....

requirements.txt

@@ -17,20 +17,14 @@ networkx==2.7.0
 transformers==4.37.2
 sentencepiece==0.1.99
 openai-whisper==20231117
-# tiktoken==0.6.0
 torch==2.2.0
 speechrecognition==3.10.1
 audio_recorder_streamlit==0.0.8
 whisper==1.1.10
 wavio==0.0.8
-# filesplit==4.0.1
 regex==2023.12.25
-# pydot==2.0.0
-# graphviz==0.20.1
 gTTS==2.5.1
 https://files.pythonhosted.org/packages/cc/58/96aff0e5cb8b59c06232ea7e249ed902d04ec89f52636f5be06ceb0855fe/extra_streamlit_components-0.1.60-py3-none-any.whl
 streamlit-option-menu==0.3.12
 deep-translator==1.11.4
-requests==2.27.0
-# asyncio==3.4.3
-# aiohttp==3.9.3
+requests==2.27.0

tabs/intro.py

@@ -1,19 +1,9 @@
 import streamlit as st
 from translate_app import tr
-import pkg_resources
 
 title = "Démosthène"
 sidebar_name = "Introduction"
 
-def print_dependencies():
-    # Récupérer toutes les distributions installées
-    installed_packages = pkg_resources.working_set
-
-    # Imprimer les dépendances avec leur version
-    print("Dépendances avec leur version :")
-    for package in installed_packages:
-        print(f"{package.key}=={package.version}")
-
 def run():
 
     # TODO: choose between one of these GIFs
@@ -21,7 +11,7 @@ def run():
     # st.image("https://dst-studio-template.s3.eu-west-3.amazonaws.com/2.gif")
     # st.image("https://dst-studio-template.s3.eu-west-3.amazonaws.com/3.gif")
     # st.image("assets/tough-communication.gif",use_column_width=True)
-    print_dependencies()
+
     st.write("")
     if st.session_state.Cloud == 0: 
         st.image("assets/miss-honey-glasses-off.gif",use_column_width=True)

tabs/modelisation_seq2seq_tab.py

@@ -4,29 +4,17 @@ import numpy as np
 import os
 from sacrebleu import corpus_bleu
 from transformers import pipeline
-# from translate import Translator
 from deep_translator import GoogleTranslator
 from audio_recorder_streamlit import audio_recorder
 import speech_recognition as sr
 import whisper
 import io
 import wavio
-# from filesplit.merge import Merge
-# import tensorflow as tf
-# import string
-# import re
-# from tensorflow import keras
-# from keras_nlp.layers import TransformerEncoder
-# from tensorflow.keras import layers
-# from tensorflow.keras.utils import plot_model
-# from PIL import Image
 from gtts import gTTS
 from extra_streamlit_components import tab_bar, TabBarItemData
 from translate_app import tr
 import csv
 import requests
-# import asyncio
-# import aiohttp
 # from multiprocessing import Pool
 import concurrent.futures
 import time
@@ -77,37 +65,6 @@ def load_all_data():
 n1 = 0
 df_data_en, df_data_fr, translation_en_fr, translation_fr_en, lang_classifier, model_speech, finetuned_translation_en_fr = load_all_data() 
 
-'''
-def display_translation3(n1, Lang,model_type):
-    global df_data_src, df_data_tgt, placeholder
-    
-    placeholder = st.empty()
-    with st.status(":sunglasses:", expanded=True):
-        s = df_data_src.iloc[n1:n1+5][0].tolist()
-        s_trad = []
-        s_trad_ref = df_data_tgt.iloc[n1:n1+5][0].tolist()
-        source = Lang[:2]
-        target = Lang[-2:]
-        for i in range(3):
-            params = {"lang_tgt": target, "texte": s[i]}
-            if model_type==1:
-                # URL de votre endpoint FastAPI avec les paramètres de requête
-                url = "https://demosthene-or-api-avr23-cds-translation.hf.space/small_vocab/rnn"
-            else:
-                # URL de votre endpoint FastAPI avec les paramètres de requête
-                url = "https://demosthene-or-api-avr23-cds-translation.hf.space/small_vocab/transformer"
-
-            # Envoie d'une requête GET avec les paramètres de requête
-            response = requests.get(url, params=params)
-            s_trad.append(response.json())
-            st.write("**"+source+"   :**  :blue["+ s[i]+"]")
-            st.write("**"+target+"   :**  "+s_trad[-1])
-            st.write("**ref. :** "+s_trad_ref[i])
-            st.write("")
-    with placeholder:
-        st.write("<p style='text-align:center;background-color:red; color:white'>Score Bleu = "+str(int(round(corpus_bleu(s_trad,[s_trad_ref]).score,0)))+"%</p>", \
-            unsafe_allow_html=True)
-'''
 
 def fetch_translation2(url):
     return requests.get(url)
@@ -130,12 +87,12 @@ def display_translation2(n1, Lang, model_type):
             else:
                 url = f"{url_base[i]}/small_vocab/transformer?lang_tgt={target}&texte={s[i]}"
             params.append(url)
+            
         '''
         with Pool(n) as p:
             # Appels parallèles à fetch_translation avec les URLs
             responses = p.map(fetch_translation2, params)
         '''
-        
         with concurrent.futures.ThreadPoolExecutor() as executor:
             # Appels parallèles à fetch_translation avec les URLs
             responses = list(executor.map(fetch_translation2, params))
@@ -157,51 +114,7 @@ def display_translation2(n1, Lang, model_type):
         st.write("<p style='text-align:center;background-color:red; color:white'>Score Bleu = "+str(int(round(score_bleu,0)))+"%</p>", \
             unsafe_allow_html=True)
 
-
-'''
-async def fetch_translation1(url, params):
-    async with aiohttp.ClientSession() as session:
-        async with session.get(url, params=params) as response:
-            return await response.json()
-
-async def display_translation1(n1, Lang, model_type):
-    global df_data_src, df_data_tgt, placeholder
-    
-    placeholder = st.empty()
-    with st.status(":sunglasses:", expanded=True):
-        s = df_data_src.iloc[n1:n1+5][0].tolist()
-        s_trad = []
-        s_trad_ref = df_data_tgt.iloc[n1:n1+5][0].tolist()
-        source = Lang[:2]
-        target = Lang[-2:]
-        
-        # Liste des tâches à exécuter en parallèle
-        tasks = []
-        for i in range(3):
-            params = {"lang_tgt": target, "texte": s[i]}
-            if model_type == 1:
-                url = "https://demosthene-or-api-avr23-cds-translation.hf.space/small_vocab/rnn"
-            else:
-                url = "https://demosthene-or-api-avr23-cds-translation.hf.space/small_vocab/transformer"
-            
-            # Ajout de la tâche à la liste des tâches
-            tasks.append(fetch_translation1(url, params))
-        
-        # Exécution des tâches en parallèle
-        responses = await asyncio.gather(*tasks)
-        
-        for i, response in enumerate(responses):
-            s_trad.append(response)
-            st.write("**"+source+"   :**  :blue["+ s[i]+"]")
-            st.write("**"+target+"   :**  "+s_trad[-1])
-            st.write("**ref. :** "+s_trad_ref[i])
-            st.write("")
-
-    with placeholder:
-        st.write("<p style='text-align:center;background-color:red; color:white'>Score Bleu = "+str(int(round(corpus_bleu(s_trad,[s_trad_ref]).score,0)))+"%</p>", \
-            unsafe_allow_html=True)
-'''
-        
+       
 @st.cache_data        
 def find_lang_label(lang_sel):
     global lang_tgt, label_lang
@@ -385,7 +298,6 @@ def run():
         with col2:
             st.write(":red[**Trad. Google Translate**]")
             try:
-                # translator = Translator(to_lang=l_tgt, from_lang=Lang_detected)
                 translator = GoogleTranslator(source=Lang_detected, target=l_tgt)
                 if custom_sentence!="":
                     translation = translator.translate(custom_sentence)