Spaces:
Sleeping
Sleeping
File size: 6,072 Bytes
0532c05 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 |
import streamlit as st
import logging
from streamlit_chat import message
from langchain.chains import ConversationalRetrievalChain
from langchain.embeddings import HuggingFaceEmbeddings, CacheBackedEmbeddings, HuggingFaceInstructEmbeddings
from langchain.llms import LlamaCpp
from langchain.vectorstores import FAISS
from langchain.memory import ConversationBufferMemory
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import PyPDFLoader
from langchain.storage import LocalFileStore
from langchain.llms import HuggingFaceHub
from langchain.embeddings import HuggingFaceInstructEmbeddings
from datetime import datetime
import os
import tempfile
import requests # Import requests here
now = datetime.now()
underlying_embeddings = HuggingFaceEmbeddings()
def initialize_session_state():
if 'history' not in st.session_state:
st.session_state['history'] = []
if 'generated' not in st.session_state:
st.session_state['generated'] = ["Hello! Ask me anything about 🤗"]
if 'past' not in st.session_state:
st.session_state['past'] = ["Hey! 👋"]
def conversation_chat(query, chain, history):
result = chain({"question": query, "chat_history": history})
history.append((query, result["answer"]))
return result["answer"]
def cache_checker(question, question_cache, chain):
# Check if the response is already cached
logging.info("I'm here")
if question in question_cache:
response = question_cache[question]
logging.info("Response retrieved from cache.")
else:
# Perform the Q&A operation
response = chain({"question": question})
question_cache[question] = response["answer"]
logging.info("Response computed and cached.")
return response["answer"]
def display_chat_history(chain):
reply_container = st.container()
container = st.container()
question_cache = {}
with container:
with st.form(key='my_form', clear_on_submit=True):
user_input = st.text_input("Question:", placeholder="Ask about your PDF", key='input')
submit_button = st.form_submit_button(label='Send')
if submit_button and user_input:
with st.spinner('Generating response...'):
output = conversation_chat(user_input, chain, st.session_state['history'])
# Check if the question is being cached
if user_input:
if user_input in question_cache:
st.info("Response retrieved from cache.")
response = question_cache[user_input]
else:
st.info("Response computed.")
response = cache_checker(user_input, question_cache, chain)
question_cache[user_input] = response
# Display the response
st.write("Response:", response)
st.session_state['past'].append(user_input)
st.session_state['generated'].append(output)
if st.session_state['generated']:
with reply_container:
for i in range(len(st.session_state['generated'])):
message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="thumbs")
message(st.session_state["generated"][i], key=str(i), avatar_style="fun-emoji")
def create_conversational_chain(vector_store):
# Create llm
llm = LlamaCpp(
streaming=True,
model_path="mistral-7b-instruct-v0.1.Q2_K.gguf",
temperature=0.75,
top_p=1,
verbose=True,
n_ctx=4096
)
# llm = HuggingFaceHub(repo_id="HuggingFaceH4/zephyr-7b-beta", model_kwargs={
# "temperature": 0.75,
# "n_ctx": 4096,
# "streaming":True,
# "top_p": 0.99,
# "verbose": True,
# "max_length": 4096
# })
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
chain = ConversationalRetrievalChain.from_llm(llm=llm, chain_type='stuff',
retriever=vector_store.as_retriever(search_kwargs={"k": 2}),
memory=memory)
return chain
def main():
# Initialize session state
initialize_session_state()
st.title("Medbot :books:")
# Initialize Streamlit
st.sidebar.title("Document Processing")
uploaded_files = st.sidebar.file_uploader("Upload files", accept_multiple_files=True)
if uploaded_files:
text = []
for file in uploaded_files:
file_extension = os.path.splitext(file.name)[1]
with tempfile.NamedTemporaryFile(delete=False) as temp_file:
temp_file.write(file.read())
temp_file_path = temp_file.name
# Initialize cache store
cache_store = LocalFileStore("./cache/")
loader = None
if file_extension == ".pdf":
loader = PyPDFLoader(temp_file_path)
if loader:
text.extend(loader.load())
os.remove(temp_file_path)
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
text_chunks = text_splitter.split_documents(text)
# Create embeddings
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2",
model_kwargs={'device': 'cpu'})
# Create cache-backed embeddings
cached_embeddings = CacheBackedEmbeddings.from_bytes_store(embeddings, cache_store, namespace="embeddings")
# Cache the embeddings
#cache_store.save("embeddings", cached_embeddings)
# Create vector store
vector_store = FAISS.from_documents(text_chunks, embedding=cached_embeddings)
# Create the chain object
chain = create_conversational_chain(vector_store)
display_chat_history(chain)
if __name__ == "__main__":
main()
|