Spaces:
Running
Running
File size: 3,355 Bytes
b4e5268 8f64959 55a8b20 dd507bb 4f4aca6 b4e5268 397c421 0a98570 9b1d956 6a7d03a 2cc0376 4f4aca6 37b2fc4 6a7d03a 9d68da3 b956157 b4e5268 b956157 b4e5268 4f4aca6 dc9b093 4f4aca6 b4e5268 4f4aca6 b4e5268 ab55f29 2651861 ab55f29 4f4aca6 ab55f29 0f28a78 2651861 ab55f29 2651861 ab55f29 2651861 ab55f29 2651861 0f28a78 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 |
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.*")
|