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import os | |
import streamlit as st | |
from dotenv import dotenv_values | |
from langchain.prompts import PromptTemplate | |
from langchain.chains import RetrievalQA, LLMChain | |
from langchain.document_loaders import TextLoader | |
from langchain.embeddings import HuggingFaceEmbeddings | |
from langchain.llms import HuggingFaceEndpoint | |
from langchain.text_splitter import CharacterTextSplitter | |
from langchain.vectorstores import Chroma | |
env_vars = dotenv_values(".env") | |
# prepare Falcon Huggingface API | |
llm = HuggingFaceEndpoint( | |
endpoint_url= f"https://api-inference.huggingface.co/models/{env_vars['HUGGINGFACE_MODEL']}", | |
huggingfacehub_api_token=env_vars["HUGGINGFACE_API_TOKEN"], | |
task="text-generation", | |
model_kwargs = { | |
"min_length": 8192, | |
"max_length":8192, | |
"temperature":0.9, | |
"max_new_tokens":4000, | |
"num_return_sequences":1 | |
} | |
) | |
keyword_prompt_template = "What are 10 important keywords related too: {word}? Only return a list of words and do not include any duplicates." | |
keyword_chain = LLMChain( | |
llm=llm, | |
prompt=PromptTemplate.from_template(keyword_prompt_template), | |
) | |
outline_prompt_template = "Create an outline for an article about: {word}." | |
outline_chain = LLMChain( | |
llm=llm, | |
prompt=PromptTemplate.from_template(outline_prompt_template), | |
) | |
article_prompt_template = """ | |
Act as an expert SEO Writer. | |
Write a well crafted article using the given outline as a guide. | |
Use the relavant keywords you are provided. | |
Apply EEAT principles and SEO best practices. | |
It is important that the content is at least 1500 words. | |
Be sure to include section headers. | |
OUTLINE: {outline} | |
KEYWORDS: {keywords} | |
""" | |
article_chain = LLMChain( | |
llm=llm, | |
prompt=PromptTemplate.from_template(article_prompt_template), | |
) | |
st.title("Blog Writer") | |
keyword = st.text_input("Input the keyword you wish to write about") | |
if keyword: | |
with st.spinner("Writing..."): | |
st.write(f"Writing about: {keyword}") | |
with st.expander("Keywords"): | |
keywords = keyword_chain.run(keyword) | |
st.write(keywords) | |
with st.expander("Outline"): | |
outline = outline_chain.run(keyword) | |
st.write(outline) | |
with st.expander("Article"): | |
article = article_chain.run(outline=outline, keywords=keywords) | |
st.write(article) |