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import os
__import__("pysqlite3")
import sys
sys.modules["sqlite3"] = sys.modules.pop("pysqlite3")
import streamlit as st
from langchain.chains import ConversationalRetrievalChain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.chat_models import ChatOpenAI
from langchain_community.document_loaders import Docx2txtLoader, PyPDFLoader, TextLoader
from langchain_community.embeddings.openai import OpenAIEmbeddings
from langchain_community.vectorstores.chroma import Chroma
from apikey import llm_api_key
key = llm_api_key
def load_and_process_file(file_data):
"""
Load and process the uploaded file.
Returns a vector store containing the embedded chunks of the file.
"""
file_name = os.path.join("./", file_data.name)
with open(file_name, "wb") as f:
f.write(file_data.getvalue())
_, extension = os.path.splitext(file_name)
# Load the file using the appropriate loader
if extension == ".pdf":
loader = PyPDFLoader(file_name)
elif extension == ".docx":
loader = Docx2txtLoader(file_name)
elif extension == ".txt":
loader = TextLoader(file_name)
else:
st.write("This document format is not supported!")
return None
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
chunks = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
vector_store = Chroma.from_documents(chunks, embeddings)
return vector_store
def initialize_chat_model(vector_store):
"""
Initialize the chat model with the given vector store.
Returns a ConversationalRetrievalChain instance.
"""
llm = ChatOpenAI(
model="gpt-3.5-turbo",
temperature=0,
openai_api_key=key,
)
retriever = vector_store.as_retriever()
return ConversationalRetrievalChain.from_llm(llm, retriever)
def main():
"""
The main function that runs the Streamlit app.
"""
st.set_page_config(page_title="InkChatGPT", page_icon="📚")
st.title("📚 InkChatGPT")
st.write("Upload a document and ask questions related to its content.")
uploaded_file = st.file_uploader(
"Select a file", type=["pdf", "docx", "txt"], key="file_uploader"
)
if uploaded_file:
add_file = st.button(
"Process File",
on_click=clear_history,
key="process_button",
)
if uploaded_file and add_file:
with st.spinner("💭 Thinking..."):
vector_store = load_and_process_file(uploaded_file)
if vector_store:
crc = initialize_chat_model(vector_store)
st.session_state.crc = crc
st.success("File processed successfully!")
if "crc" in st.session_state:
st.markdown("## Ask a Question")
question = st.text_area(
"Enter your question",
height=93,
key="question_input",
)
submit_button = st.button("Submit", key="submit_button")
if submit_button and "crc" in st.session_state:
handle_question(question)
display_chat_history()
def handle_question(question):
"""
Handles the user's question by generating a response and updating the chat history.
"""
crc = st.session_state.crc
if "history" not in st.session_state:
st.session_state["history"] = []
with st.spinner("Generating response..."):
response = crc.run(
{
"question": question,
"chat_history": st.session_state["history"],
}
)
st.session_state["history"].append((question, response))
st.write(response)
def display_chat_history():
"""
Displays the chat history in the Streamlit app.
"""
if "history" in st.session_state:
st.markdown("## Chat History")
for q, a in st.session_state["history"]:
st.markdown(f"**Question:** {q}")
st.write(a)
st.write("---")
def clear_history():
"""
Clear the chat history stored in the session state.
"""
if "history" in st.session_state:
del st.session_state["history"]
if __name__ == "__main__":
main()
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