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Runtime error
| from langchain.agents import ConversationalChatAgent, AgentExecutor | |
| from langchain.callbacks import StreamlitCallbackHandler | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain import HuggingFaceHub | |
| from langchain.memory import ConversationBufferMemory | |
| from langchain.memory.chat_message_histories import StreamlitChatMessageHistory | |
| from langchain.chains import LLMChain, RetrievalQA | |
| from langchain import PromptTemplate | |
| import streamlit as st | |
| import os | |
| import dotenv | |
| dotenv.load_dotenv() | |
| HUGGINGFACE_API = os.getenv("HUGGINGFACE_API") | |
| st.set_page_config(page_title="ChatBot", page_icon="😊") | |
| st.title("ChatBot") | |
| msgs = StreamlitChatMessageHistory() | |
| memory = ConversationBufferMemory( | |
| chat_memory=msgs, return_messages=True, memory_key="chat_history", output_key="output" | |
| ) | |
| if len(msgs.messages) == 0: | |
| msgs.clear() | |
| msgs.add_ai_message("How can I help you?") | |
| st.session_state.steps = {} | |
| avatars = {"human": "user", "ai": "assistant"} | |
| for idx, msg in enumerate(msgs.messages): | |
| with st.chat_message(avatars[msg.type]): | |
| # Render intermediate steps if any were saved | |
| for step in st.session_state.steps.get(str(idx), []): | |
| if step[0].tool == "_Exception": | |
| continue | |
| with st.expander(f"✅ **{step[0].tool}**: {step[0].tool_input}"): | |
| st.write(step[0].log) | |
| st.write(f"**{step[1]}**") | |
| st.write(msg.content) | |
| if prompt := st.chat_input(placeholder="Who won the Women's U.S. Open in 2018?"): | |
| st.chat_message("user").write(prompt) | |
| msgs.add_user_message(prompt) | |
| llm = HuggingFaceHub( | |
| repo_id="tiiuae/falcon-7b-instruct", | |
| model_kwargs={"temperature": 0.5, "max_new_tokens": 500}, | |
| huggingfacehub_api_token=HUGGINGFACE_API, | |
| ) | |
| prompt_template = PromptTemplate.from_template( | |
| "Answer the question: {prompt}" | |
| ) | |
| qa_chain = LLMChain(llm = llm, prompt = prompt_template) | |
| with st.chat_message("assistant"): | |
| st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False) | |
| response = qa_chain({"prompt": prompt}) | |
| msgs.add_ai_message(response["text"]) | |
| st.write(response["text"]) | |