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import streamlit as st
from streamlit_chat import message
from ingest_data import embed_doc
from query_data import get_chain
import os
import time
st.set_page_config(page_title="LangChain Local PDF Chat", page_icon=":robot:")
footer="""<style>
.footer {
position: fixed;
left: 0;
bottom: 0;
width: 100%;
background-color: white;
color: black;
text-align: right;
}
</style>
<div class="footer">
<p>Adapted with ❤ and \U0001F916 by Fakezeta from the original Mobilefirst</p>
</div>
"""
st.markdown(footer,unsafe_allow_html=True)
def process_file(uploaded_file):
with open(uploaded_file.name,"wb") as f:
f.write(uploaded_file.getbuffer())
st.write("File Uploaded successfully")
with st.spinner("Document is being vectorized...."):
vectorstore = embed_doc(uploaded_file.name)
f.close()
os.remove(uploaded_file.name)
return vectorstore
def get_text():
input_text = st.text_input("You: ", value="", key="input", disabled=st.session_state.disabled)
return input_text
def query(query):
start = time.time()
with st.spinner("Doing magic...."):
if len(st.session_state.past) > 0 and len(st.session_state.generated) > 0:
chat_history=[("HUMAN: "+st.session_state.past[-1], "ASSISTANT: "+st.session_state.generated[-1])]
else:
chat_history=[]
print("chat_history:", chat_history)
output = st.session_state.chain.run(input= query,
question= query,
vectorstore= st.session_state.vectorstore,
chat_history= chat_history
)
end = time.time()
print("Query time: \a "+str(round(end - start,1)))
return output
with open("style.css") as f:
st.markdown('<style>{}</style>'.format(f.read()), unsafe_allow_html=True)
st.header("Local Chat with Pdf")
if "uploaded_file_name" not in st.session_state:
st.session_state.uploaded_file_name = ""
if "past" not in st.session_state:
st.session_state.past = []
if "generated" not in st.session_state:
st.session_state["generated"] = []
if "vectorstore" not in st.session_state:
st.session_state.vectorstore = None
if "chain" not in st.session_state:
st.session_state.chain = None
uploaded_file = st.file_uploader("Choose a file", type=['pdf'])
if uploaded_file:
if uploaded_file.name != st.session_state.uploaded_file_name:
st.session_state.vectorstore = None
st.session_state.chain = None
st.session_state["generated"] = []
st.session_state.past = []
st.session_state.uploaded_file_name = uploaded_file.name
st.session_state.all_messages = []
print(st.session_state.uploaded_file_name)
if not st.session_state.vectorstore:
st.session_state.vectorstore = process_file(uploaded_file)
if st.session_state.vectorstore and not st.session_state.chain:
with st.spinner("Loading Large Language Model...."):
st.session_state.chain=get_chain(st.session_state.vectorstore)
searching=False
user_input = st.text_input("You: ", value="", key="input", disabled=searching)
send_button = st.button(label="Query")
if send_button:
searching = True
output = query(user_input)
searching = False
st.session_state.past.append(user_input)
st.session_state.generated.append(output)
if st.session_state["generated"]:
for i in range(len(st.session_state["generated"]) - 1, -1, -1):
message(st.session_state["generated"][i], key=str(i))
message(st.session_state.past[i], is_user=True, key=str(i) + "_user")
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