from langchain.chains import LLMChain
from langchain_community.llms import OpenAI
from langchain_core.prompts import PromptTemplate
import streamlit as st

# Set the page to wide mode
st.set_page_config(layout="wide")

mini_template = "You are an expert researcher. You\'ve talked to hundreds of {target_audience}. \
Each person in the niche of {target_audience} has certain struggles that make it easier to sell {target_course}. \
These are called Pain Points. There's a recipe for getting to the core of the Pain Points of {target_audience}. \
Namely, answer each of these Questions 3 times, each getting deeper in the issues of {target_audience}, \
appealing to their Emotions and uncertainties related to {target_course}. \
The Questions (answer each QUESTION 3 tiems in listicle format according to the instructions):\
1. What keeps them awake at night?\
2. What are they afraid of?\
3. What are they angry about?\
"


st.title("Marketing Technology")

prompt = PromptTemplate(
    input_variables = ["target_audience", "target_course"],
    template=mini_template,
)

chain = LLMChain(llm=OpenAI(), prompt=prompt)

#target_audience = "professionals looking for course on Power BI"
#my_course = "Zero to Hero in PowerBI"

# Use the sidebar for input
target_audience = st.sidebar.text_input('Enter your target audience', value = 'professionals looking for course on Power BI')
target_course = st.sidebar.text_input('Enter your course name', value = 'Zero to Hero in PowerBI')

if st.sidebar.button("Get response"):
    if target_audience and target_course:
        with st.spinner("Generating response..."):
            with st.expander("Show prompt", expanded=False):
                st.info(prompt.template)
            answer = chain.run({"target_audience": target_audience, "target_course": target_course})

        # Split the 'answer' into sections based on the questions
        sections = [section.strip() for section in answer.split("\n\n") if section.strip() != ""]

        # Assuming there are exactly three sections based on your output structure
        if len(sections) == 3:
            # Extract titles for tabs
            titles = [section.split('\n')[0] for section in sections]
            
            # Extract content for each section, removing the title
            contents = [section.split('\n')[1:] for section in sections]

            # Create tabs for each category
            tabs = st.tabs(titles)

            for i, tab in enumerate(tabs):
                with tab:
                    st.header(titles[i])
                    for content in contents[i]:
                        st.markdown(content)
        else:
            st.error("The answer format does not match the expected structure.")
    elif target_audience:
        st.error("Enter your course/service name")
    elif my_course:
        st.error("Enter your target audience")
    else:
        st.error("No input detected, Please provide the desired information.")