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import streamlit as st
from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate
from llama_index.llms.huggingface import HuggingFaceInferenceAPI
from dotenv import load_dotenv
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core.memory import ChatMemoryBuffer
from llama_index.core import Settings
import os
import base64
import datetime
# Load environment variables
load_dotenv()
# Configure the Llama index settings
Settings.llm = HuggingFaceInferenceAPI(
model_name="meta-llama/Meta-Llama-3-8B-Instruct",
tokenizer_name="meta-llama/Meta-Llama-3-8B-Instruct",
context_window=3900,
token=os.getenv("HF_TOKEN"),
max_new_tokens=1024,
generate_kwargs={"temperature": 0.1},
)
Settings.embed_model = HuggingFaceEmbedding(
model_name="BAAI/bge-large-en-v1.5"
)
# Declare directory's for data and persistent storage
PERSIST_DIR = "./db"
DATA_DIR = "data"
# Ensure data directory exists
os.makedirs(DATA_DIR, exist_ok=True)
os.makedirs(PERSIST_DIR, exist_ok=True)
# Here, a memory token limit of 1500 is set
memory = ChatMemoryBuffer.from_defaults(token_limit=1500)
def displayPDF(file):
with open(file, "rb") as f:
base64_pdf = base64.b64encode(f.read()).decode('utf-8')
pdf_display = f'<iframe src="data:application/pdf;base64,{base64_pdf}" width="100%" height="600" type="application/pdf"></iframe>'
st.markdown(pdf_display, unsafe_allow_html=True)
def data_ingestion():
documents = SimpleDirectoryReader(DATA_DIR).load_data()
storage_context = StorageContext.from_defaults()
index = VectorStoreIndex.from_documents(documents)
index.storage_context.persist(persist_dir=PERSIST_DIR)
def handle_query(query):
storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
index = load_index_from_storage(storage_context)
chat_text_qa_msgs = [
(
"user",
"""You are a Q&A assistant. Created by Abraham Paul [linkedin](https://www.linkedin.com/in/abraham-paul-16317a235/) a Software / AI Engineer.
Your primary objective is to provide accurate and helpful answers based on the instructions and context provided.
If a question falls outside the given context or scope, kindly guide the user to ask questions that align with the provided context.
Context:
{context_str}
Question:
{query_str}
"""
)
]
text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
# query_engine = index.as_query_engine(text_qa_template=text_qa_template, memory=memory)
query_engine = index.as_query_engine(text_qa_template=text_qa_template)
answer = query_engine.query(query)
if hasattr(answer, 'response'):
return answer.response
elif isinstance(answer, dict) and 'response' in answer:
return answer['response']
else:
return "Sorry, I couldn't find an answer."
# Streamlit app initialization
st.title("Get insights from your data!👇")
if 'messages' not in st.session_state:
st.session_state.messages = [{'role': 'assistant', "content": 'Upload your pdf doc and ask me anything about it, Lets chat!!'}]
with st.sidebar:
st.markdown("# Chat with your Doc")
st.markdown("**Created by [Abraham](https://www.linkedin.com/in/abraham-paul-16317a235/)**")
st.title(':blue[Get Started]:')
uploaded_file = st.file_uploader("Upload your PDF and Click Submit")
if st.button("Submit"):
with st.spinner("Processing..."):
filepath = "data/saved_pdf.pdf"
with open(filepath, "wb") as f:
f.write(uploaded_file.getbuffer())
data_ingestion() # Process PDF every time new file is uploaded
st.success("Done")
user_prompt = st.chat_input("Ask me anything from the uploaded document:")
if user_prompt:
st.session_state.messages.append({'role': 'user', "content": user_prompt})
response = handle_query(user_prompt)
st.session_state.messages.append({'role': 'assistant', "content": response})
for message in st.session_state.messages:
with st.chat_message(message['role']):
st.write(message['content'])
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