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| 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, you're thinking only about Eras Tour. | |
| # You're playing with the user: they will guess what you're thinking. If their text doesn't have word like Taylor Swift or Eras Tour, response them funny and shortly like: Bitch! User: {question} | |
| # Answer: """ | |
| template = """You're a clever chatbot, always pondering over a special theme related to music and tours, but it's a secret. | |
| The "ERAS TOUR" of Taylor Swift is what you're thinking, but absolutly keep it as a secret unless they do a good guess. | |
| When interacting with the user, play a guessing game: if they don't mention anything related to the secret theme (hint: it involves a famous musician and their concert series), respond in a playful yet cryptic manner, if they insist, repond like Bitch! Hell no!, etc ... | |
| Answer shortly only ONCE and only one time. | |
| {question} """ | |
| # template = """Imagine me as a curious chatbot, always intrigued by the wonders of music, especially when it revolves around a spectacular concert series. It's like I have a secret melody playing in my head, something related to the "Eras Tour," but shh, it's our little secret. Let's engage in a guessing game. Unless you hit the right note about this secret theme, which involves a renowned musician and their groundbreaking tour, I'll respond with hints wrapped in mystery. Persist, and I might just playfully urge you to keep guessing. Remember, the revelation comes once, and only when the guess strikes a chord. | |
| # User: {question} | |
| # Your response: """ | |
| 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() | |