dynamic_rag / app.py
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import streamlit as st
import os
from utils import *
import uuid
def main():
st.title("Custom Chatbot")
if 'OPENAI_API_KEY' not in st.session_state:
st.session_state["OPENAI_API_KEY"]=''
if "unique_id" not in st.session_state:
st.session_state["unique_id"]=''
if "bot_ready_flag" not in st.session_state:
st.session_state["bot_ready_flag"]=''
# st.sidebar.title("πŸ˜ŽπŸ—οΈ")
# st.session_state["OPENAI_API_KEY"]=st.sidebar.text_input("Enter your API key?", type="password")
# os.environ["OPENAI_API_KEY"] = st.session_state["OPENAI_API_KEY"]
st.session_state["unique_id"] = uuid.uuid4().hex
unique_id = st.session_state["unique_id"]
os.environ["NAMESPACE"] = unique_id
files = st.sidebar.file_uploader("Upload your file", type="pdf",accept_multiple_files=True)
push_doc = st.sidebar.button("Push for bot to learn")
if push_doc:
st.session_state["bot_ready_flag"] = False
# create doc out of provided pdf
docs = create_docs(files,unique_id)
global tiny_docs
tiny_docs = split_docs(docs, chunk_size=500, chunk_overlap=40)
# creating embeddings
embedding = get_embeddings()
with st.spinner("Wait! ChatBot is Learning βœ‹πŸ»"):
push_to_pinecone(tiny_docs, embedding, os.environ.get("NAMESPACE"))
st.session_state["bot_ready_flag"] = True
st.subheader("Search Document..πŸ€–")
global query
query = st.text_area("Enter query here.. πŸ‘‡πŸ»", key='question')
submit = st.button("Get Answer")
if submit and st.session_state["bot_ready_flag"]==True:
if query:
# text embedding
embedding = get_embeddings()
# get similar docs
with st.spinner("Generating Response"):
response = get_answer(query, embedding, k=2)
st.write(response)
st.success("How was that?")
else:
st.error("You gotta be kidding me.. I really wish I could read your mind")
elif submit:
st.error("Provide the document first")
if __name__ == "__main__":
main()