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from langchain.chains import LLMChain | |
from langchain_community.llms import OpenAI | |
from langchain_core.prompts import PromptTemplate | |
import streamlit as st | |
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 {My 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 {My 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("Saas Application") | |
prompt = PromptTemplate( | |
input_variables = ["Target Audience", "My 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" | |
target_audience = st.text_input("Enter your target audience") | |
my_course = st.text_input("Enter your course name") | |
if st.button("Get resposne"): | |
if target_audience and my_course: | |
with st.spinner("Generating response..."): | |
with st.expander("Show prompt", expanded=False): | |
st.info(prompt.template) | |
answer = chain.run({"Target Audience": target_audience, "My Course":my_course}) | |
st.write(answer) | |
st.success("Hope you like the resposne.❤") | |
elif target_audience: | |
st.error("Enter your course name.") | |
elif my_course: | |
st.error("Enter your target audience.") | |
else: | |
st.error("No input detected, Please provide the desired information.") | |