from langchain.chains import LLMChain
from langchain.llms import HuggingFaceHub
from langchain.prompts import PromptTemplate
import streamlit as st
import json

# Load existing ideas from a file
def load_ideas():
    try:
        with open("ideas.json", "r") as file:
            ideas = json.load(file)
    except FileNotFoundError:
        ideas = []
    return ideas

# Save ideas to a file
def save_ideas(ideas):
    with open("ideas.json", "w") as file:
        json.dump(ideas, file)


topic = st.text_input("Enter Topic for the bog")
button_clicked = st.button("Create blog!")

existing_ideas = load_ideas()
st.sidebar.header("Previous Ideas:")
selected_idea = st.sidebar.selectbox("Select Idea", existing_ideas, key="selectbox")
st.markdown(selected_idea)

if button_clicked:

  hub_llm = HuggingFaceHub(repo_id ="HuggingFaceH4/zephyr-7b-beta")
  prompt = PromptTemplate(
      input_variables = ['keyword'],
      template = """
      Write a comprehensive article about {keyword} covering the following aspects:
      Introduction, History and Background, Key Concepts and Terminology, Use Cases and Applications, Benefits and Drawbacks, Future Outlook, Conclusion
      Ensure that the article is well-structured, informative, and at least 1500 words long. Use SEO best practices for content optimization.
      """)
  hub_chain = LLMChain(prompt=prompt,llm = hub_llm,verbose=True)

  content = hub_chain.run(topic)


  subheadings = [
          "Introduction:",
          "History and Background:",
          "Key Concepts and Terminology:",
          "Use Cases and Applications:",
          "Benefits and Drawbacks:",
          "Future Outlook:",
          "Conclusion:",
      ]
  organized_content = ""

  for subheading in subheadings:
      if subheading in content:
          content = content.replace(subheading,"## "+subheading+"\n")

  existing_ideas.append({keyword:content})
  save_ideas(existing_ideas)
  selected_idea = st.sidebar.selectbox("Select Idea", existing_ideas, key="selectbox")
  st.markdown(selected_idea)