File size: 1,379 Bytes
bf7287c 59b1384 bf7287c 9c24a51 bf7287c ebe2a01 bf7287c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | import streamlit as st
from langchain.prompts import PromptTemplate
from langchain.llms import CTransformers
from langchain import HuggingFaceHub
import os
from dotenv import load_dotenv
load_dotenv()
import warnings
warnings.filterwarnings('ignore')
#streamlit run app.py
def get_llama_response(inputtext,wordcount,blogstyle):
template = f"""
Write a Blog as a {blogstyle} on {inputtext} within {wordcount} words.
"""
llm_hugg = HuggingFaceHub(repo_id="google/flan-t5-large",model_kwargs={'temperature':0.6, "max_length":64})
promptTemp = PromptTemplate(input_variables=['blogstyle','inputtext','wordcount'],
template=template)
response = llm_hugg(promptTemp.format(blogstyle=blogstyle,inputtext=inputtext,wordcount=wordcount))
return response
st.set_page_config(page_title='Blog Generation',page_icon="🧊",layout="centered",initial_sidebar_state="collapsed")
st.header("Generate Blogs")
input_text = st.text_input("Enter the Blog Topic")
col1,col2 = st.columns([5,5])
with col1:
num_words = st.text_input("No Of Words")
with col2:
blog_style = st.selectbox("Writing the Blog For," ,("Researchers","Data Scientist","Common People"),index=0)
submit =st.button("Generate")
if submit:
st.subheader("The Response is:")
st.write(get_llama_response(input_text,num_words,blog_style))
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