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| import asyncio | |
| import streamlit as st | |
| from langchain.llms import HuggingFaceHub | |
| from langchain.chains import ConversationChain | |
| import os | |
| from langchain.chains.conversation.memory import ConversationBufferMemory | |
| from langchain.chains.conversation.memory import ConversationSummaryBufferMemory | |
| from dotenv import load_dotenv | |
| import os | |
| load_dotenv() | |
| st.sidebar.title("Welcome Wanderers", help='This is just a beta model, and is still in progress!!!') | |
| # Add an image to the sidebar | |
| st.sidebar.image("assets/chatbot.jpg") | |
| st.sidebar.divider() | |
| # Create a sidebar dropdown | |
| selected_option = st.sidebar.selectbox("Select Model:", ["lmsys/fastchat-t5-3b-v1.0", "google/flan-t5-base"]) | |
| # Display the selected option below the dropdown | |
| # st.sidebar.write("Model : ", selected_option) | |
| st.sidebar.divider() | |
| max_length = st.sidebar.slider("Max Length", value=132, min_value=32, max_value=250) | |
| temperature = st.sidebar.slider("Temperature", value=0.60, min_value=0.0, max_value=1.0, step=0.05) | |
| repo_id = selected_option | |
| llm = HuggingFaceHub( | |
| huggingfacehub_api_token=os.getenv('HUGGING_FACE_HUB_API_KEY'), | |
| repo_id=repo_id, | |
| model_kwargs={ | |
| 'temperature': temperature, | |
| 'max_length': max_length, | |
| } | |
| ) | |
| memory = ConversationSummaryBufferMemory(llm=llm, max_token_limit=80) | |
| Conversation_buf = ConversationChain( | |
| llm=llm, | |
| memory=memory | |
| ) | |
| st.markdown("<h1 style='text-align: center;'>Chat Application 🚀🤖</h1>", unsafe_allow_html=True) | |
| st.divider() | |
| default_value = "This is just a small Chat application, 🤗 This site, \nbuilt by the Me using HuggingFace Models, Its like having a smart machine that \ncompletes your thoughts 😀 Get started by typing a custom snippet, check out the \nrepository, or try one of the examples. Have fun!" | |
| st.text(default_value) | |
| st.text('Note : The max response token will be only 250 characters.') | |
| st.divider() | |
| # Create a placeholder for the conversation history | |
| conversation_history_placeholder = st.empty() | |
| # Create a list to store the conversation history | |
| conversation_history = [] | |
| user_input = st.text_input("Your Query", max_chars=2024) | |
| if st.button("Predict"): | |
| # Append user input to the conversation history | |
| conversation_history.insert(0 ,f"User: {user_input}") | |
| # Await the coroutine to get the actual text | |
| prediction = asyncio.run(Conversation_buf.acall(inputs=user_input)) | |
| keys_list = list(prediction.items()) | |
| keys = keys_list[2] | |
| response = keys[1][5:] | |
| # Append model response to the conversation history | |
| conversation_history.insert(1, f"Mr.Zhongli: {response}") | |
| # Update the conversation history placeholder | |
| #conversation_history_placeholder.text_area("Conversation...", "\n".join(conversation_history), height=200) | |
| st.subheader('_Response_ :blue[here] :sunglasses:') | |
| st.write(response) | |
| # st.text(memory.buffer) | |