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import gradio as gr
import json
import zipfile
import pandas as pd
from sentence_transformers import SentenceTransformer
# Importa le funzioni dal tuo script
from scripts.aulsign import AulSign
from scripts.scripts.sign2text_mapping import sign2text
# Configurazione iniziale del modello e dei dati
def load_resources():
zip_file_path = 'tools/corpus_embeddings.json.zip'
output_folder = 'tools/'
with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
zip_ref.extractall(output_folder)
print("File estratto con successo.")
# Modello di embedding
model_name = "mixedbread-ai/mxbai-embed-large-v1"
model = SentenceTransformer(model_name)
# Percorsi dei file
corpus_embeddings_path = 'tools/corpus_embeddings.json'
sentences_train_embeddings_path = 'tools/sentences_train_embeddings_filtered_01.json'
rules_prompt_path_text2sign = 'tools/rules_prompt_text2sign.txt'
rules_prompt_path_sign2text = 'tools/rules_prompt_sign2text.txt'
# Carica dati
with open(corpus_embeddings_path, 'r') as file:
corpus_embeddings = pd.DataFrame(json.load(file))
with open(sentences_train_embeddings_path, 'r') as file:
sentences_train_embeddings = pd.DataFrame(json.load(file))
return model, corpus_embeddings_path, corpus_embeddings, sentences_train_embeddings, rules_prompt_path_text2sign, rules_prompt_path_sign2text
# Funzione per la modalità text2sign
def text_to_sign(sentence_to_analyse):
try:
pseudo_can, fsw_seq, can_desc_association_seq, _ = AulSign(
input=sentence_to_analyse,
rules_prompt_path=rules_prompt_path_text2sign,
train_sentences=sentences_train_embeddings,
vocabulary=corpus_embeddings,
model=model,
ollama=False,
modality="text2sign"
)
return fsw_seq,can_desc_association_seq
except Exception as e:
return f"Error: {str(e)}"
# Funzione per la modalità sign2text
def sign_to_text(fsw_to_analyse):
try:
mapped_input = sign2text(fsw_to_analyse,corpus_embeddings_path)
print(mapped_input)
except Exception as e:
return f"Error on mapping : {str(e)}"
try:
translation = AulSign(
input=mapped_input,
rules_prompt_path=rules_prompt_path_sign2text,
train_sentences=sentences_train_embeddings,
vocabulary=corpus_embeddings,
model=model,
ollama=False,
modality="sign2text"
)
return translation, mapped_input
except Exception as e:
return f"Error on AulSign: {str(e)}"
# Carica le risorse
model, corpus_embeddings_path, corpus_embeddings, sentences_train_embeddings, rules_prompt_path_text2sign, rules_prompt_path_sign2text = load_resources()
# Interfaccia Gradio
with gr.Blocks() as demo:
gr.Markdown("# AulSign Translator")
gr.Markdown("Translate from Natural Language to Formal SignWriting (FSW) or viceversa.")
with gr.Tab("Text to Sign"):
text_input = gr.Textbox(label="Insert a sentence", placeholder="Digit here your sentence...")
fsw_output = gr.Textbox(label="Output")
intermediate_output = gr.Textbox(label="Intermediate Output")
translate_button = gr.Button("Translate")
translate_button.click(text_to_sign, inputs=text_input, outputs=[fsw_output,intermediate_output])
gr.Examples(
examples=["This is a new ASL translator"],
inputs=text_input,
outputs=[fsw_output,intermediate_output],
fn=text_to_sign
)
with gr.Tab("Sign to Text"):
sign_input = gr.Textbox(label="Insert a FSW sequence", placeholder="Digit here your FSW sequence...")
text_output = gr.Textbox(label="Output")
intermediate_output = gr.Textbox(label="Intermediate Output")
reconstruct_button = gr.Button("Translate")
reconstruct_button.click(sign_to_text, inputs=sign_input, outputs=[text_output,intermediate_output])
gr.Examples(
examples=["M518x584S10004492x534S22a04493x569S30a00482x483 AS33b00S19210S20500S26504M519x547S33b00482x482S20500466x512S26504464x532S19210498x511 M530x522S15a36502x510S1813e501x503S2890f470x478 M512x535S1f720492x466S20320497x485S1dc20488x505 M528x595S10009483x405S10021473x422S2e024488x453S10001491x488S10029493x504S15a48477x548S15a40515x548S22a14476x580S22a04515x580"],
inputs=sign_input,
outputs=[text_output,intermediate_output],
fn=sign_to_text
)
# Avvia l'app
if __name__ == "__main__":
demo.launch(share=True)
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