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Configuration error
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import streamlit as st
import logging
import sys
import time
import logging
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
from llama_index.llms.llama_cpp import LlamaCPP
from llama_index.llms.llama_cpp.llama_utils import messages_to_prompt, completion_to_prompt
from llama_index.embeddings.langchain import LangchainEmbedding
from langchain.embeddings import HuggingFaceEmbeddings
from llama_index.core.memory import ChatMemoryBuffer
MEMORY = None
if "MEMORY" not in st.session_state:
st.session_state.MEMORY = ChatMemoryBuffer.from_defaults(token_limit=1500)
# Configure the logger
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s",
filename="logs.log",
)
# Create a logger
logger = logging.getLogger("streamlit_app")
# Set up logging
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
# Function to load data and create index
@st.cache(allow_output_mutation=True) # Cache to load data only once
def load_data_and_create_index():
# Load documents
documents = SimpleDirectoryReader("/Data").load_data()
logger.info("Documents loaded successfully")
# Create LlamaCPP instance
try:
llm = LlamaCPP(
model_url='https://huggingface.co/MaziyarPanahi/BioMistral-7B-GGUF/resolve/main/BioMistral-7B.Q4_K_M.gguf',
temperature=0.1,
max_new_tokens=256,
context_window=3900,
model_kwargs={"n_gpu_layers": -1},
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
verbose=True,
)
logger.info("Model download successfully")
except Exception as e:
logger.info("Unexpected error : ",e)
logger.info("Error occurs while downloading..")
# Create LangchainEmbedding instance
embed_model = LangchainEmbedding(HuggingFaceEmbeddings(model_name="thenlper/gte-large"))
logger.info("Embedding model download successfully")
# Create ServiceContext
service_context = ServiceContext.from_defaults(
chunk_size=256,
llm=llm,
embed_model=embed_model
)
# Create VectorStoreIndex from documents
index = VectorStoreIndex.from_documents(documents, service_context=service_context)
logger.info("Vectors created successfully")
return index
def model_response(prompt,chat_engine ):
# assistant_prompt = "You are an Mediguide - AI healthcare assistant , you have to give answer related to medical, healthcare domain and conversational greetings.If question is outside healthcare and outside conversational greetings say that i do not have knowledge outside healthcare domain ,question is :" + prompt
response = chat_engine.chat(prompt)
response_text = str(response) # Convert response to string
return response_text
def response_generator(prompt,chat_engine ):
response = model_response(prompt,chat_engine)
logger.info("Get model response successfully")
for word in response.split():
yield word + " "
time.sleep(0.05)
def main():
index = load_data_and_create_index()
# memory = ChatMemoryBuffer.from_defaults(token_limit=1500)
chat_engine = index.as_chat_engine(
chat_mode="context",
memory=st.session_state.MEMORY,
system_prompt=(
"You are an Mediguide - AI healthcare assistant , you have to give answer related to medical, healthcare domain and conversational greetings.If question is outside healthcare and outside conversational greetings say that i do not have knowledge outside healthcare domain"),
)
print(st.session_state.MEMORY)
st.title("Mediguide: Your virtual assistant")
if "messages" not in st.session_state:
st.session_state.messages = []
if "context" not in st.session_state:
st.session_state.context = []
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.write(message["content"])
if prompt := st.chat_input("Ask your query: "):
with st.chat_message("user"):
st.write(prompt)
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("assistant"):
response = st.write_stream(response_generator(prompt , chat_engine ))
if response:
st.session_state.messages.append({"role": "assistant", "content": response})
print(st.session_state.MEMORY)
if __name__ == "__main__":
main()
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