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 # Montez Google Drive loader = PyPDFLoader("test-1.pdf") data = loader.load() # split the documents into chunks text_splitter1 = CharacterTextSplitter(chunk_size=512, chunk_overlap=0,separator="\n\n") texts = text_splitter1.split_documents(data) db = FAISS.from_documents(texts, HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L12-v2')) retriever = db.as_retriever( search_type="mmr", search_kwargs={'k': 1} ) 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. 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-v0.2" mistral_llm = HuggingFaceEndpoint( repo_id=repo_id, max_length=512, temperature=0.05, 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':1} 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="Chatbot Interface", page_icon="🤖") # Define function to handle user input and display chatbot response def chatbot_response(user_input): response = qa.run(user_input) return response # Streamlit components st.markdown("# 🤖 **Your Friendly Methodo Assistant**") st.markdown("## \"Votre Réponse à Chaque Défi Méthodologique\" 📈") user_input = st.text_input("You:", "") submit_button = st.button("Send 📨") # Handle user input if submit_button: if user_input.strip() != "": bot_response = chatbot_response(user_input) st.markdown("### You:") st.markdown(f"> {user_input}") st.markdown("### Bot:") st.markdown(f"> {bot_response}") 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.*")