cartographie

.streamlit/.env

@@ -1 +1,2 @@
 API_TOKEN_PERPLEXITYAI = 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
+OPENAI_API_KEY = sk-proj-2bop2HKuCcRui0omQJnYT3BlbkFJQChzXj1sc6N3FfwV6fk2

app.py

@@ -8,6 +8,8 @@ from statistiques import main as display_statistics
 from ActionsRSE import display_actions_rse
 from AnalyseActionsRSE import display_analyse_actions_rse
 from partiesprenantes import display_materiality_partiesprenantes
+from partie_prenante_carte import display_pp
+from pp_viz import display_viz
 
 # Import modifiédes fonctions liées aux scripts
 from projetRSE import display_rse_projects
@@ -68,15 +70,24 @@ def main():
                 "Prompt RSE disponibles",
                 "Générations de contenus RSE",
                 "Parties prenantes",
+                "Cartographie",
                 "Matrice de matérialité"
             ]
         )
 
         if ia_mode == "Parties prenantes":
-            data, bziiit_data = fetch_data()
-            selected_company = display_company_selection_for_materiality(data)
-            if selected_company:
-                display_materiality_partiesprenantes(selected_company, data, bziiit_data)
+            # data, bziiit_data = fetch_data()
+            # selected_company = display_company_selection_for_materiality(data)
+            # if selected_company:
+            #     display_materiality_partiesprenantes(selected_company, data, bziiit_data)
+            display_pp()
+        
+        elif ia_mode == "Cartographie":
+            # data, bziiit_data = fetch_data()
+            # selected_company = display_company_selection_for_materiality(data)
+            # if selected_company:
+            #     display_materiality_partiesprenantes(selected_company, data, bziiit_data)
+            display_viz()
 
         elif ia_mode == "Matrice de matérialité":
             data, bziiit_data = fetch_data()

partie_prenante_carte.py

@@ -0,0 +1,187 @@
+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)
+                
+
+

partiesprenantes.py

@@ -30,6 +30,7 @@ def display_company_selection_for_materiality(data):
 
     selected_company = st.selectbox('Sélectionnez une entreprise', companies, index=0)
 
+    
     # If the default selection is still selected, return None
     if selected_company == "Sélectionner l'entreprise engagée à découvrir":
         return None

pp_viz.py

@@ -0,0 +1,41 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import re
+
+import altair as alt
+from session import get_partie_prenante
+import os
+from streamlit_vertical_slider import vertical_slider
+
+def display_viz():
+    st.header("Viz")
+    st.title("Visualisation des parties prenantes")
+    partie_prenante = get_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")
+    )
+
+    number_of_sliders = len(partie_prenante)
+    st.write("Modifiez le pouvoir des parties prenantes en utilisant les sliders ci-dessous")
+    bar = st.columns(number_of_sliders)
+    for i in range(number_of_sliders):
+        with bar[i]:
+            df["pouvoir"][i] = 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.altair_chart(c, use_container_width=True)

requirements.txt

@@ -23,4 +23,8 @@ openai
 InstructorEmbedding 
 sentence-transformers==2.2.2
 langchainhub
-
+plotly==5.22.0
+pandas
+"altair[all]"
+streamlit-vertical-slider
+streamlit_toggle

session.py

@@ -11,4 +11,13 @@ def get_rag():
     
 def set_rag(vectorstore, chain):
     st.session_state['vectorstore'] = vectorstore
-    st.session_state['chain'] = chain
+    st.session_state['chain'] = chain
+
+def set_partie_prenante(partie_prenante):
+    st.session_state['partie_prenante'] = partie_prenante
+
+def get_partie_prenante():
+    if 'partie_prenante' in st.session_state:
+        return st.session_state['partie_prenante']
+    else:
+        return None