nice done

.gitignore

@@ -183,3 +183,4 @@ Talk-Sheet/share/jupyter/nbextensions/pydeck/index.js
 Talk-Sheet/pyvenv.cfg
 Talk-Sheet/Include/site/python3.10/greenlet/greenlet.h
 Talk-Sheet/etc/jupyter/nbconfig/notebook.d/pydeck.json
+poto-associations-sample.csv.pkl

talk_sheet.py

@@ -1,159 +1,212 @@
-from fastapi import Query
-import streamlit as st
-import pandas as pd
-import os
-
-from pathlib import Path
-
-from streamlit_chat import message
 
-from langchain.chat_models import ChatOpenAI
-from langchain.vectorstores import Chroma
-from langchain.prompts import PromptTemplate
-from langchain.document_loaders.csv_loader import CSVLoader
-from langchain.text_splitter import CharacterTextSplitter
 from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.chains import RetrievalQA
-from langchain.callbacks import get_openai_callback
-from sympy import use
-import tiktoken
-from langchain.chains import ConversationChain
-from langchain.memory import ChatMessageHistory
-from langchain.memory import ConversationBufferMemory
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import FAISS
 from langchain.chains.question_answering import load_qa_chain
-import streamlit as st
-from langchain.chains import ConversationChain
-from langchain.chains.conversation.memory import ConversationEntityMemory
-from langchain.chains.conversation.prompt import ENTITY_MEMORY_CONVERSATION_TEMPLATE
-from langchain.llms import OpenAI
-from langchain.chains import ChatVectorDBChain
+from langchain.chat_models import ChatOpenAI
 from langchain.chains import ConversationalRetrievalChain
-from langchain.chains.qa_with_sources import load_qa_with_sources_chain
-from langchain.chains import LLMChain
-from langchain.chains.conversation.memory import ConversationSummaryMemory
+import pickle
+from langchain.document_loaders.csv_loader import CSVLoader
 
+from pathlib import Path
+from dotenv import load_dotenv
+import os
+import streamlit as st
+from streamlit_chat import message
+from langchain.text_splitter import CharacterTextSplitter
+import tempfile
+import pandas as pd
+from langchain.prompts import PromptTemplate
 
+import asyncio
 
+ 
 
-# Configure the Streamlit page
+# vectors = getDocEmbeds("gpt4.pdf")
+# qa = ChatVectorDBChain.from_llm(ChatOpenAI(model_name="gpt-3.5-turbo"), vectors, return_source_documents=True)
 st.set_page_config(layout="wide", page_icon="contents\logo_site.png", page_title="Talk-Sheet")
 
 st.markdown(
-    "<h1 style='text-align: center;'>Talk-Sheet, Talk with your  sheet-data ! 💬</h1>",
-    unsafe_allow_html=True
-)
-
-# Input field for the user's OpenAI API key
-user_secret = st.sidebar.text_input(
-        label="#### Your OpenAI API key 👇",
-        placeholder="Paste your openAI API key, sk-",
-        type="password",
-    )
-os.environ["OPENAI_API_KEY"] = user_secret
-
-
-if user_secret == "":
-    st.markdown(
-        "<div style='text-align: center;'><h4>Enter your OpenAI API key to start chatting 😉</h4></div>",
-        unsafe_allow_html=True
-    )
-else:
-    # Upload CSV file
-    uploaded_file = st.sidebar.file_uploader(label=" ",label_visibility='hidden', type=["csv"])
-    if uploaded_file is not None:
-        # Show uploaded CSV file
-        def show_user_file(uploaded_file):
-            file_container = st.expander("Votre fichier CSV :")
-            shows = pd.read_csv(uploaded_file)
-            uploaded_file.seek(0)
-            file_container.write(shows)
-            
-        show_user_file(uploaded_file)
-        
-    else :
-        st.sidebar.info(
+"<h1 style='text-align: center;'>Talk-Sheet, Talk with your  sheet-data ! 💬</h1>",
+unsafe_allow_html=True)
+
+user_api_key = st.sidebar.text_input(
+    label="#### Your OpenAI API key 👇",
+    placeholder="Paste your openAI API key, sk-",
+    type="password")
+
+async def main():
+    
+    if user_api_key == "":
+        st.markdown(
+            "<div style='text-align: center;'><h4>Enter your OpenAI API key to start chatting 😉</h4></div>",
+            unsafe_allow_html=True
+        )
+    else:
+        os.environ["OPENAI_API_KEY"] = user_api_key
         
+        uploaded_file = st.sidebar.file_uploader("", type="csv", label_visibility="hidden")
+        if uploaded_file:
+            # Show uploaded CSV file
+            def show_user_file(uploaded_file):
+                file_container = st.expander("Votre fichier CSV :")
+                shows = pd.read_csv(uploaded_file)
+                uploaded_file.seek(0)
+                file_container.write(shows)
+            show_user_file(uploaded_file)
+        else :
+            st.sidebar.info(
             "👆 Upload a .csv file to get started, "
             "example : [fishfry-locations.csv](https://drive.google.com/file/d/18i7tN2CqrmoouaSqm3hDfAk17hmWx94e/view?usp=sharing)" 
-        )
+            )
     
-    if uploaded_file:
         
-        # Save user's CSV file
-        save_folder = 'contents\dataset'
-        save_path = Path(save_folder, uploaded_file.name)
-        with open(save_path, mode='wb') as w:
-            w.write(uploaded_file.getvalue())
+        if uploaded_file :
+            async def storeDocEmbeds(file, filename):
+                with tempfile.NamedTemporaryFile(mode="wb", delete=False) as tmp_file:
+                    tmp_file.write(file)
+                    tmp_file_path = tmp_file.name
+
+                loader = CSVLoader(file_path=tmp_file_path, encoding="utf-8")
+                data = loader.load()
+                
+                splitter = CharacterTextSplitter(separator="\n",chunk_size=1500, chunk_overlap=0)
+                chunks = splitter.split_documents(data)
+                
+                embeddings = OpenAIEmbeddings()
+                vectors = FAISS.from_documents(chunks, embeddings)
+                os.remove(tmp_file_path)
+
+                
+                with open(filename + ".pkl", "wb") as f:
+                    pickle.dump(vectors, f)
+
+                
+            async def getDocEmbeds(file, filename):
+                
+                if not os.path.isfile(filename + ".pkl"):
+                    await storeDocEmbeds(file, filename)
+                
+                with open(filename + ".pkl", "rb") as f:
+                    global vectores
+                    vectors = pickle.load(f)
+                    
+                return vectors
             
-        file_path_user=os.path.join('contents\dataset', uploaded_file.name)
 
-        memory = ConversationSummaryMemory(llm=OpenAI(), memory_key="chat_history")
-        
+            async def conversational_chat(query):
+                result = qa({"question": query, "chat_history": st.session_state['history']})
+                st.session_state['history'].append((query, result["answer"]))
+                print("Log: ")
+                print(st.session_state['history'])
+                return result["answer"]
+
+            prompt_template = (
+            "You are Talk-Sheet, a user-friendly chatbot designed to assist users by engaging in conversations based on data from CSV or Excel files. "
+            "Your knowledge comes from:"
+
+            "{context}"
+
+            "Help users by providing relevant information from the data in their files. Answer their questions accurately and concisely. "
+            "If the user's specific issue or need cannot be addressed with the available data, "
+            "empathize with their situation and suggest that they may need to seek assistance elsewhere. "
+            "Always maintain a friendly and helpful tone. "
+            "If you don't know the answer to a question, truthfully say you don't know."
+            "answers the user's question in the same language as the user"
+      
+            "Human: {question} "
+
+            "Talk-Sheet: "
+            )
+
+            PROMPT = PromptTemplate(template=prompt_template, input_variables=["context","question"])
 
-        with st.sidebar.expander(" 🛠️ Settings ", expanded=False):
+                    # Set up sidebar with various options
+            with st.sidebar.expander("🛠️ setting", expanded=False):
+                # Option to preview memory store
+                if st.button("Reset Chat"):
+                    st.session_state['reset_chat'] = True
 
-            MODEL = st.selectbox(label='Model', options=['gpt-3.5-turbo','gpt-4'])
+                MODEL = st.selectbox(label='Model', options=['gpt-3.5-turbo','gpt-4'])
+                
+            #llm = ChatOpenAI(model_name="gpt-3.5-turbo")
+            #chain = load_qa_chain(llm, chain_type="stuff")
             
 
-        try:
-            # Create retriever from user's CSV file
-            loader = CSVLoader(file_path=file_path_user, encoding="utf-8")
-            data = loader.load()
-            text_splitter = CharacterTextSplitter(separator="\n",chunk_size=1500, chunk_overlap=0)
-            documents = text_splitter.split_documents(data)
+            if 'history' not in st.session_state:
+                st.session_state['history'] = []
+
+
+            if 'ready' not in st.session_state:
+                st.session_state['ready'] = False
+                
+            if 'reset_chat' not in st.session_state:
+                st.session_state['reset_chat'] = False
 
-            embeddings = OpenAIEmbeddings()
-            
-            vectorstore = Chroma.from_documents(documents, embeddings)
 
-            # return ConversationRetrievalChain that answers user questions based on a given document store
-            chain = ConversationalRetrievalChain.from_llm(ChatOpenAI(temperature=0, model_name=MODEL),
-                retriever=vectorstore.as_retriever(search_type="similarity", search_kwargs={"k":2})
-            )
-    
-            # Chatbot UI function
-            if 'generated' not in st.session_state:
-                st.session_state['generated'] = []
-
-            if 'past' not in st.session_state:
-                st.session_state['past'] = []
-
-            def generate_response(query):
-                chat_history = []
-
-                result = chain({'chat_history': {}, 'question': query})
-                chat_history = []
-                query = query
-                result = chain({"question": query, "chat_history": chat_history})
-                response = result["answer"]
-                print(f"Type of response: {type(response)}, response: {response}")
-                return response
-
-            def get_text():
-                input_text = st.text_input("##### Let's Talk ! 👇: ", key="input", placeholder="Your AI assistant here! Ask me anything ...")
-                return input_text
             
-            user_input = get_text()
 
-            if user_input:
-                output = generate_response(user_input)
+            if uploaded_file is not None:
+
+                with st.spinner("Processing..."):
+                # Add your code here that needs to be executed
+                    uploaded_file.seek(0)
+                    file = uploaded_file.read()
+                    # pdf = PyPDF2.PdfFileReader()
+                    vectors = await getDocEmbeds(file, uploaded_file.name)
+                    qa = ConversationalRetrievalChain.from_llm(ChatOpenAI(model_name=MODEL), retriever=vectors.as_retriever(), qa_prompt=PROMPT,return_source_documents=False)
+
+                st.session_state['ready'] = True
+
+
+            if st.session_state['ready']:
+
+                    # Le reste du code existant
 
-                st.session_state.past.append(user_input)
-                st.session_state.generated.append(output)
+                if 'generated' not in st.session_state:
+                    st.session_state['generated'] = ["Welcome! You can now ask any questions regarding " + uploaded_file.name]
 
-            if st.session_state['generated']:
-                print(f"st.session_state['generated']: {st.session_state['generated']}")
+                if 'past' not in st.session_state:
+                    st.session_state['past'] = ["Hey!"]
 
-                for i in range(len(st.session_state['generated'])-1, -1, -1):
-                    message(st.session_state["generated"][i], key=str(i))
-                    message(st.session_state['past'][i], is_user=True, key=str(i) + '_user')
-        except Exception as e:
-            st.error(f"Error: {str(e)}")
+                # container for chat history
+                response_container = st.container()
 
 
 
-# About section
-about = st.sidebar.expander("About Talk-Sheet 🤖")
-about.write("#### Talk-Sheet is a user-friendly chatbot designed to assist users by engaging in conversations based on data from CSV or excel files. 📄")
-about.write("#### Ideal for various purposes and users, Talk-Sheet provides a simple yet effective way to interact with your sheet-data. 🌐")
-about.write("#### Powered by [Langchain]('https://github.com/hwchase17/langchain'), [OpenAI]('https://platform.openai.com/docs/models/gpt-3-5') and [Streamlit]('https://github.com/streamlit/streamlit') Talk-Sheet offers a seamless and personalized experience. ⚡")
+
+                # container for text box
+                container = st.container()
+
+                with container:
+                    with st.form(key='my_form', clear_on_submit=True):
+                        user_input = st.text_input("Query:", placeholder="e.g: Summarize the paper in a few sentences", key='input')
+                        submit_button = st.form_submit_button(label='Send')
+                        
+
+                        if st.session_state['reset_chat']:
+                            st.session_state['history'] = []
+                            st.session_state['past'] = ["Hey!"]
+                            st.session_state['generated'] = ["Welcome! You can now ask any questions regarding " + uploaded_file.name]
+                            response_container.empty()
+                            st.session_state['reset_chat'] = False
+
+                    if submit_button and user_input:
+                        output = await conversational_chat(user_input)
+                        st.session_state['past'].append(user_input)
+                        st.session_state['generated'].append(output)
+
+                if st.session_state['generated']:
+                    with response_container:
+                        for i in range(len(st.session_state['generated'])):
+                            message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="big-smile")
+                            message(st.session_state["generated"][i], key=str(i), avatar_style="thumbs")
+            
+    # About section
+    about = st.sidebar.expander("About Talk-Sheet 🤖")
+    about.write("#### Talk-Sheet is a user-friendly chatbot designed to assist users by engaging in conversations based on data from CSV or excel files. 📄")
+    about.write("#### Ideal for various purposes and users, Talk-Sheet provides a simple yet effective way to interact with your sheet-data. 🌐")
+    about.write("#### Powered by [Langchain]('https://github.com/hwchase17/langchain'), [OpenAI]('https://platform.openai.com/docs/models/gpt-3-5') and [Streamlit]('https://github.com/streamlit/streamlit') Talk-Sheet offers a seamless and personalized experience. ⚡")
+
+if __name__ == "__main__":
+    asyncio.run(main())