app.py

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 LLM and Embedding settings
Settings.llm = HuggingFaceInferenceAPI(
    model_name="google/gemma-1.1-7b-it",
    tokenizer_name="google/gemma-1.1-7b-it",
    context_window=3000,
    token=os.getenv("HF_TOKEN"),
    max_new_tokens=512,
    generate_kwargs={"temperature": 0.1},
)
Settings.embed_model = HuggingFaceEmbedding(
    model_name="BAAI/bge-small-en-v1.5"
)

# Define directory paths
PERSIST_DIR = "./db"
DATA_DIR = "data"

# Create directories if they don't exist
os.makedirs(DATA_DIR, exist_ok=True)
os.makedirs(PERSIST_DIR, exist_ok=True)

def display_pdf(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 ingest_data():
    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 chatbot created by Prateek Mohan. Your main goal is to provide accurate answers based on the given context. If a question is outside the scope of the document, kindly advise the user to ask within the 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)
    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
st.title("Talk to your  PDF")
st.markdown("by Prateek Mohan (https://github.com/prtkmhn/)")

if 'messages' not in st.session_state:
    st.session_state.messages = [{'role': 'system', "content": 'Chat to PDF'}]

with st.sidebar:
    st.title("Menu:")
    uploaded_file = st.file_uploader("Upload and Click Submit")
    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())
            ingest_data() 
            st.success("Done")

user_prompt = st.chat_input("Query")
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:
    st.write(message['content'])