no_cat'] = df['lemma'].str.split(' #').str[0].str.strip().str.replace(' ', '_')
@@ -52,4 +52,4 @@ if sentence:
     pictogram_ids = [get_id_picto_from_predicted_lemma(lexicon, lemma) for lemma in sentence_to_map]
 
     html = generate_html(pictogram_ids)
-    st.components.v1.html(html, height=600, scrolling=True)
+    st.components.v1.html(html, height=600, scrolling=True)