import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import pandas as pd
from fpdf import FPDF
# Interface utilisateur
st.set_page_config(
page_title="Traduction d'une phrase en pictogrammes ARASAAC",
page_icon="📝",
layout="wide"
)
# Charger le modèle et le tokenizer
# checkpoint = "Propicto/t2p-t5-large-orfeo"
checkpoint = "Propicto/t2p-nllb-200-distilled-600M-all"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
# Lire le lexique
@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(' ', '_')
return df
lexicon = read_lexicon("lexicon.csv")
# Processus de sortie de la traduction
def process_output_trad(pred):
return pred.split()
def get_id_picto_from_predicted_lemma(df_lexicon, lemma):
if lemma.endswith("!"):
lemma = lemma[:-1]
id_picto = df_lexicon.loc[df_lexicon['keyword_no_cat'] == lemma, 'id_picto'].tolist()
return (id_picto[0], lemma) if id_picto else (0, lemma)
# Génération du contenu HTML pour afficher les pictogrammes
def generate_html(ids):
html_content = '<html><head><style>'
html_content += '''
figure {
display: inline-block;
text-align: center;
font-family: Arial, sans-serif;
margin: 0;
}
figcaption {
color: black;
background-color: white;
border-radius: 5px;
}
img {
background-color: white;
margin: 0;
padding: 0;
border-radius: 6px;
}
'''
html_content += '</style></head><body>'
for picto_id, lemma in ids:
if picto_id != 0: # ignore invalid IDs
img_url = f"https://static.arasaac.org/pictograms/{picto_id}/{picto_id}_500.png"
html_content += f'''
<figure>
<img src="{img_url}" alt="{lemma}" width="100" height="100"/>
<figcaption>{lemma}</figcaption>
</figure>
'''
html_content += '</body></html>'
return html_content
def generate_pdf(ids):
pdf = FPDF(orientation='L', unit='mm', format='A4') # 'L' for landscape orientation
pdf.add_page()
pdf.set_auto_page_break(auto=True, margin=15)
# Start positions
x_start = 10
y_start = 10
img_width = 50
img_height = 50
spacing = 1
max_width = 297 # A4 landscape width in mm
current_x = x_start
current_y = y_start
for picto_id, lemma in ids:
if picto_id != 0: # ignore invalid IDs
img_url = f"https://static.arasaac.org/pictograms/{picto_id}/{picto_id}_500.png"
pdf.image(img_url, x=current_x, y=current_y, w=img_width, h=img_height)
pdf.set_xy(current_x, current_y + img_height + 5)
pdf.set_font("Arial", size=12)
pdf.cell(img_width, 10, txt=lemma, ln=1, align='C')
current_x += img_width + spacing
# Move to the next line if exceeds max width
if current_x + img_width > max_width:
current_x = x_start
current_y += img_height + spacing + 10 # Adjust for image height and some spacing
pdf_path = "pictograms.pdf"
pdf.output(pdf_path)
return pdf_path
st.title("Traduction d'une phrase en pictogrammes ARASAAC")
st.info("Text-to-Pictograms traduit une phrase en français en pictogrammes ARASAAC. Renseignez une phrase, puis validez. Vous pouvez sauvegarder la traduction au format PDF en cliquant sur le bouton en bas de page.", icon='ℹ️')
pictogram_ids = []
sentence = st.text_input("Entrez une phrase en français:")
if sentence:
with st.spinner("Affichage des pictogrammes..."):
inputs = tokenizer(sentence, return_tensors="pt").input_ids
outputs = model.generate(inputs, max_new_tokens=40, do_sample=True, top_k=30, top_p=0.95)
pred = tokenizer.decode(outputs[0], skip_special_tokens=True)
sentence_to_map = process_output_trad(pred)
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=200, scrolling=True)
if pictogram_ids:
# Container to hold the download button
pdf_path = generate_pdf(pictogram_ids)
with open(pdf_path, "rb") as pdf_file:
st.download_button(label="Télécharger la traduction en PDF", data=pdf_file, file_name="pictograms.pdf", mime="application/pdf")