Update app.py

app.py

@@ -1,106 +0,0 @@
-import streamlit as st
-from streamlit_chat import message
-import tempfile
-#from langchain_community.documentloader.csv_loader import CSVLoader
-from langchain_community.document_loaders.csv_loader import CSVLoader
-from langchain_community.embeddings import HuggingFaceEmbeddings
-#from langchain_community.embeddings import HuggingFaceBgeEmbeddings
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import FAISS
-#from langchain_community.llms import CTransformers
-from langchain_community.llms.ctransformers import CTransformers
-
-from langchain.chains.conversational_retrieval.base import ConversationalRetrievalChain
-                                                            
-#from langchain.chains.conversational_retrieval.base import ConversationalRetreievalChain
-
-
-DB_FAISS_PATH = 'vectorstore/db_faiss'
-
-def load_llm():
-    # load model from hugging face repo
-    llm = CTransformers(
-        model = 'TheBloke/Llama-2-7B-Chat-GGML',
-        model_type = 'llma',
-        max_new_token = 512,
-        temperature = 0.5
-    )
-    return llm
-
-st.title("Chat with CSV using Llma 2")
-st.markdown("<h1 style='text-align: center; color: blue;'>Chat with your PDF 📄 </h1>", unsafe_allow_html=True)
-st.markdown("<h3 style='text-align: center; color: grey;'>Built by <a href='https://github.com/DrKareemKAmal'>MindSparks ❤️ </a></h3>", unsafe_allow_html=True)
-
-uploaded_file = st.sidebar.file_uploader('Upload your data', type=['csv'])
-
-if uploaded_file:
-    with tempfile.NamedTemporaryFile(delete=False)as temp_file :
-        temp_file.write(uploaded_file.getvalue())
-        tempfile_path = temp_file.name
-    
-    loader = CSVLoader(file_path = tempfile_path, encoding = 'utf-8',
-                        csv_args = {'delimiter': ','} )
-    data = loader.load()
-    #st.json(data)
-
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size = 500 , chunk_overlap = 20)
-    text_chunks = text_splitter.split_documents(data)
-    st.write (f"Total text chunks : {len(text_chunks)}")
-
-    embeddings = HuggingFaceEmbeddings(
-        model = 'sentence-transformers/all-MiniLM-L6-v2',
-        model_kwargs = {'device': 'cpu'}
-    )
-
-
-    db = FAISS.from_documents(text_chunks, embeddings)
-    db.save_local (DB_FAISS_PATH)
-    llm = load_llm()
-
-    chain = ConversationalRetrievalChain.from_llm(llm= llm , retriever = db.as_retriever())
-
-    def conversational_chat(query):
-        result  = chain({"quetion": query ,
-                         "chat_history": st.session_state['history']})
-        st.session_state['history'].append((query , result['answer']))
-        return result['answer']
-    
-    if 'history' not in st.session_state :
-        st.session_state['history'] = []
-
-    if 'generated' not in st.session_state :
-        st.session_state['generated'] = ['Hello, Ask me anything about ' + uploaded_file.name]
-
-    if 'past' not in st.session_state :
-        st.session_state['past'] = ['Hey !']
-
-    # Container for the chat history
-    response_container = st.container()
-    container = st.container()
-
-    with container :
-        with st.form(key = 'my_form',
-                     clear_on_submit=True):
-            user_input = st.text_input('Query:', placeholder= "Talk to youur CSV Data here ")
-            submit_button = st.from_submit_button(label = 'chat')
-
-        if submit_button and user_input :
-            output = 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='thumb')
-        
-
-
-
-
-
-
-