| from langchain_community.llms import HuggingFaceEndpoint | |
| from langchain.prompts import PromptTemplate | |
| from langchain.schema import AIMessage, HumanMessage | |
| from langchain.chains import LLMChain | |
| import gradio as gr | |
| import os | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| repo_id = "mistralai/Mistral-7B-Instruct-v0.2" | |
| llm = HuggingFaceEndpoint( | |
| repo_id = repo_id, | |
| # huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN, | |
| ) | |
| template = """You're a good chatbot. Answer this request: {question} | |
| Answer: Let's think step by step.""" | |
| prompt = PromptTemplate.from_template(template=template) | |
| llm_chain = LLMChain(llm=llm, prompt=prompt) | |
| def predict(message, history): | |
| history_langchain_format = [] | |
| # for human, ai in history: | |
| # history_langchain_format.append(HumanMessage(content=human)) | |
| # history_langchain_format.append(AIMessage(content=ai)) | |
| # history_langchain_format.append(HumanMessage(content=message)) | |
| # gpt_response = llm(history_langchain_format) | |
| response = llm_chain.invoke(message)['text'] | |
| return response | |
| gr.ChatInterface(predict).launch() | |