app.py

# import streamlit as st
# import random
# import time
# from mistralai.client import MistralClient
# from mistralai.models.chat_completion import ChatMessage

# api_key = st.secrets["MISTRAL_API_KEY"]
# model = "mistral-large-latest"

# client = MistralClient(api_key=api_key)

# chat_response = client.chat(
#     model=model,
#     messages=[ChatMessage(role="user", content="What is the best French cheese?")]
# )



# # Streamed response emulator
# def response_generator():
#     response = client.chat(
#     model=model,
#     messages=[ChatMessage(role="user", content="What is the best French cheese?")]
# )


# st.title("Personality test")

# # Initialize chat history
# if "messages" not in st.session_state:
#     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"])

# # Accept user input
# if prompt := st.chat_input("What is up?"):
#     # Add user message to chat history
#     st.session_state.messages.append({"role": "user", "content": prompt})
#     # Display user message in chat message container
#     with st.chat_message("user"):
#         st.markdown(prompt)

#     # Display assistant response in chat message container
#     with st.chat_message("assistant"):
#         response = st.write_stream(response_generator())
#     # Add assistant response to chat history
#     st.session_state.messages.append({"role": "assistant", "content": response})



from mistralai.client import MistralClient
from mistralai.models.chat_completion import ChatMessage
import streamlit as st
import json
import faiss
import numpy as np

model = "open-mixtral-8x7b"
mistral_api_key = st.secrets["MISTRAL_API_KEY"]
client = MistralClient(api_key=mistral_api_key)

st.title("Assistant ChatBot catalogue 2024")

def load_json(rep:str):
    f = open(rep, encoding='UTF-8')
    return json.load(f)

def split_chunk(data, chunk_size):
    data_str = [json.dumps(entry) for entry in data]
    chunk_size = chunk_size
    chunks = [data_str[i:i + chunk_size] for i in range(0, len(data_str), chunk_size)]
    print(f"Nb. chunks = {len(chunks)}")
    return chunks
    
def get_text_embedding(input):
    embeddings_batch_response = client.embeddings(
          model='mistral-embed',
          input=input
      )
    return embeddings_batch_response.data[0].embedding

def load_vector_db(text_embedded):
    d = text_embedded.shape[1]
    index = faiss.IndexFlatL2(d)
    index.add(text_embedded)
    return index

def find_similar_chunk(index, question_embeddings, chunks):
    D, I = index.search(question_embeddings, k=2) # distance, index
    return [chunks[i] for i in I.tolist()[0]]

def prompt_chat(retrieved_chunk, question):
    return f"""
    Les informations contextuelles sont les suivantes.
    ---------------------
    {retrieved_chunk}
    ---------------------
    Compte tenu des informations contextuelles et sans connaissances préalables,
    réponds en français à la question suivante de manière concise.
    Utilise des listes pour plus de lisibilité. 
    Question: {question}
    Réponse:
    """

# Chargement des données
data = load_json('catalogue_2024.json')
chunks = split_chunk(data, 3)
text_embeddings = np.load("catalogue_embeddings.npy")
index = load_vector_db(text_embeddings)

if "messages" not in st.session_state:
    st.session_state["messages"] = [{"role": "assistant", "content": "Comment puis-je vous aider?"}]
    st.session_state["History"] = []
    st.session_state.History.append(ChatMessage(role="assitant", content="Comment puis-je vous aider?"))

for msg in st.session_state.messages:
    st.chat_message(msg["role"]).write(msg["content"])

if prompt := st.chat_input():
    question_embeddings = np.array([get_text_embedding(prompt)])
    retrieved_chunk = find_similar_chunk(index, question_embeddings, chunks)
    p = prompt_chat(retrieved_chunk=retrieved_chunk, question=prompt)

    st.session_state.messages.append({"role": "user", "content": prompt})
    st.session_state.History.append(ChatMessage(role="user", content=p))
    st.chat_message("user").write(prompt)

    with st.chat_message("assistant"):
        message_placeholder = st.empty()
        full_response = ""
        for response in client.chat_stream(
            model=model,
            messages=st.session_state.History[1:]
        ):
            full_response += (response.choices[0].delta.content or "")
            message_placeholder.markdown(full_response + "|")
        
        message_placeholder.markdown(full_response)
        
        st.session_state.History.append(ChatMessage(role="assistant", content=full_response))
        st.session_state.messages.append({"role": "assistant", "content": full_response})