import streamlit as st # type: ignore import os from datetime import datetime from extra_streamlit_components import tab_bar, TabBarItemData import io from gtts import gTTS import soundfile as sf import wavio from audio_recorder_streamlit import audio_recorder import speech_recognition as sr import whisper import numpy as np from translate_app import tr import getpass from langchain_mistralai import ChatMistralAI from langchain_openai import ChatOpenAI from langgraph.checkpoint.memory import MemorySaver from langgraph.graph import START, END, MessagesState, StateGraph from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder from typing import Sequence from langchain_core.messages import BaseMessage, SystemMessage, HumanMessage, AIMessage, trim_messages from langgraph.graph.message import add_messages from typing_extensions import Annotated, TypedDict from dotenv import load_dotenv import time from tabs.google_drive_read_preprompt import read_param, format_param import warnings warnings.filterwarnings('ignore') title = "Sales coaching" sidebar_name = "Sales coaching" dataPath = st.session_state.DataPath os.environ["LANGCHAIN_TRACING_V2"] = "true" os.environ["LANGCHAIN_ENDPOINT"]="https://api.smith.langchain.com" os.environ["LANGCHAIN_HUB_API_URL"]="https://api.smith.langchain.com" os.environ["LANGCHAIN_PROJECT"] = "Sales Coaching Chatbot" if st.session_state.Cloud != 0: load_dotenv() os.getenv("LANGCHAIN_API_KEY") os.getenv("MISTRAL_API_KEY") os.getenv("OPENAI_API_KEY") prompt = ChatPromptTemplate.from_messages( [ ( "system", "Répond à toutes les questions du mieux possible dans la langue {language}, même si la question est posée dans une autre langue", ), MessagesPlaceholder(variable_name="messages"), ] ) class State(TypedDict): messages: Annotated[Sequence[BaseMessage], add_messages] language: str def call_model(state: State): chain = prompt | model response = chain.invoke(state) return {"messages": [response]} # Define a new graph workflow = StateGraph(state_schema=State) # Define the (single) node in the graph workflow.add_edge(START, "model") workflow.add_node("model", call_model) workflow.add_edge("model", END) # Add memory memory = MemorySaver() app = workflow.compile(checkpointer=memory) selected_index1 = 0 selected_index2 = 0 selected_index3 = 0 selected_indices4 = [] selected_indices5 = [] selected_indices6 = [] selected_indices7 = [] selected_options4 = [] selected_options5 = [] selected_options6 = [] selected_options7 = [] selected_index8 = 0 context="" human_message1="" thread_id ="" virulence = 1 question = [] thread_id = datetime.now().strftime("%Y-%m-%d %H:%M:%S") config = {"configurable": {"thread_id": thread_id}} to_init = True initialized = False messages = [ SystemMessage(content=""), HumanMessage(content=""), AIMessage(content=""), HumanMessage(content="") ] if 'model' in st.session_state: model = st.session_state.model used_model = st.session_state.model def init_run(): global initialized, to_init, thread_id, config, app, context, human_message1, model, used_model, messages initialized = True to_init = False thread_id = datetime.now().strftime("%Y-%m-%d %H:%M:%S") config = {"configurable": {"thread_id": thread_id}} app.invoke( {"messages": messages, "language": language}, config, ) st.session_state.thread_id = thread_id st.session_state.config = config st.session_state.messages_init = messages st.session_state.context = context st.session_state.human_message1 = human_message1 st.session_state.messages = [] if 'model' in st.session_state and (st.session_state.model[:3]=="gpt") and ("OPENAI_API_KEY" in st.session_state): model = ChatOpenAI(model=st.session_state.model, temperature=0.8, # Adjust creativity level max_tokens=150 # Define max output token limit ) else: model = ChatMistralAI(model=st.session_state.model) if 'model' in st.session_state: used_model=st.session_state.model return def init(): global config,thread_id, context,human_message1,ai_message1,language, app, model_speech,prompt,model,question, to_init, initialized global selected_index1, selected_index2, selected_index3, selected_indices4,selected_indices5,selected_indices6,selected_indices7 global selected_options4,selected_options5,selected_options6,selected_options7, selected_index8, virulence, used_model, messages model_speech = whisper.load_model("base") if (st.button(label=tr("Nouvelle conversation"), type="primary")): selected_index1 = 0 selected_index2 = 0 selected_index3 = 0 selected_indices4 = [] selected_indices5 = [] selected_indices6 = [] selected_indices7 = [] selected_options4 = [] selected_options5 = [] selected_options6 = [] selected_options7 = [] selected_index8 = 0 context = "" human_message1="" thread_id ="" virulence = 1 if 'model' in st.session_state and (st.session_state.model[:3]=="gpt") and ("OPENAI_API_KEY" in st.session_state): model = ChatOpenAI(model=st.session_state.model, temperature=0.8, # Adjust creativity level max_tokens=150 # Define max output token limit ) else: model = ChatMistralAI(model=st.session_state.model) if 'model' in st.session_state: used_model=st.session_state.model label, question, options = format_param() translated_options1 = [tr(o) for o in options[0]] selected_option1 = st.selectbox(tr(label[0]),translated_options1, index = selected_index1) # index=int(var1_init)) selected_index1 = translated_options1.index(selected_option1) translated_options2 = [tr(o) for o in options[1]] selected_option2 = st.selectbox(tr(label[1]),translated_options2, index = selected_index2) # index=int(var2_init)) selected_index2 = translated_options2.index(selected_option2) translated_options3 = [tr(o) for o in options[2]] selected_option3 = st.selectbox(tr(label[2]),translated_options3, index=selected_index3) #index=int(var3_init)) selected_index3 = translated_options3.index(selected_option3) context = tr(f"""Tu es un {options[0][selected_index1]}, d'une {options[1][selected_index2]}. Cette entreprise propose des {options[2][selected_index3]}. """) context = st.text_area(label=tr("Résumé du Contexte (modifiable):"), value=context) st.markdown(''' ------------------------------------------------------------------------------------ ''') translated_options4 = [tr(o) for o in options[3]] selected_options4 = st.multiselect(tr(label[3]),translated_options4, default=[translated_options4[o] for o in selected_indices4]) selected_indices4 = [translated_options4.index(o) for o in selected_options4] problematique = selected_options4 if problematique != []: markdown_text4 = """\n"""+tr(question[3]) markdown_text4 = markdown_text4+"".join(f"\n- {o}" for o in problematique) st.write(markdown_text4) else: markdown_text4 = "" translated_options5 = [tr(o) for o in options[4]] selected_options5 = st.multiselect(tr(label[4]),translated_options5, default=[translated_options5[o] for o in selected_indices5]) selected_indices5 = [translated_options5.index(o) for o in selected_options5] processus = selected_options5 if processus != []: markdown_text5 = """\n\n"""+tr(question[4]) markdown_text5 = markdown_text5+"".join(f"\n- {o}" for o in processus) st.write(markdown_text5) else: markdown_text5 = "" translated_options6 = [tr(o) for o in options[5]] selected_options6 = st.multiselect(tr(label[5]),translated_options6, default=[translated_options6[o] for o in selected_indices6]) selected_indices6 = [translated_options6.index(o) for o in selected_options6] objectifs = selected_options6 if objectifs != []: markdown_text6 = """\n\n"""+tr(question[5]) markdown_text6 = markdown_text6+"".join(f"\n- {o}" for o in objectifs) st.write(markdown_text6) else: markdown_text6 = "" translated_options7 = [tr(o) for o in options[6]] selected_options7 = st.multiselect(tr(label[6]),translated_options7, default=[translated_options7[o] for o in selected_indices7]) selected_indices7 = [translated_options7.index(o) for o in selected_options7] solutions_utilisees = selected_options7 if solutions_utilisees != []: markdown_text7 = """\n\n"""+tr(question[6]) markdown_text7 = markdown_text7+"".join(f"\n- {o}" for o in solutions_utilisees) st.write(markdown_text7) st.write("") else: markdown_text7 = "" translated_options8 = [tr(o) for o in options[7]] selected_option8 = st.selectbox(tr(label[7]),translated_options8, index = selected_index8) selected_index8 = translated_options8.index(selected_option8) markdown_text8 = """\n\n"""+tr(question[7])+"""\n"""+(f"""{translated_options8[selected_index8]}""") col1, col2, col3 = st.columns(3) with col1: virulence = st.slider(tr("Virulence (choisissez une valeur entre 1 et 5)"), min_value=1, max_value=5, step=1,value=virulence) markdown_text9 = """\n\n"""+tr(f"""Le prospect est très occupé et n'aime pas être dérangé inutilement. Tu vas utiliser une échelle de 1 à 5 d'agressivité du prospect à l'égard du vendeur. Pour cette simulation utilise le niveau {virulence}.""") human_message1 = tr("""Je souhaite que nous ayons une conversation verbale entre moi le vendeur, et toi que je prospecte. Mon entreprise propose une solution logicielle pour gérer la proposition de valeur d’entreprise B2B qui commercialise des solutions technologiques. """)+markdown_text4+markdown_text5+markdown_text6+markdown_text7+markdown_text8+markdown_text9+tr(f""" Je suis le vendeur. Répond à mes questions en tant que {options[0][selected_index1]}, connaissant mal le concept de proposition de valeur, et mon équipe de vente n'est pas performante. Attention: Ce n'est pas toi qui m'aide, c'est moi qui t'aide avec ma solution. Attention: Si le vendeur aborde des points qui ne concerne pas cette simulation, lui répondre que c'est hors contexte. Es tu prêt à commencer ? """) human_message1 = st.text_area(label=tr("Consigne"), value=tr(human_message1),height=300) st.markdown(''' ------------------------------------------------------------------------------------ ''') ai_message1 = tr(f"J'ai bien compris, je suis un {options[0][selected_index1]} prospecté et je réponds seulement à tes questions. Je réponds à une seule question à la fois, sans commencer mes réponses par 'En tant que {options[0][selected_index1]}'.") # ai_message1 = st.text_area(label=tr("Réponse du prospect"), value=ai_message1) messages = [ SystemMessage(content=context), HumanMessage(content=human_message1), # AIMessage(content=ai_message1), # HumanMessage(content=tr("Commençons la conversation. Attention, je suis le vendeur et je parle le premier. Tu es le propect.")) ] st.write("") if ("context" in st.session_state) and ("human_message1" in st.session_state): if (st.session_state.context != context) or (st.session_state.human_message1 != human_message1 ) or (used_model != st.session_state.model) or (thread_id==""): to_init = True else: to_init = False else: to_init = True if to_init: if st.button(label=tr("Validez"), on_click=init_run,type="primary"): initialized=True else: initialized = False st.write("**thread_id:** "+thread_id) return config, thread_id, messages # Fonction pour générer et jouer le texte en speech def play_audio(custom_sentence, Lang_target, speed=1.0): # Générer le speech avec gTTS audio_stream_bytesio_src = io.BytesIO() tts = gTTS(custom_sentence, lang=Lang_target) # Revenir au début du flux audio audio_stream_bytesio_src.seek(0) audio_stream_bytesio_src.truncate(0) tts.write_to_fp(audio_stream_bytesio_src) audio_stream_bytesio_src.seek(0) # Charger l'audio dans un tableau numpy data, samplerate = sf.read(audio_stream_bytesio_src) # Modifier la vitesse de lecture en ajustant le taux d'échantillonnage new_samplerate = int(samplerate * speed) new_audio_stream_bytesio = io.BytesIO() # Enregistrer l'audio avec la nouvelle fréquence d'échantillonnage sf.write(new_audio_stream_bytesio, data, new_samplerate, format='wav') new_audio_stream_bytesio.seek(0) # Lire l'audio dans Streamlit # time.sleep(2) st.audio(new_audio_stream_bytesio, start_time=0, autoplay=True) def run(): global thread_id, config, model_speech, language,prompt,model, model_name, question, to_init, initialized, messages st.write("") st.write("") st.title(tr(title)) if 'language_label' in st.session_state: language = st.session_state['language_label'] else: language = "French" chosen_id = tab_bar(data=[ TabBarItemData(id="tab1", title=tr("Initialisation"), description=tr("d'une nouvelle conversation")), TabBarItemData(id="tab2", title=tr("Conversation"), description=tr("avec le prospect")), TabBarItemData(id="tab3", title=tr("Evaluation"), description=tr("de l'acte de vente"))], default="tab1") if (chosen_id == "tab1"): if 'model' in st.session_state and (st.session_state.model[:3]=="gpt") and ("OPENAI_API_KEY" in st.session_state): model = ChatOpenAI(model=st.session_state.model, temperature=0.8, # Adjust creativity level max_tokens=150 # Define max output token limit ) else: model = ChatMistralAI(model=st.session_state.model) config,thread_id, messages = init() query = "" elif (chosen_id == "tab2"): try: if to_init and not initialized: init_run() except NameError: config,thread_id, messages = init() with st.container(): # Diviser l'écran en deux colonnes col1, col2 = st.columns(2) with col1: st.write("**thread_id:** "+thread_id) query = "" audio_bytes = audio_recorder (pause_threshold=2.0, sample_rate=16000, auto_start=False, text=tr("Cliquez pour parler, puis attendre 2sec."), \ recording_color="#e8b62c", neutral_color="#1ec3bc", icon_size="6x",) if audio_bytes: # st.write("**"+tr("Vendeur")+" :**\n") # Fonction pour générer et jouer le texte en speech st.audio(audio_bytes, format="audio/wav", autoplay=False) try: detection = False if detection: # Create a BytesIO object from the audio stream audio_stream_bytesio = io.BytesIO(audio_bytes) # Read the WAV stream using wavio wav = wavio.read(audio_stream_bytesio) # Extract the audio data from the wavio.Wav object audio_data = wav.data # Convert the audio data to a NumPy array audio_input = np.array(audio_data, dtype=np.float32) audio_input = np.mean(audio_input, axis=1)/32768 result = model_speech.transcribe(audio_input) Lang_detected = result["language"] query = result["text"] else: # Avec l'aide de la bibliothèque speech_recognition de Google Lang_detected = st.session_state['Language'] # Transcription google audio_stream = sr.AudioData(audio_bytes, 32000, 2) r = sr.Recognizer() query = r.recognize_google(audio_stream, language = Lang_detected) # Transcription # st.write("**"+tr("Vendeur :")+"** "+query) with st.chat_message("user"): st.markdown(query) st.write("") if query != "": input_messages = [HumanMessage(query)] output = app.invoke( {"messages": input_messages, "language": language}, config, ) #with st.chat_message("user"): # Add user message to chat history st.session_state.messages.append({"role": "user", "content": query}) # Récupération de la réponse custom_sentence = output["messages"][-1].content # Joue l'audio play_audio(custom_sentence,Lang_detected , 1) # st.write("**"+tr("Prospect :")+"** "+custom_sentence) with st.chat_message("assistant"): st.markdown(custom_sentence) # Add user message to chat history st.session_state.messages.append({"role": "assistant", "content": custom_sentence}) except KeyboardInterrupt: st.write(tr("Arrêt de la reconnaissance vocale.")) except: st.write(tr("Problème, essayer de nouveau..")) st.write("") # Ajouter un espace pour séparer les zones # st.divider() with col2: if ("messages" in st.session_state) : if (st.session_state.messages != []): # Display chat messages from history on app rerun for message in st.session_state.messages: with st.chat_message(message["role"]): st.markdown(message["content"]) else: if to_init and not initialized: init_run() st.write("**thread_id:** "+thread_id) for i in range(8,len(question)): st.write("") q = st.text_input(label=".", value=tr(question[i]),label_visibility="collapsed") if (q !=""): input_messages = [HumanMessage(q)] output = app.invoke( {"messages": input_messages, "language": language}, config, ) # output = app.invoke( # {"messages": q,"language": language}, # config, # ) custom_sentence = output["messages"][-1].content st.write(custom_sentence) st.write("") if (used_model[:3] == 'mis'): time.sleep(2) st.divider()