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import streamlit as st
import pandas as pd
import numpy as np
import re
import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter,RecursiveCharacterTextSplitter
from langchain_experimental.text_splitter import SemanticChunker
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_community.chat_models import ChatOpenAI
from langchain.llms import HuggingFaceHub
from langchain import hub
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain_community.document_loaders import WebBaseLoader
from langchain_core.prompts.prompt import PromptTemplate
import altair as alt
from session import set_partie_prenante
import os
from streamlit_vertical_slider import vertical_slider
load_dotenv()
def get_docs_from_website(urls):
loader = WebBaseLoader(urls, header_template={
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.0.0 Safari/537.36',
})
docs = loader.load()
return docs
def get_doc_chunks(docs):
# Split the loaded data
# text_splitter = RecursiveCharacterTextSplitter(
# chunk_size=500,
# chunk_overlap=100)
text_splitter = SemanticChunker(OpenAIEmbeddings())
docs = text_splitter.split_documents(docs)
return docs
def disp_test():
chart_data = pd.DataFrame(np.random.randn(20, 3), columns=["a", "b", "c"])
st.scatter_chart(chart_data)
def get_vectorstore_from_docs(doc_chunks):
embedding = OpenAIEmbeddings(model="text-embedding-3-large")
vectorstore = FAISS.from_documents(documents=doc_chunks, embedding=embedding)
return vectorstore
def get_conversation_chain(vectorstore):
llm = ChatOpenAI(model="gpt-4o",temperature=0.5, max_tokens=2048)
retriever=vectorstore.as_retriever()
prompt = hub.pull("rlm/rag-prompt")
# Chain
rag_chain = (
{"context": retriever , "question": RunnablePassthrough()}
| prompt
| llm
)
return rag_chain
# FILL THE PROMPT FOR THE QUESTION VARIABLE THAT WILL BE USED IN THE RAG PROMPT, ATTENTION NOT CONFUSE WITH THE RAG PROMPT
def fill_promptQ_template(input_variables, template):
prompt = PromptTemplate(input_variables=["BRAND_NAME","BRAND_DESCRIPTION"], template=template)
return prompt.format(BRAND_NAME=input_variables["BRAND_NAME"], BRAND_DESCRIPTION=input_variables["BRAND_DESCRIPTION"])
template_extraction_PP = '''
Objectif : identifiez et proposez tout les noms de marques qui serviront comme partie prenante de la marque suivante pour développer un marketing de coopération (co-op marketing)
Le nom de la marque de référence est le suivant : {BRAND_NAME}
Son activité est la suivante : {BRAND_DESCRIPTION}
TA REPONSE DOIT ETRE SOUS FORME DE LISTE DE NOMS DE MARQUES
'''
#don't forget to add the input variables from the maim function
def text_to_list(text):
lines = text.replace("- ","").split('\n')
lines = [line.split() for line in lines]
items = [[' '.join(line[:-1]),line[-1]] for line in lines]
# Assuming `items` is the list of items
for item in items:
item[1] = re.sub(r'\D', '', item[1])
return items
def extract_pp(urls,input_variables):
template_extraction_PP = '''
Objectif : identifiez et proposez tout les noms de marques qui serviront comme partie prenante de la marque suivante pour développer un marketing de coopération (co-op marketing)
Le nom de la marque de référence est le suivant : {BRAND_NAME}
Son activité est la suivante : {BRAND_DESCRIPTION}
TA REPONSE DOIT ETRE SOUS FORME DE LISTE DE NOMS DE MARQUES
'''
#don't forget to add the input variables from the maim function
docs = get_docs_from_website(urls)
#get text chunks
text_chunks = get_doc_chunks(docs)
#create vectorstore
vectorstore = get_vectorstore_from_docs(text_chunks)
chain = get_conversation_chain(vectorstore)
question = fill_promptQ_template(input_variables, template_extraction_PP)
response = chain.invoke(question)
# version plus poussée a considérer
# each item in the list is a list with the name of the brand and the similarity percentage
#partie_prenante = text_to_list(response.content)
#version simple
partie_prenante = response.content.replace("- ","").split('\n')
return partie_prenante
def disp_vertical_slider(partie_prenante):
number_of_sliders = len(partie_prenante)
st.set_page_config(layout="wide")
st.subheader("Vertical Slider")
st.title("Vertical Slider")
st.write("This is a vertical slider example")
bar = st.columns(number_of_sliders)
for i in range(number_of_sliders):
with bar[i]:
tst = vertical_slider(
label=partie_prenante[i],
height=100,
key=partie_prenante[i],
default_value=50,
thumb_color= "orange", #Optional - Defaults to Streamlit Red
step=1,
min_value=0,
max_value=100,
value_always_visible=False,
)
st.write(tst)
def display_pp():
load_dotenv()
st.header("INDIQUEZ VOS PAGES WEB ET/OU DOCUMENTS D’ENTREPRISE POUR AUDITER LE CONTENU RSE")
loaded = False
option = st.radio("Source", ("A partir de votre site web", "A partir de vos documents entreprise"))
if option == "A partir de votre site web":
url1 = st.text_input("URL 1")
brand_name = st.text_input("Nom de la marque")
brand_description = st.text_area("Description de la marque")
if st.button("Process") and loaded == False:
loaded = True
with st.spinner("Processing..."):
input_variables = {"BRAND_NAME": brand_name, "BRAND_DESCRIPTION": brand_description}
partie_prenante = extract_pp([url1], input_variables)
partie_prenante = sorted(partie_prenante)
set_partie_prenante(partie_prenante)
st.write(pd.DataFrame(partie_prenante, columns=["Partie prenante"]))
# alphabet = [ pp[0] for pp in partie_prenante]
# pouvoir = [ 50 for _ in range(len(partie_prenante))]
# df = pd.DataFrame({'partie_prenante': partie_prenante, 'pouvoir': pouvoir, 'code couleur': partie_prenante})
# st.write(df)
# c = (
# alt.Chart(df)
# .mark_circle(size=300)
# .encode(x="partie_prenante", y=alt.Y("pouvoir",scale=alt.Scale(domain=[0,100])), color="code couleur")
# )
# st.subheader("Vertical Slider")
# age = st.slider("How old are you?", 0, 130, 25)
# st.write("I'm ", age, "years old")
# disp_vertical_slider(partie_prenante)
# st.altair_chart(c, use_container_width=True)
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