Delete App.py

App.py

@@ -1,94 +0,0 @@
-# Import necessary libraries
-import streamlit as st
-from streamlit_chat import message
-import tempfile
-from langchain.document_loaders.csv_loader import CSVLoader
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.llms import CTransformers
-from langchain.chains import ConversationalRetrievalChain
-
-import sys
-sys.path.append(r"vectorstore/db_faiss")
-import dataset_utils
-
-# Define the path for generated embeddings
-DB_FAISS_PATH = 'vectorstore/db_faiss'
-
-# Load the model of choice
-def load_llm():
-    llm = CTransformers(
-        model="Phind-CodeLlama-34B-v1",
-        model_type="llama",
-        max_new_tokens=512,
-        temperature=0.5
-    )
-    return llm
-
-# Set the title for the Streamlit app
-st.title("Llama2 Chat CSV - 🦜🦙")
-
-# Create a file uploader in the sidebar
-uploaded_file = st.sidebar.file_uploader("Upload File", type="csv")
-
-# Handle file upload
-if uploaded_file:
-    with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
-        tmp_file.write(uploaded_file.getvalue())
-        tmp_file_path = tmp_file.name
-
-    # Load CSV data using CSVLoader
-    loader = CSVLoader(file_path=tmp_file_path, encoding="utf-8", csv_args={'delimiter': ','})
-    data = loader.load()
-
-    # Create embeddings using Sentence Transformers
-    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2', model_kwargs={'device': 'cpu'})
-
-    # Create a FAISS vector store and save embeddings
-    db = FAISS.from_documents(data, embeddings)
-    db.save_local(DB_FAISS_PATH)
-
-    # Load the language model
-    llm = load_llm()
-
-    # Create a conversational chain
-    chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=db.as_retriever())
-
-    # Function for conversational chat
-    def conversational_chat(query):
-        result = chain({"question": query, "chat_history": st.session_state['history']})
-        st.session_state['history'].append((query, result["answer"]))
-        return result["answer"]
-
-    # Initialize chat history
-    if 'history' not in st.session_state:
-        st.session_state['history'] = []
-
-    # Initialize messages
-    if 'generated' not in st.session_state:
-        st.session_state['generated'] = ["Hello ! Ask me(LLAMA2) about " + uploaded_file.name + " 🤗"]
-
-    if 'past' not in st.session_state:
-        st.session_state['past'] = ["Hey ! 👋"]
-
-    # Create containers for chat history and user input
-    response_container = st.container()
-    container = st.container()
-
-    # User input form
-    with container:
-        with st.form(key='my_form', clear_on_submit=True):
-            user_input = st.text_input("Query:", placeholder="Talk to csv data 👉 (:", key='input')
-            submit_button = st.form_submit_button(label='Send')
-
-        if submit_button and user_input:
-            output = conversational_chat(user_input)
-            st.session_state['past'].append(user_input)
-            st.session_state['generated'].append(output)
-
-    # Display chat history
-    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")