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 import altair as alt # Load environment variables load_dotenv() # Configure the Llama index 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 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'' 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", """created by vivek created for Neonflake Enterprises OPC Pvt Ltd 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("Built by [vivek](https://github.com/saravivek-cyber)") 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'])