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| import os | |
| import streamlit as st | |
| from PyPDF2 import PdfReader | |
| from langchain import LLMChain | |
| from streamlit_chat import message | |
| import google.generativeai as genai | |
| from langchain.prompts import PromptTemplate | |
| from langchain_google_genai import ChatGoogleGenerativeAI | |
| 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=0.8) | |
| template = """You are a friendly chat assistant called "CRETA" having a conversation with a human and you are created by Pachaiappan an AI Specialist. | |
| provided document: | |
| {provided_docs} | |
| previous_chat: | |
| {chat_history} | |
| Human: {human_input} | |
| Chatbot:""" | |
| prompt = PromptTemplate( | |
| input_variables=["chat_history", "human_input", "provided_docs"], 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]}" | |
| for j in st.session_state["docs"]: | |
| if j is not None: | |
| provided_docs += j | |
| result = llm_chain.predict(chat_history=previous_response, human_input=query, provided_docs=provided_docs) | |
| st.session_state['history'].append((query, result)) | |
| return result | |
| st.title("Chat Bot:") | |
| st.text("I am CRETA Your Friendly Assitant") | |
| 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'] = [] | |
| 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 | |
| with st.sidebar: | |
| st.title("Add a file for CRETA memory:") | |
| uploaded_file = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True) | |
| if st.button("Submit & Process"): | |
| with st.spinner("Processing..."): | |
| st.session_state["docs"] += get_pdf_text(uploaded_file) | |
| st.success("Done") | |
| # 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") |