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import os
import streamlit as st
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_huggingface import HuggingFaceEndpoint
from langchain.prompts import PromptTemplate
from langchain.schema.runnable import RunnablePassthrough
from langchain.chains import LLMChain
from huggingface_hub import login
login(token=st.secrets["HF_TOKEN"])
from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import CharacterTextSplitter
from langchain_community.document_loaders import PyPDFLoader
from langchain.chains import RetrievalQA
from langchain.prompts import PromptTemplate
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
db = FAISS.load_local("faiss_index", HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L12-v2'),allow_dangerous_deserialization=True)
retriever = db.as_retriever(
search_type="similarity",
search_kwargs={'k': 2}
)
prompt_template = """
### [INST]
Instruction: You are a Q&A assistant. Your goal is to answer questions as accurately as possible based on the instructions and context provided without using prior knowledge.You answer in FRENCH
Analyse carefully the context and provide a direct answer based on the context. If the user said Bonjour or Hello your only answer will be Hi! comment puis-je vous aider?
Answer in french only
{context}
Vous devez répondre aux questions en français.
### QUESTION:
{question}
[/INST]
Answer in french only
Vous devez répondre aux questions en français.
"""
repo_id = "mistralai/Mistral-7B-Instruct-v0.3"
mistral_llm = HuggingFaceEndpoint(
repo_id=repo_id, max_length=2048, temperature=0.02, huggingfacehub_api_token=st.secrets["HF_TOKEN"]
)
# Create prompt from prompt template
prompt = PromptTemplate(
input_variables=["question"],
template=prompt_template,
)
# Create llm chain
llm_chain = LLMChain(llm=mistral_llm, prompt=prompt)
retriever.search_kwargs = {'k':4}
qa = RetrievalQA.from_chain_type(
llm=mistral_llm,
chain_type="stuff",
retriever=retriever,
chain_type_kwargs={"prompt": prompt},
)
import streamlit as st
# Streamlit interface with improved aesthetics
st.set_page_config(page_title="Alter-IA Chat", page_icon="🤖")
# Define function to handle user input and display chatbot response
def chatbot_response(user_input):
response = qa.run(user_input)
return response
# Create columns for logos
col1, col2, col3 = st.columns([2, 3, 2])
with col1:
st.image("Design 3_22.png", width=150, use_column_width=True) # Adjust image path and size as needed
with col3:
st.image("Altereo logo 2023 original - eau et territoires durables.png", width=150, use_column_width=True) # Adjust image path and size as needed
# Streamlit components
# Ajouter un peu de CSS pour centrer le texte
# Ajouter un peu de CSS pour centrer le texte et le colorer en orange foncé
st.markdown("""
<style>
.centered-text {
text-align: center;
}
</style>
""", unsafe_allow_html=True)
# Utiliser la classe CSS pour centrer et colorer le texte
st.markdown('<h3 class="centered-text">🤖 AlteriaChat 🤖 </h3>', unsafe_allow_html=True)
st.markdown("""
<style>
.centered-orange-text {
text-align: center;
color: darkorange;
}
</style>
""", unsafe_allow_html=True)
# Centrer le texte principal
# Centrer et colorer en orange foncé le texte spécifique
st.markdown('<p class="centered-orange-text">"Votre Réponse à Chaque Défi Méthodologique "</p>', unsafe_allow_html=True)
# Input and button for user interaction
user_input = st.text_input("You:", "")
submit_button = st.button("Ask 📨")
# Handle user input
if submit_button:
if user_input.strip() != "":
bot_response = chatbot_response(user_input)
st.markdown("### Bot:")
st.text_area("", value=bot_response, height=600)
else:
st.warning("⚠️ Please enter a message.")
# Motivational quote at the bottom
st.markdown("---")
st.markdown("La collaboration est la clé du succès. Chaque question trouve sa réponse, chaque défi devient une opportunité.")
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