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| from langchain_community.chat_message_histories import StreamlitChatMessageHistory | |
| import streamlit as st | |
| from langchain.prompts import ( | |
| ChatPromptTemplate, | |
| HumanMessagePromptTemplate, | |
| MessagesPlaceholder, | |
| ) | |
| from more_itertools import chunked | |
| from langserve import RemoteRunnable | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| import os | |
| from langchain import PromptTemplate | |
| from langchain import LLMChain | |
| from langchain_together import Together | |
| import re | |
| import pdfplumber | |
| # Set the API key with double quotes | |
| os.environ['TOGETHER_API_KEY'] = "5653bbfbaf1f7c1438206f18e5dfc2f5992b8f0b6aa9796b0131ea454648ccde" | |
| text = "" | |
| max_pages = 16 | |
| with pdfplumber.open("AI Engineer Test.pdf") as pdf: | |
| for i, page in enumerate(pdf.pages): | |
| if i >= max_pages: | |
| break | |
| text += page.extract_text() + "\n" | |
| def Bot(Questions): | |
| chat_template = """ | |
| Based on the provided context: {text} | |
| Please answer the following question: {Questions} | |
| Only provide answers that are directly related to the context. If the question is unrelated, respond with "I don't know". | |
| """ | |
| prompt = PromptTemplate( | |
| input_variables=['text', 'Questions'], | |
| template=chat_template | |
| ) | |
| llama3 = Together(model="meta-llama/Llama-3-70b-chat-hf", max_tokens=250) | |
| Generated_chat = LLMChain(llm=llama3, prompt=prompt) | |
| def ChatBot(Questions): | |
| greetings = ["hi", "hello", "hey", "greetings", "what's up", "howdy"] | |
| # Check if the input question is a greeting | |
| question_lower = Questions.lower().strip() | |
| if question_lower in greetings or any(question_lower.startswith(greeting) for greeting in greetings): | |
| return "Hello! How can I assist you with the document today?" | |
| else: | |
| response=Bot(Questions) | |
| return response.translate(str.maketrans('', '', '\n')) | |
| # --- Logo --- | |
| st.set_page_config( | |
| page_title="AI Engineer Test Chatbot", | |
| page_icon="Insight Therapy Solutions.png", | |
| layout="wide", | |
| ) | |
| st.sidebar.image("Insight Therapy Solutions.png", width=200) | |
| st.sidebar.title("Navigation") | |
| st.sidebar.write("Reclaim Your Mental Health") | |
| st.sidebar.markdown("[Visit us at](https://www.insighttherapysolutions.com/)") | |
| #rag_chain = RemoteRunnable("http://69.61.24.171:8000/rag_chain/") | |
| #msgs = StreamlitChatMessageHistory(key="langchain_messages") | |
| # --- Main Content --- | |
| st.markdown("## 🔍 Chatbot For AI Engineer test:") | |
| if len(msgs.messages) == 0: | |
| msgs.add_ai_message("Hi! How can I assist you today?") | |
| for msg in msgs.messages: | |
| st.chat_message(msg.type).write(msg.content) | |
| if prompt := st.chat_input(): | |
| st.chat_message("human").write(prompt) | |
| with st.chat_message("assistant"): | |
| message_placeholder = st.empty() | |
| full_response = "" | |
| try: | |
| _chat_history = st.session_state.langchain_messages[1:40] | |
| _chat_history_tranform = list( | |
| chunked([msg.content for msg in _chat_history], n=2) | |
| ) | |
| response = rag_chain.stream( | |
| {"question": prompt, "chat_history": _chat_history_tranform} | |
| ) | |
| for res in response: | |
| full_response += res or "" | |
| message_placeholder.markdown(full_response + "|") | |
| message_placeholder.markdown(full_response) | |
| msgs.add_user_message(prompt) | |
| msgs.add_ai_message(full_response) | |
| except Exception as e: | |
| st.error(f"An error occured. {e}") |