Spaces:
Sleeping
Sleeping
File size: 2,892 Bytes
f8fecf8 |
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 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
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 {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 times 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"
# Use the sidebar for input
target_audience = st.sidebar.text_input("Enter your target audience")
my_course = st.sidebar.text_input("Enter your course name")
if st.sidebar.button("Get response"):
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})
# 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.")
st.success("Hope you like the response.❤")
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.")
|