Delete streamlit_app.py

streamlit_app.py

@@ -1,99 +0,0 @@
-import os
-import pathlib
-import re
-import streamlit as st
-from streamlit_chat import message
-from langchain.docstore.document import Document
-from langchain.document_loaders import TextLoader
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.vectorstores import FAISS
-
-st.set_page_config(
-    page_title="LLM Chatbot"
-)
-st.header(" LLM Chatbot on Custom data")
-st.sidebar.header("Instructions")
-st.sidebar.info(
-    '''This is a web application that allows you to interact with 
-    your custom data
-    '''
-)
-st.sidebar.info('''Enter a query in the text box and press enter
-    to receive a response''')
-
-st.sidebar.info('''
-This project works perfectly even on your own data
-''')
-
-os.environ["OPENAI_API_KEY"] = "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"
-
-from langchain.prompts.chat import (
-    ChatPromptTemplate,
-    SystemMessagePromptTemplate,
-    HumanMessagePromptTemplate,
-)
-from langchain.chat_models import ChatOpenAI
-from langchain.chains import RetrievalQAWithSourcesChain
-
-# Initialize Streamlit
-st.title("Stanlito AI Chatbot")
-
-# Set the data store directory
-DATA_STORE_DIR = "data_store"
-
-# Upload the files `$DATA_STORE_DIR/index.faiss` and `$DATA_STORE_DIR/index.pkl` to local
-if os.path.exists(DATA_STORE_DIR):
-    vector_store = FAISS.load_local(
-        DATA_STORE_DIR,
-        OpenAIEmbeddings()
-    )
-else:
-    st.write(f"Missing files. Upload index.faiss and index.pkl files to {DATA_STORE_DIR} directory first")
-
-# Define system template
-system_template = """Use the following pieces of context to answer the user's question.
-Take note of the sources and include them in the answer in the format: "SOURCES: source1", use "SOURCES" in capital letters regardless of the number of sources.
-If you don't know the answer, just say "I don't know", don't try to make up an answer.
-----------------
-{summaries}"""
-
-# Create the prompt
-messages = [
-    SystemMessagePromptTemplate.from_template(system_template),
-    HumanMessagePromptTemplate.from_template("{question}")
-]
-prompt = ChatPromptTemplate.from_messages(messages)
-
-# Load the language model
-llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0,
-                 max_tokens=256)  # Modify model_name if you have access to GPT-4
-
-# Create the chain
-chain_type_kwargs = {"prompt": prompt}
-chain = RetrievalQAWithSourcesChain.from_chain_type(
-    llm=llm,
-    chain_type="stuff",
-    retriever=vector_store.as_retriever(),
-    return_source_documents=True,
-    chain_type_kwargs=chain_type_kwargs
-)
-
-
-# Define function to print the result
-def print_result(result):
-    output_text = f"""### Question:
-    {query}
-    Answer:
-    {result['answer']}
-    """
-    st.markdown(output_text)
-
-
-# Get user input
-query = st.text_input("Ask a question")
-
-# Process user input
-if query:
-    result = chain(query)
-    print_result(result)