-
Notifications
You must be signed in to change notification settings - Fork 0
/
chatpdf.py
113 lines (91 loc) · 3.93 KB
/
chatpdf.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from template import css, bot_template, user_template
import os
from langchain_together import Together
from dotenv import load_dotenv
load_dotenv()
TOGETHER_API = os.getenv("TOGETHER_API")
def get_pdf_text(pdf_docs):
text = ""
for pdf in pdf_docs:
pdf_reader = PdfReader(pdf)
for page in pdf_reader.pages:
text += page.extract_text()
return text
def get_text_chunks(text):
text_splitter = CharacterTextSplitter(
separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len
)
chunks = text_splitter.split_text(text)
return chunks
def get_vectorstore(text_chunks):
embeddings = HuggingFaceEmbeddings(model_name="nomic-ai/nomic-embed-text-v1", model_kwargs={"trust_remote_code": True})
vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
return vectorstore
def get_conversation_chain(vectorstore):
llm = Together(model="mistralai/Mixtral-8x7B-Instruct-v0.1",
temperature=0.7, max_tokens=450,
together_api_key=TOGETHER_API)
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
conversation_chain = ConversationalRetrievalChain.from_llm(
llm=llm, retriever=vectorstore.as_retriever(), memory=memory
)
return conversation_chain
def handle_userinput(user_question):
if st.session_state.conversation is not None:
response = st.session_state.conversation({"question": user_question})
st.session_state.chat_history = response["chat_history"]
for i, message in enumerate(st.session_state.chat_history):
if i % 2 == 0:
st.write(
user_template.replace("{{MSG}}", message.content),
unsafe_allow_html=True,
)
else:
st.write(
bot_template.replace("{{MSG}}", message.content), unsafe_allow_html=True
)
def main():
st.set_page_config(page_title="Chatbot")
st.write(css, unsafe_allow_html=True)
if "conversation" not in st.session_state:
st.session_state.conversation = None
if "chat_history" not in st.session_state:
st.session_state.chat_history = None
if "show_uploader" not in st.session_state:
st.session_state.show_uploader = False
st.header("Chatbot for PDFs")
col1, col2 = st.columns([9, 1])
with col1:
user_question = st.text_input("Ask a question about your documents:", key="user_question")
if st.button("Ask"):
handle_userinput(user_question)
with col2:
if st.button("📁"):
st.session_state.show_uploader = True
st.session_state.conversation = None
st.session_state.chat_history = None
#st.session_state.reset_trigger = True
st.rerun()
if st.session_state.show_uploader:
with st.expander("Upload PDFs", expanded=True):
pdf_docs = st.file_uploader(
"Upload your PDFs here and click on 'Add Data'", accept_multiple_files=True
)
if st.button("Add Data"):
if pdf_docs:
with st.spinner("Adding Data..."):
raw_text = get_pdf_text(pdf_docs)
text_chunks = get_text_chunks(raw_text)
vectorstore = get_vectorstore(text_chunks)
st.session_state.conversation = get_conversation_chain(vectorstore)
st.session_state.show_uploader = False
st.rerun()
if __name__ == "__main__":
main()