| import streamlit as st |
| import os |
| from langchain_huggingface import HuggingFaceEndpoint |
| from langchain.chains import LLMChain |
| from langchain.prompts import PromptTemplate |
| from langchain.chains import SequentialChain |
| from langchain import HuggingFaceHub |
|
|
| hf_token = os.getenv("HF_TKN") |
|
|
| model = HuggingFaceHub( |
| repo_id='google/flan-t5-large', |
| model_kwargs={"temperature":0.4, "max_length":180, 'max_new_tokens' : 240}, |
| huggingfacehub_api_token=hf_token |
| ) |
|
|
| st.title('Resturant Name Generator') |
| def generate_rest_name_and_items(cuisine): |
| template_name = PromptTemplate(input_variables=['cuisine'], |
| template = "I want to open a resturant for {cuisine} food. Suggest a fancy name for this." |
| ) |
| chain_1 = LLMChain(llm = model, |
| prompt = template_name, |
| output_key = "rest_name") |
|
|
| template_items = PromptTemplate(input_variables=['rest_name'], |
| template = 'Suggest at least 5 menu items for {rest_name}. Return it as a comma separated string' |
| ) |
| chain_2 = LLMChain(llm = model, |
| prompt = template_items, |
| output_key = "menu_items") |
|
|
| s_chain = SequentialChain(chains = [chain_1,chain_2], |
| input_variables = ['cuisine'], |
| output_variables = ['rest_name','menu_items']) |
|
|
| response = s_chain({'cuisine':cuisine}) |
| return response |
| |
| cuisine = st.sidebar.selectbox('Pick a cuisine',('Indian','Italian','Mexican','Arabic','American')) |
| if cuisine: |
| response = generate_rest_name_and_items(cuisine) |
| st.header(response['rest_name']) |
| items = response['menu_items'].split(",") |
|
|
| st.write('**Menu Items**') |
| for item in items: |
| st.write(">" ,item) |
