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| import streamlit as st | |
| import os | |
| from langchain_core.prompts import ChatPromptTemplate,MessagesPlaceholder | |
| from langchain_google_genai import ChatGoogleGenerativeAI | |
| from langchain.memory import ConversationBufferWindowMemory | |
| from operator import itemgetter | |
| from langchain_core.runnables import RunnableLambda, RunnablePassthrough | |
| os.environ['GOOGLE_API_KEY'] = "AIzaSyClJ9WDy55XBfYGhlXXDjZUdd9H_moqi4c" | |
| prompt = ChatPromptTemplate.from_messages( | |
| [ | |
| ('system', 'you are a good assistant.'), | |
| MessagesPlaceholder(variable_name='history'), | |
| ("human", "{input}") | |
| ] | |
| ) | |
| if 'memory' not in st.session_state: | |
| st.session_state.memory = ConversationBufferWindowMemory(k=10, return_messages=True) | |
| chain = (RunnablePassthrough.assign(history=RunnableLambda(st.session_state.memory.load_memory_variables) | itemgetter("history")) | | |
| prompt | ChatGoogleGenerativeAI(model='gemini-pro', temperature=0, max_output_tokens=500, convert_system_message_to_human=True)) | |
| def home(): | |
| st.header('Interactive Chatbot') | |
| st.write('''An interactive chatbot is designed to engage in dynamic, back-and-forth conversations with users. | |
| These chatbots can understand and retain context from previous interactions, making their responses more | |
| relevant and coherent as the conversation progresses. Interactive chatbots often use advanced natural language | |
| processing (NLP) techniques and memory management to provide a more human-like experience. They are commonly used | |
| in applications where ongoing interaction and context awareness are crucial, such as customer support, virtual | |
| assistants, and personalized recommendations.''') | |
| st.header('Non-Interactive Chatbot') | |
| st.write('''A non-interactive chatbot, on the other hand, is designed for more straightforward, single-turn interactions. | |
| These chatbots do not retain context from previous interactions, meaning each user query is treated independently. | |
| Non-interactive chatbots are typically used for simple, transactional tasks where context is not required. They are | |
| easier to develop and deploy and are suitable for scenarios where the interaction is brief and to the point.''') | |
| def page1(): | |
| st.title("Interactive Chatbot") | |
| if 'user_input' not in st.session_state: | |
| st.session_state.user_input = "" | |
| user_input = st.text_area("User: ", st.session_state.user_input, height=100) | |
| if st.button("Submit"): | |
| response = chain.invoke({"input": user_input}) | |
| st.write(f"Assistant: {response.content}") | |
| st.session_state.memory.save_context({"input": user_input}, {"output": response.content}) | |
| st.session_state.user_input = "" # Clear the input box | |
| if st.checkbox("Show Chat History"): | |
| chat_history = st.session_state.memory.load_memory_variables({}) | |
| st.write(chat_history) | |
| def page2(): | |
| st.title("Non Interactive Chatbot") | |
| if 'user_input' not in st.session_state: | |
| st.session_state.user_input = "" | |
| user_input = st.text_area("User: ", st.session_state.user_input, height=100) | |
| if st.button("Submit"): | |
| response = chain.invoke({"input": user_input}) | |
| st.write(f"Assistant: {response.content}") | |
| st.session_state.memory.save_context({"input": user_input}, {"output": response.content}) | |
| st.session_state.user_input = "" # Clear the input box | |
| if st.checkbox("Show Chat History"): | |
| chat_history = st.session_state.memory.load_memory_variables({}) | |
| st.write(chat_history) | |
| # Sidebar navigation | |
| st.sidebar.title("Navigation") | |
| page = st.sidebar.radio("Go to", ("Home", "Page 1", "Page 2")) | |
| # Page rendering | |
| if page == "Home": | |
| home() | |
| elif page == "Page 1": | |
| page1() | |
| elif page == "Page 2": | |
| page2() |