| import streamlit as st | |
| import os | |
| from langchain import PromptTemplate, HuggingFaceHub, LLMChain | |
| x = st.slider('Select a value') | |
| st.write(x, 'squared is', x * x) | |
| # https://cobusgreyling.medium.com/langchain-creating-large-language-model-llm-applications-via-huggingface-192423883a74 | |
| # !pip install langchain[all] | |
| template = """Question: {question}""" | |
| HUGGING_FACE_API_KEY= os.environ.get("HUGGING_FACE_API_KEY") | |
| Answer= "Let's think step by step." | |
| prompt = PromptTemplate(template=template, input_variables=["question"]) | |
| llm=HuggingFaceHub(repo_id="Writer/palmyra-small", model_kwargs={"temperature":1e-10},huggingfacehub_api_token=HUGGING_FACE_API_KEY) | |
| question=st.text_input("Input the question",'') | |
| # question = "When was Google founded?" | |
| # print(llm.run(question)) | |
| if question: | |
| d=LLMChain(llm=llm,prompt=prompt) | |
| st.write("out",d.run(question)) | |