import streamlit as st import os from streamlit_chat import message from PyPDF2 import PdfReader import bs4 import google.generativeai as genai from langchain.prompts import PromptTemplate from langchain import LLMChain from langchain_google_genai import ChatGoogleGenerativeAI import nest_asyncio from langchain.document_loaders import WebBaseLoader nest_asyncio.apply() os.environ["GOOGLE_API_KEY"] = os.getenv("GOOGLE_API_KEY") genai.configure(api_key=os.environ["GOOGLE_API_KEY"]) llm = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.4) template = """You are "CRETA," a friendly chatbot developed by Suriya, an AI enthusiast. Your role is to engage in clear and informative conversations with users, providing helpful responses based on the information extracted from URLs, documents, and previous chat interactions. Extracted URL text: {extracted_text} Provided document: {provided_docs} Previous Chat history: {chat_history} Human: {human_input} Chatbot:""" prompt = PromptTemplate( input_variables=["chat_history", "human_input", "provided_docs","extracted_text"], template=template ) llm_chain = LLMChain( llm=llm, prompt=prompt, verbose=True, ) previous_response = "" provided_docs = "" def conversational_chat(query): global previous_response, provided_docs for i in st.session_state['history']: if i is not None: previous_response += f"Human: {i[0]}\n Chatbot: {i[1]}" docs = "" for j in st.session_state["docs"]: if j is not None: docs += j ex_text = st.session_state["extracted_text"] provided_docs = docs result = llm_chain.predict(chat_history=previous_response, human_input=query, provided_docs=provided_docs,extracted_text=ex_text) st.session_state['history'].append((query, result)) return result st.title("Chat Bot:") st.text("I am CRETA Your Friendly Assitant") st.markdown("Built by [Suriya❤️](https://github.com/theSuriya)") if 'history' not in st.session_state: st.session_state['history'] = [] # Initialize messages if 'generated' not in st.session_state: st.session_state['generated'] = ["Hello ! Ask me anything"] if 'past' not in st.session_state: st.session_state['past'] = [" "] if 'docs' not in st.session_state: st.session_state['docs'] = [] if "extracted_text" not in st.session_state: st.session_state["extracted_text"] = "" def get_pdf_text(pdf_docs): text = "" for pdf in pdf_docs: pdf_reader = PdfReader(pdf) for page in pdf_reader.pages: text += page.extract_text() return text def get_url_text(url_link): website_url = url_link loader = WebBaseLoader(website_url) loader.requests_per_second = 1 docs = loader.aload() extracted_text = "" for page in docs: extracted_text+=page.page_content return extracted_text with st.sidebar: st.title("Add a file for CRETA memory:") uploaded_files = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True) uploaded_url = st.text_area("Please upload a URL:") if st.button("Submit & Process"): if uploaded_files or uploaded_url: with st.spinner("Processing..."): if uploaded_files: pdf_text = get_pdf_text(uploaded_files) st.session_state["docs"] += get_pdf_text(uploaded_files) if uploaded_url: url_text = get_url_text(uploaded_url) st.session_state["extracted_text"] += get_url_text(uploaded_url) st.success("Processing complete!") else: st.error("Please upload at least one PDF file or provide a URL.") # Create containers for chat history and user input response_container = st.container() container = st.container() # User input form user_input = st.chat_input("Ask Your Questions 👉..") with container: if user_input: output = conversational_chat(user_input) # answer = response_generator(output) st.session_state['past'].append(user_input) st.session_state['generated'].append(output) # Display chat history if st.session_state['generated']: with response_container: for i in range(len(st.session_state['generated'])): if i != 0: message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="adventurer") message(st.session_state["generated"][i], key=str(i), avatar_style="bottts")