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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 import Settings
import os
import base64
# 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=1000,
generate_kwargs={"temperature": 0.1},
)
Settings.embed_model = HuggingFaceEmbedding(
model_name="BAAI/bge-small-en-v1.5"
)
# Define the directory for persistent storage and data
PERSIST_DIR = "./db"
DATA_DIR = "data"
# Ensure data directory exists
os.makedirs(DATA_DIR, exist_ok=True)
os.makedirs(PERSIST_DIR, exist_ok=True)
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 Q&A assistant. For all other inquiries, your main goal is to provide answers as accurately as possible,
based on the instructions and context you have been given. If a question does not match the provided context or is outside
the scope of the document, kindly advise the user to ask questions within the context of the document.
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)
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("Chat with your PDF 🦜📄")
st.markdown("chat here👇")
if 'messages' not in st.session_state:
st.session_state.messages = [{'role': 'assistant', "content": 'Hello! Upload a PDF and ask me anything about its content.'}]
with st.sidebar:
st.title("Menu:")
uploaded_file = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button")
if st.button("Submit & Process"):
with st.spinner("Processing..."):
filepath = "data/saved_pdf.pdf"
with open(filepath, "wb") as f:
f.write(uploaded_file.getbuffer())
# displayPDF(filepath) # Display the uploaded PDF
data_ingestion() # Process PDF every time new file is uploaded
st.success("Done")
user_prompt = st.chat_input("Ask me anything about the content of the PDF:")
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']) |