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()